Sensitivity of LWR fuel cycle costs to uncertainties in detailed thermal cross sections

International Nuclear Information System (INIS)

Ryskamp, J.M.; Becker, M.; Harris, D.R.

1979-01-01

Cross sections averaged over the thermal energy (< 1 or 2 eV) group have been shown to have an important economic role for light-water reactors. Cost implications of thermal cross section uncertainties at the few-group level were reported earlier. When it has been determined that costs are sensitive to a specific thermal-group cross section, it becomes desirable to determine how specific energy-dependent cross sections influence fuel cycle costs. Multigroup cross-section sensitivity coefficients vary with fuel exposure. By changing the shape of a cross section displayed on a view-tube through an interactive graphics system, one can compute the change in few-group cross section using the exposure dependent sensitivity coefficients. With the changed exposure dependent few-group cross section, a new fuel cycle cost is computed by a sequence of batch depletion, core analysis, and fuel batch cost code modules. Fuel cycle costs are generally most sensitive to cross section uncertainties near the peak of the hardened Maxwellian flux

Multi-scenario modelling of uncertainty in stochastic chemical systems

International Nuclear Information System (INIS)

Evans, R. David; Ricardez-Sandoval, Luis A.

2014-01-01

Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo

Low cost high performance uncertainty quantification

KAUST Repository

Bekas, C.

2009-01-01

Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications. Copyright © 2009 ACM.

Cost benchmarking of railway projects in Europe – dealing with uncertainties in cost estimates

DEFF Research Database (Denmark)

Trabo, Inara

Past experiences in the construction of high-speed railway projects demontrate either positive or negative financial outcomes of the actual project’s budget. Usually some uncertainty value is included into initial budget calculations. Uncertainty is related to the increase of material prices...... per main cost drivers were compared and analyzed. There were observed nine railway projects, comparable to the Copenhagen-Ringsted project. The results of this comparison provided a certain overview on the cost range in different budget disciplines. The Copenhagen-Ringsted project is positioned right...

Cost Implications of Uncertainty in CO{sub 2} Storage Resource Estimates: A Review

Energy Technology Data Exchange (ETDEWEB)

Anderson, Steven T., E-mail: sanderson@usgs.gov [National Center, U.S. Geological Survey (United States)

2017-04-15

Carbon capture from stationary sources and geologic storage of carbon dioxide (CO{sub 2}) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO{sub 2} storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference, and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO{sub 2}, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO{sub 2} storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO{sub 2} injection will be mitigated by reservoir pressure management, estimates of the costs of CO{sub 2} storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO{sub 2} storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO{sub 2} storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the

Analysis on Calibration and Uncertainty for TD-LTE Radio Test System

Directory of Open Access Journals (Sweden)

Zhang Weipeng

2014-06-01

Full Text Available TD-LTE base station radio test system measures radio signal with a required accuracy, so calibration need to be done for transmission path between base station and measurement instruments before test. Considering Transmitter OFF Power measurement within OFF period, modulated signal generator and spectrum analyzer inside test system is used for calibration, to get accurate transmission parameters of the paths, and to reduce test cost without more instruments. The paper describes the uncertainty of test system, analyzes uncertainty contribution of interface mismatch, calculates uncertainty for Transmitter OFF Power measurement, uncertainty is 1.193 dB, within the requirement of 3GPP specification.

Using performance indicators to reduce cost uncertainty of China's CO2 mitigation goals

International Nuclear Information System (INIS)

Xu, Yuan

2013-01-01

Goals on absolute emissions and intensity play key roles in CO 2 mitigation. However, like cap-and-trade policies with price uncertainty, they suffer from significant uncertainty in abatement costs. This article examines whether an indicator could be established to complement CO 2 mitigation goals and help reduce cost uncertainty with a particular focus on China. Performance indicators on CO 2 emissions per unit of energy consumption could satisfy three criteria: compared with the mitigation goals, (i) they are more closely associated with active mitigation efforts and (ii) their baselines have more stable projections from historical trajectories. (iii) Their abatement costs are generally higher than other mitigation methods, particularly energy efficiency and conservation. Performance indicators could be used in the following way: if a CO 2 goal on absolute emissions or intensity is attained, the performance indicator should still reach a lower threshold as a cost floor. If the goal cannot be attained, an upper performance threshold should be achieved as a cost ceiling. The narrower cost uncertainty may encourage wider and greater mitigation efforts. - Highlights: ► CO 2 emissions per unit of energy consumption could act as performance indicators. ► Performance indicators are more closely related to active mitigation activities. ► Performance indicators have more stable historical trajectories. ► Abatement costs are higher for performance indicators than for other activities. ► Performance thresholds could reduce the cost uncertainty of CO 2 mitigation goals.

How to deal with climate change uncertainty in the planning of engineering systems

Science.gov (United States)

Spackova, Olga; Dittes, Beatrice; Straub, Daniel

2016-04-01

The effect of extreme events such as floods on the infrastructure and built environment is associated with significant uncertainties: These include the uncertain effect of climate change, uncertainty on extreme event frequency estimation due to limited historic data and imperfect models, and, not least, uncertainty on future socio-economic developments, which determine the damage potential. One option for dealing with these uncertainties is the use of adaptable (flexible) infrastructure that can easily be adjusted in the future without excessive costs. The challenge is in quantifying the value of adaptability and in finding the optimal sequence of decision. Is it worth to build a (potentially more expensive) adaptable system that can be adjusted in the future depending on the future conditions? Or is it more cost-effective to make a conservative design without counting with the possible future changes to the system? What is the optimal timing of the decision to build/adjust the system? We develop a quantitative decision-support framework for evaluation of alternative infrastructure designs under uncertainties, which: • probabilistically models the uncertain future (trough a Bayesian approach) • includes the adaptability of the systems (the costs of future changes) • takes into account the fact that future decisions will be made under uncertainty as well (using pre-posterior decision analysis) • allows to identify the optimal capacity and optimal timing to build/adjust the infrastructure. Application of the decision framework will be demonstrated on an example of flood mitigation planning in Bavaria.

Limitations of acceptability curves for presenting uncertainty in cost-effectiveness analysis

NARCIS (Netherlands)

Groot Koerkamp, Bas; Hunink, M. G. Myriam; Stijnen, Theo; Hammitt, James K.; Kuntz, Karen M.; Weinstein, Milton C.

2007-01-01

Clinical journals increasingly illustrate uncertainty about the cost and effect of health care interventions using cost-effectiveness acceptability curves (CEACs). CEACs present the probability that each competing alternative is optimal for a range of values of the cost-effectiveness threshold. The

Identification of optimal strategies for energy management systems planning under multiple uncertainties

International Nuclear Information System (INIS)

Cai, Y.P.; Huang, G.H.; Yang, Z.F.; Tan, Q.

2009-01-01

Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the planning of energy management systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority-inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management planning for a region with three cities. Useful solutions for the planning of energy management systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower

Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period

OpenAIRE

Mcmahon, James E.

2000-01-01

The paper introduces an innovative methology for evaluating the relative significance of energy-efficient technologies applied to fluorescent lamp ballasts. The method involves replacing the point estimates of life cycle cost of the ballasts with uncertainty distributions reflecting the whole spectrum of possible costs, and the assessed probability associated with each value. The results of uncertainty and sensitivity analyses will help analysts reduce effort in data collection and carry on a...

Quantifying the uncertainty of wave energy conversion device cost for policy appraisal: An Irish case study

International Nuclear Information System (INIS)

Farrell, Niall; Donoghue, Cathal O’; Morrissey, Karyn

2015-01-01

Wave Energy Conversion (WEC) devices are at a pre-commercial stage of development with feasibility studies sensitive to uncertainties surrounding assumed input costs. This may affect decision making. This paper analyses the impact these uncertainties may have on investor, developer and policymaker decisions using an Irish case study. Calibrated to data present in the literature, a probabilistic methodology is shown to be an effective means to carry this out. Value at Risk (VaR) and Conditional Value at Risk (CVaR) metrics are used to quantify the certainty of achieving a given cost or return on investment. We analyse the certainty of financial return provided by the proposed Irish Feed-in Tariff (FiT) policy. The influence of cost reduction through bulk discount is also discussed, with cost reduction targets for developers identified. Uncertainty is found to have a greater impact on the profitability of smaller installations and those subject to lower rates of cost reduction. This paper emphasises that a premium is required to account for cost uncertainty when setting FiT rates. By quantifying uncertainty, a means to specify an efficient premium is presented. - Highlights: • Probabilistic model quantifies uncertainty for wave energy feasibility analyses. • Methodology presented and applied to an Irish case study. • A feed-in tariff premium of 3–4 c/kWh required to account for cost uncertainty. • Sensitivity of uncertainty and cost to rates of technological change analysed. • Use of probabilistic model for investors and developers also demonstrated

Cost Recommendation under Uncertainty in IQWiG's Efficiency Frontier Framework.

Science.gov (United States)

Corro Ramos, Isaac; Lhachimi, Stefan K; Gerber-Grote, Andreas; Al, Maiwenn J

2017-02-01

The National Institute for Quality and Efficiency in Health Care (IQWiG) employs an efficiency frontier (EF) framework to facilitate setting maximum reimbursable prices for new interventions. Probabilistic sensitivity analysis (PSA) is used when yes/no reimbursement decisions are sought based on a fixed threshold. In the IQWiG framework, an additional layer of complexity arises as the EF itself may vary its shape in each PSA iteration, and thus the willingness-to-pay, indicated by the EF segments, may vary. To explore the practical problems arising when, within the EF approach, maximum reimbursable prices for new interventions are sought through PSA. When the EF is varied in a PSA, cost recommendations for new interventions may be determined by the mean or the median of the distances between each intervention's point estimate and each EF. Implications of using these metrics were explored in a simulation study based on the model used by IQWiG to assess the cost-effectiveness of 4 antidepressants. Depending on the metric used, cost recommendations can be contradictory. Recommendations based on the mean can also be inconsistent. Results (median) suggested that costs of duloxetine, venlafaxine, mirtazapine, and bupropion should be decreased by €131, €29, €12, and €99, respectively. These recommendations were implemented and the analysis repeated. New results suggested keeping the costs as they were. The percentage of acceptable PSA outcomes increased 41% on average, and the uncertainty associated to the net health benefit was significantly reduced. The median of the distances between every intervention outcome and every EF is a good proxy for the cost recommendation that would be given should the EF be fixed. Adjusting costs according to the median increased the probability of acceptance and reduced the uncertainty around the net health benefit distribution, resulting in a reduced uncertainty for decision makers.

Resilient guaranteed cost control of a power system.

Science.gov (United States)

Soliman, Hisham M; Soliman, Mostafa H; Hassan, Mohammad F

2014-05-01

With the development of power system interconnection, the low-frequency oscillation is becoming more and more prominent which may cause system separation and loss of energy to consumers. This paper presents an innovative robust control for power systems in which the operating conditions are changing continuously due to load changes. However, practical implementation of robust control can be fragile due to controller inaccuracies (tolerance of resistors used with operational amplifiers). A new design of resilient (non-fragile) robust control is given that takes into consideration both model and controller uncertainties by an iterative solution of a set of linear matrix inequalities (LMI). Both uncertainties are cast into a norm-bounded structure. A sufficient condition is derived to achieve the desired settling time for damping power system oscillations in face of plant and controller uncertainties. Furthermore, an improved controller design, resilient guaranteed cost controller, is derived to achieve oscillations damping in a guaranteed cost manner. The effectiveness of the algorithm is shown for a single machine infinite bus system, and then, it is extended to multi-area power system.

Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period

Energy Technology Data Exchange (ETDEWEB)

McMahon, James E.; Liu, Xiaomin; Turiel, Ike; Hakim, Sajid; Fisher, Diane

2000-06-01

The paper introduces an innovative methodology for evaluating the relative significance of energy-efficient technologies applied to fluorescent lamp ballasts. The method involves replacing the point estimates of life cycle cost of the ballasts with uncertainty distributions reflecting the whole spectrum of possible costs, and the assessed probability associated with each value. The results of uncertainty and sensitivity analyses will help analysts reduce effort in data collection and carry on analysis more efficiently. These methods also enable policy makers to gain an insightful understanding of which efficient technology alternatives benefit or cost what fraction of consumers, given the explicit assumptions of the analysis.

Revised cost savings estimate with uncertainty for enhanced sludge washing of underground storage tank waste

International Nuclear Information System (INIS)

DeMuth, S.

1998-01-01

Enhanced Sludge Washing (ESW) has been selected to reduce the amount of sludge-based underground storage tank (UST) high-level waste at the Hanford site. During the past several years, studies have been conducted to determine the cost savings derived from the implementation of ESW. The tank waste inventory and ESW performance continues to be revised as characterization and development efforts advance. This study provides a new cost savings estimate based upon the most recent inventory and ESW performance revisions, and includes an estimate of the associated cost uncertainty. Whereas the author's previous cost savings estimates for ESW were compared against no sludge washing, this study assumes the baseline to be simple water washing which more accurately reflects the retrieval activity along. The revised ESW cost savings estimate for all UST waste at Hanford is $6.1 B ± $1.3 B within 95% confidence. This is based upon capital and operating cost savings, but does not include development costs. The development costs are assumed negligible since they should be at least an order of magnitude less than the savings. The overall cost savings uncertainty was derived from process performance uncertainties and baseline remediation cost uncertainties, as determined by the author's engineering judgment

Forecasting the Number of Soil Samples Required to Reduce Remediation Cost Uncertainty

OpenAIRE

Demougeot-Renard, Hélène; de Fouquet, Chantal; Renard, Philippe

2008-01-01

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of vol...

Use of probabilistic methods for analysis of cost and duration uncertainties in a decision analysis framework

International Nuclear Information System (INIS)

Boak, D.M.; Painton, L.

1995-01-01

Probabilistic forecasting techniques have been used in many risk assessment and performance assessment applications on radioactive waste disposal projects such as Yucca Mountain and the Waste Isolation Pilot Plant (WIPP). Probabilistic techniques such as Monte Carlo and Latin Hypercube sampling methods are routinely used to treat uncertainties in physical parameters important in simulating radionuclide transport in a coupled geohydrologic system and assessing the ability of that system to comply with regulatory release limits. However, the use of probabilistic techniques in the treatment of uncertainties in the cost and duration of programmatic alternatives on risk and performance assessment projects is less common. Where significant uncertainties exist and where programmatic decisions must be made despite existing uncertainties, probabilistic techniques may yield important insights into decision options, especially when used in a decision analysis framework and when properly balanced with deterministic analyses. For relatively simple evaluations, these types of probabilistic evaluations can be made using personal computer-based software

Uncertainty analysis in raw material and utility cost of biorefinery synthesis and design

DEFF Research Database (Denmark)

Cheali, Peam; Quaglia, Alberto; Gernaey, Krist

2014-01-01

are characterized by considerable uncertainty. These uncertainties might have significant impact on the results of the design problem, and therefore need to be carefully evaluated and managed, in order to generate candidates for robust design. In this contribution, we study the effect of data uncertainty (raw...... material price and utility cost) on the design of a biorefinery process network....

Using cost-benefit concepts in design floods improves communication of uncertainty

Science.gov (United States)

Ganora, Daniele; Botto, Anna; Laio, Francesco; Claps, Pierluigi

2017-04-01

Flood frequency analysis, i.e. the study of the relationships between the magnitude and the rarity of high flows in a river, is the usual procedure adopted to assess flood hazard, preliminary to the plan/design of flood protection measures. It grounds on the fit of a probability distribution to the peak discharge values recorded in gauging stations and the final estimates over a region are thus affected by uncertainty, due to the limited sample availability and of the possible alternatives in terms of the probabilistic model and the parameter estimation methods used. In the last decade, the scientific community dealt with this issue by developing a number of methods to quantify such uncertainty components. Usually, uncertainty is visually represented through confidence bands, which are easy to understand, but are not yet demonstrated to be useful for design purposes: they usually disorient decision makers, as the design flood is no longer univocally defined, making the decision process undetermined. These considerations motivated the development of the uncertainty-compliant design flood estimator (UNCODE) procedure (Botto et al., 2014) that allows one to select meaningful flood design values accounting for the associated uncertainty by considering additional constraints based on cost-benefit criteria. This method suggests an explicit multiplication factor that corrects the traditional (without uncertainty) design flood estimates to incorporate the effects of uncertainty in the estimate at the same safety level. Even though the UNCODE method was developed for design purposes, it can represent a powerful and robust tool to help clarifying the effects of the uncertainty in statistical estimation. As the process produces increased design flood estimates, this outcome demonstrates how uncertainty leads to more expensive flood protection measures, or insufficiency of current defenses. Moreover, the UNCODE approach can be used to assess the "value" of data, as the costs

Least-cost failure diagnosis in uncertain reliability systems

International Nuclear Information System (INIS)

Cox, Louis Anthony; Chiu, Steve Y.; Sun Xiaorong

1996-01-01

In many textbook solutions, for systems failure diagnosis problems studied using reliability theory and artificial intelligence, the prior probabilities of different failure states can be estimated and used to guide the sequential search for failed components after the whole system fails. In practice, however, both the component failure probabilities and the structure function of the system being examined--i.e., the mapping between the states of its components and the state of the system--may not be known with certainty. At best:, the probabilities of different hypothesized system descriptions, each specifying the component failure probabilities and the system's structure function, may be known to a useful approximation, perhaps based on sample data and previous experience. Cost-effective diagnosis of the system's failure state is then a challenging problem. Although the probabilities of component failures are aleatory, uncertainties about these probabilities and about the system structure function are epistemic. This paper examines how to make best use of both epistemic prior probabilities for system descriptions and the information gleaned from costly inspections of component states after the system fails, to minimize the average cost of identifying the failure state. Two approaches are introduced for systems dominated by aleatory uncertainties, one motivated by information theory and the other based on the idea of trying to prove a hypothesis about the identity of the failure state as efficiently as possible. While the general problem of cost-effective failure diagnosis is computationally intractable (NP-hard), both heuristics provide useful approximations on small to moderate sized problems and optimal results for certain common types of reliability systems, including series, parallel, parallel-series, and k-out-of-n systems. A hybrid heuristic that adaptively chooses which heuristic to apply next after any sequence of observations (component test results

Optimum sizing of wind-battery systems incorporating resource uncertainty

International Nuclear Information System (INIS)

Roy, Anindita; Kedare, Shireesh B.; Bandyopadhyay, Santanu

2010-01-01

The inherent uncertainty of the wind is a major impediment for successful implementation of wind based power generation technology. A methodology has been proposed in this paper to incorporate wind speed uncertainty in sizing wind-battery system for isolated applications. The uncertainty associated with the wind speed is incorporated using chance constraint programming approach. For a pre-specified reliability requirement, a deterministic equivalent energy balance equation may be derived from the chance constraint that allows time series simulation of the entire system. This results in a generation of the entire set of feasible design options, satisfying different system level constraints, on a battery capacity vs. generator rating diagram, also known as the design space. The proposed methodology highlights the trade-offs between the wind turbine rating, rotor diameter and the battery size for a given reliability of power supply. The optimum configuration is chosen on the basis of the minimum cost of energy (US$/kWh). It is shown with the help of illustrative examples that the proposed methodology is generic and flexible to incorporate alternate sub-component models. (author)

Automatic Voltage Control (AVC) System under Uncertainty from Wind Power

DEFF Research Database (Denmark)

Qin, Nan; Abildgaard, Hans; Flynn, Damian

2016-01-01

An automatic voltage control (AVC) system maintains the voltage profile of a power system in an acceptable range and minimizes the operational cost by coordinating the regulation of controllable components. Typically, all of the parameters in the optimization problem are assumed to be certain...... and constant in the decision making process. However, for high shares of wind power, uncertainty in the decision process due to wind power variability may result in an infeasible AVC solution. This paper proposes a voltage control approach which considers the voltage uncertainty from wind power productions....... The proposed method improves the performance and the robustness of a scenario based approach by estimating the potential voltage variations due to fluctuating wind power production, and introduces a voltage margin to protect the decision against uncertainty for each scenario. The effectiveness of the proposed...

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs

International Nuclear Information System (INIS)

Yin, Hao; Pizzol, Massimo; Xu, Linyu

2017-01-01

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO 2 and NO x . Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C 0  = 35 μg/m 3 ) to 0.9% (VSL, WHO guideline: C 0  = 10 μg/m 3 ) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C 0 ). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments. - Highlights: �

Economic performance optimization of an absorption cooling system under uncertainty

International Nuclear Information System (INIS)

Gebreslassie, Berhane H.; Guillen-Gosalbez, Gonzalo; Jimenez, Laureano; Boer, Dieter

2009-01-01

Many of the strategies devised so far to address the optimization of energy systems are deterministic approaches that rely on estimated data. However, in real world applications there are many sources of uncertainty that introduce variability into the decision-making problem. Within this general context, we propose a novel approach to address the design of absorption cooling systems under uncertainty in the energy cost. As opposed to other approaches that optimize the expected performance of the system as a single objective, in our method the design task is formulated as a stochastic bi-criteria non-linear optimization problem that simultaneously accounts for the minimization of the expected total cost and the financial risk associated with the investment. The latter criterion is measured by the downside risk, which avoids the need to define binary variables thus improving the computational performance of the model. The capabilities of the proposed modeling framework and solution strategy are illustrated in a case study problem that addresses the design of a typical absorption cooling system. Numerical results demonstrate that the method presented allows to manage the risk level effectively by varying the area of the heat exchangers of the absorption cycle. Specifically, our strategy allows identifying the optimal values of the operating and design variables of the cycle that make it less sensitive to fluctuations in the energy price, thus improving its robustness in the face of uncertainty.

Uncertainties in Early Stage Capital Cost Estimation of Process Design – A case study on biorefinery design

Directory of Open Access Journals (Sweden)

Gurkan eSin

2015-02-01

Full Text Available Capital investment, next to the product demand, sales and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early stage design is a challenging task. This is especially important in biorefinery research, where available information and experiences with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs (a Bootstrapping as a regression method when cost data is available and (b the Monte Carlo technique as an error propagation method based on expert input when cost data is not available. Four well-known models for early stage cost estimation are reviewed an analyzed using the methodology. The significance of uncertainties of cost data for early stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision making under uncertainties. One of the results using an order-of-magnitude estimate shows that the production of diethyl ether and 1,3-butadiene are the most promising with economic risks of 0.24 MM$/a and 4.6 MM$/a due to uncertainties in cost estimations, respectively.

Communicating uncertainty in cost-benefit analysis : A cognitive psychological perspective

NARCIS (Netherlands)

Mouter, N.; Holleman, M.; Calvert, S.C.; Annema, J.A.

2013-01-01

Based on a cognitive psychological theory, this paper aims to improve the communication of uncertainty in Cost-Benefit Analysis. The theory is based on different cognitive-personality and cognitive-social psychological constructs that may help explain individual differences in the processing of

A smart Monte Carlo procedure for production costing and uncertainty analysis

International Nuclear Information System (INIS)

Parker, C.; Stremel, J.

1996-01-01

Electric utilities using chronological production costing models to decide whether to buy or sell power over the next week or next few weeks need to determine potential profits or losses under a number of uncertainties. A large amount of money can be at stake--often $100,000 a day or more--and one party of the sale must always take on the risk. In the case of fixed price ($/MWh) contracts, the seller accepts the risk. In the case of cost plus contracts, the buyer must accept the risk. So, modeling uncertainty and understanding the risk accurately can improve the competitive edge of the user. This paper investigates an efficient procedure for representing risks and costs from capacity outages. Typically, production costing models use an algorithm based on some form of random number generator to select resources as available or on outage. These algorithms allow experiments to be repeated and gains and losses to be observed in a short time. The authors perform several experiments to examine the capability of three unit outage selection methods and measures their results. Specifically, a brute force Monte Carlo procedure, a Monte Carlo procedure with Latin Hypercube sampling, and a Smart Monte Carlo procedure with cost stratification and directed sampling are examined

Stochastic Systems Uncertainty Quantification and Propagation

CERN Document Server

Grigoriu, Mircea

2012-01-01

Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: ·         A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis   ·          Probabilistic models for random variables an...

Strategic design of cost savings guarantee in energy performance contracting under uncertainty

International Nuclear Information System (INIS)

Deng, Qianli; Jiang, Xianglin; Cui, Qingbin; Zhang, Limao

2015-01-01

Highlights: • A methodology is proposed to assist Energy Service Companies to maintain competitiveness in winning bids. • Uncertainties within the energy cost savings are modeled stochastically using the Monte-Carlo simulation. • A strategic energy savings guarantee design curve is derived, where all points return as appropriate guarantees. • A campus case is presented to demonstrate the applicability for finding appropriate guaranteed savings value. - Abstract: Among the key barriers to profit in Energy Performance Contracting (EPC) are uncertainties about attaining the realized energy cost savings and potential disputes over the guaranteed cost savings. In this paper, a methodology has been proposed to assist the Energy Service Company (ESCO): (1) to evaluate the risk threshold if the guarantee has already been made, and (2) to determine the guarantee design, if the guarantee has not been made yet, that not only promises the ESCO’s profitability from EPC but also maintains its competitiveness to win the bid. Uncertainties within the energy cost savings are modeled stochastically using Monte-Carlo simulation, taking both the energy price fluctuation and the facility performance variability into account. Based on that, a strategic energy savings guarantee design curve is derived, that all the points on it would return as appropriate guarantees. Finally, a campus case is presented to demonstrate the applicability for finding the appropriate guaranteed savings value. This method is also worth popularizing in similar performance-based projects

Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs)

International Nuclear Information System (INIS)

Sadeghi, Mehdi; Mirshojaeian Hosseini, Hossein

2006-01-01

For many years, energy models have been used in developed or developing countries to satisfy different needs in energy planning. One of major problems against energy planning and consequently energy models is uncertainty, spread in different economic, political and legal dimensions of energy planning. Confronting uncertainty, energy planners have often used two well-known strategies. The first strategy is stochastic programming, in which energy system planners define different scenarios and apply an explicit probability of occurrence to each scenario. The second strategy is Minimax Regret strategy that minimizes regrets of different decisions made in energy planning. Although these strategies have been used extensively, they could not flexibly and effectively deal with the uncertainties caused by fuzziness. 'Fuzzy Linear Programming (FLP)' is a strategy that can take fuzziness into account. This paper tries to demonstrate the method of application of FLP for optimization of supply energy system in Iran, as a case study. The used FLP model comprises fuzzy coefficients for investment costs. Following the mentioned purpose, it is realized that FLP is an easy and flexible approach that can be a serious competitor for other confronting uncertainties approaches, i.e. stochastic and Minimax Regret strategies. (author)

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

Science.gov (United States)

Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

Integrating uncertainties for climate change mitigation

Science.gov (United States)

Rogelj, Joeri; McCollum, David; Reisinger, Andy; Meinshausen, Malte; Riahi, Keywan

2013-04-01

The target of keeping global average temperature increase to below 2°C has emerged in the international climate debate more than a decade ago. In response, the scientific community has tried to estimate the costs of reaching such a target through modelling and scenario analysis. Producing such estimates remains a challenge, particularly because of relatively well-known, but ill-quantified uncertainties, and owing to limited integration of scientific knowledge across disciplines. The integrated assessment community, on one side, has extensively assessed the influence of technological and socio-economic uncertainties on low-carbon scenarios and associated costs. The climate modelling community, on the other side, has worked on achieving an increasingly better understanding of the geophysical response of the Earth system to emissions of greenhouse gases (GHG). This geophysical response remains a key uncertainty for the cost of mitigation scenarios but has only been integrated with assessments of other uncertainties in a rudimentary manner, i.e., for equilibrium conditions. To bridge this gap between the two research communities, we generate distributions of the costs associated with limiting transient global temperature increase to below specific temperature limits, taking into account uncertainties in multiple dimensions: geophysical, technological, social and political. In other words, uncertainties resulting from our incomplete knowledge about how the climate system precisely reacts to GHG emissions (geophysical uncertainties), about how society will develop (social uncertainties and choices), which technologies will be available (technological uncertainty and choices), when we choose to start acting globally on climate change (political choices), and how much money we are or are not willing to spend to achieve climate change mitigation. We find that political choices that delay mitigation have the largest effect on the cost-risk distribution, followed by

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs.

Science.gov (United States)

Yin, Hao; Pizzol, Massimo; Xu, Linyu

2017-07-01

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO 2 and NO x . Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C 0  = 35 μg/m 3 ) to 0.9% (VSL, WHO guideline: C 0  = 10 μg/m 3 ) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C 0 ). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments. Copyright © 2017

Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

Science.gov (United States)

McCaffrey, Nikki; Agar, Meera; Harlum, Janeane; Karnon, Jonathon; Currow, David; Eckermann, Simon

2015-01-01

Comparing multiple, diverse outcomes with cost-effectiveness analysis (CEA) is important, yet challenging in areas like palliative care where domains are unamenable to integration with survival. Generic multi-attribute utility values exclude important domains and non-health outcomes, while partial analyses-where outcomes are considered separately, with their joint relationship under uncertainty ignored-lead to incorrect inference regarding preferred strategies. The objective of this paper is to consider whether such decision making can be better informed with alternative presentation and summary measures, extending methods previously shown to have advantages in multiple strategy comparison. Multiple outcomes CEA of a home-based palliative care model (PEACH) relative to usual care is undertaken in cost disutility (CDU) space and compared with analysis on the cost-effectiveness plane. Summary measures developed for comparing strategies across potential threshold values for multiple outcomes include: expected net loss (ENL) planes quantifying differences in expected net benefit; the ENL contour identifying preferred strategies minimising ENL and their expected value of perfect information; and cost-effectiveness acceptability planes showing probability of strategies minimising ENL. Conventional analysis suggests PEACH is cost-effective when the threshold value per additional day at home (1) exceeds $1,068 or dominated by usual care when only the proportion of home deaths is considered. In contrast, neither alternative dominate in CDU space where cost and outcomes are jointly considered, with the optimal strategy depending on threshold values. For example, PEACH minimises ENL when 1=$2,000 and 2=$2,000 (threshold value for dying at home), with a 51.6% chance of PEACH being cost-effective. Comparison in CDU space and associated summary measures have distinct advantages to multiple domain comparisons, aiding transparent and robust joint comparison of costs and multiple

Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples

NARCIS (Netherlands)

Ettema, D.F.; Timmermans, H.J.P.

2006-01-01

A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves

Estimating the uncertainty of damage costs of pollution: A simple transparent method and typical results

International Nuclear Information System (INIS)

Spadaro, Joseph V.; Rabl, Ari

2008-01-01

Whereas the uncertainty of environmental impacts and damage costs is usually estimated by means of a Monte Carlo calculation, this paper shows that most (and in many cases all) of the uncertainty calculation involves products and/or sums of products and can be accomplished with an analytic solution which is simple and transparent. We present our own assessment of the component uncertainties and calculate the total uncertainty for the impacts and damage costs of the classical air pollutants; results for a Monte Carlo calculation for the dispersion part are also shown. The distribution of the damage costs is approximately lognormal and can be characterized in terms of geometric mean μ g and geometric standard deviation σ g , implying that the confidence interval is multiplicative. We find that for the classical air pollutants σ g is approximately 3 and the 68% confidence interval is [μ g / σ g , μ g σ g ]. Because the lognormal distribution is highly skewed for large σ g , the median is significantly smaller than the mean. We also consider the case where several lognormally distributed damage costs are added, for example to obtain the total damage cost due to all the air pollutants emitted by a power plant, and we find that the relative error of the sum can be significantly smaller than the relative errors of the summands. Even though the distribution for such sums is not exactly lognormal, we present a simple lognormal approximation that is quite adequate for most applications

Electric-power systems planning and greenhouse-gas emission management under uncertainty

International Nuclear Information System (INIS)

Li, Y.P.; Huang, G.H.

2012-01-01

Highlight: ►A multistage stochastic integer programming model is developed for planning electric-power systems. ►Uncertain and dynamic information can be incorporated within a multilayer scenario tree. ►This can help minimize system cost under random energy demand and greenhouse gas (GHG) abatement goal. ►Results can support decisions of facility expansion, electricity supply and GHG mitigation. - Abstract: In this study, a multistage interval-stochastic integer programming model is formulated for managing greenhouse gas (GHG) emissions and planning electric-power systems under uncertainty. The developed model can reflect dynamic, interactive, and uncertain characteristics of energy systems. Besides, the model can be used for answering questions related to types, times, demands and mitigations of energy systems planning practices, with the objective of minimizing system cost over a long-time planning horizon. The solutions can help generate electricity-generation schemes and capacity-expansion plans under different GHG-mitigation options and electricity-demand levels. Tradeoffs among system cost, energy security, and emission management can also be tackled. A high system cost will increase renewable energy supply and reduce GHG emission, while a desire for a low cost will run into risks of a high energy deficiency and a high GHG emission.

Task Uncertainty Can Account for Mixing and Switch Costs in Task-Switching

Science.gov (United States)

Rennie, Jaime L.

2015-01-01

Cognitive control is required in situations that involve uncertainty or change, such as when resolving conflict, selecting responses and switching tasks. Recently, it has been suggested that cognitive control can be conceptualised as a mechanism which prioritises goal-relevant information to deal with uncertainty. This hypothesis has been supported using a paradigm that requires conflict resolution. In this study, we examine whether cognitive control during task switching is also consistent with this notion. We used information theory to quantify the level of uncertainty in different trial types during a cued task-switching paradigm. We test the hypothesis that differences in uncertainty between task repeat and task switch trials can account for typical behavioural effects in task-switching. Increasing uncertainty was associated with less efficient performance (i.e., slower and less accurate), particularly on switch trials and trials that afford little opportunity for advance preparation. Interestingly, both mixing and switch costs were associated with a common episodic control process. These results support the notion that cognitive control may be conceptualised as an information processor that serves to resolve uncertainty in the environment. PMID:26107646

Uncertainties in Early-Stage Capital Cost Estimation of Process Design – A Case Study on Biorefinery Design

International Nuclear Information System (INIS)

Cheali, Peam; Gernaey, Krist V.; Sin, Gürkan

2015-01-01

Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which is especially relevant in biorefinery research where information about new technologies and experience with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs: (a) bootstrapping as a regression method when cost data are available; and, (b) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is indeed found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision-making under uncertainties. One of the results using order-of-magnitude estimates shows that the production of diethyl ether and 1,3-butadiene are the most promising with the lowest economic risks (among the alternatives considered) of 0.24 MM$/a and 4.6 MM$/a, respectively.

Uncertainties in Early-Stage Capital Cost Estimation of Process Design – A Case Study on Biorefinery Design

Energy Technology Data Exchange (ETDEWEB)

Cheali, Peam; Gernaey, Krist V.; Sin, Gürkan, E-mail: gsi@kt.dtu.dk [Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby (Denmark)

2015-02-06

Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which is especially relevant in biorefinery research where information about new technologies and experience with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs: (a) bootstrapping as a regression method when cost data are available; and, (b) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is indeed found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision-making under uncertainties. One of the results using order-of-magnitude estimates shows that the production of diethyl ether and 1,3-butadiene are the most promising with the lowest economic risks (among the alternatives considered) of 0.24 MM$/a and 4.6 MM$/a, respectively.

Uncertainty in decision models analyzing cost-effectiveness : The joint distribution of incremental costs and effectiveness evaluated with a nonparametric bootstrap method

NARCIS (Netherlands)

Hunink, Maria; Bult, J.R.; De Vries, J; Weinstein, MC

1998-01-01

Purpose. To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. Methods. The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous

How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications

Science.gov (United States)

McMillan, Hilary; Seibert, Jan; Petersen-Overleir, Asgeir; Lang, Michel; White, Paul; Snelder, Ton; Rutherford, Kit; Krueger, Tobias; Mason, Robert; Kiang, Julie

2017-07-01

Streamflow data are used for important environmental and economic decisions, such as specifying and regulating minimum flows, managing water supplies, and planning for flood hazards. Despite significant uncertainty in most flow data, the flow series for these applications are often communicated and used without uncertainty information. In this commentary, we argue that proper analysis of uncertainty in river flow data can reduce costs and promote robust conclusions in water management applications. We substantiate our argument by providing case studies from Norway and New Zealand where streamflow uncertainty analysis has uncovered economic costs in the hydropower industry, improved public acceptance of a controversial water management policy, and tested the accuracy of water quality trends. We discuss the need for practical uncertainty assessment tools that generate multiple flow series realizations rather than simple error bounds. Although examples of such tools are in development, considerable barriers for uncertainty analysis and communication still exist for practitioners, and future research must aim to provide easier access and usability of uncertainty estimates. We conclude that flow uncertainty analysis is critical for good water management decisions.

Better Informing Decision Making with Multiple Outcomes Cost-Effectiveness Analysis under Uncertainty in Cost-Disutility Space

Science.gov (United States)

McCaffrey, Nikki; Agar, Meera; Harlum, Janeane; Karnon, Jonathon; Currow, David; Eckermann, Simon

2015-01-01

Introduction Comparing multiple, diverse outcomes with cost-effectiveness analysis (CEA) is important, yet challenging in areas like palliative care where domains are unamenable to integration with survival. Generic multi-attribute utility values exclude important domains and non-health outcomes, while partial analyses—where outcomes are considered separately, with their joint relationship under uncertainty ignored—lead to incorrect inference regarding preferred strategies. Objective The objective of this paper is to consider whether such decision making can be better informed with alternative presentation and summary measures, extending methods previously shown to have advantages in multiple strategy comparison. Methods Multiple outcomes CEA of a home-based palliative care model (PEACH) relative to usual care is undertaken in cost disutility (CDU) space and compared with analysis on the cost-effectiveness plane. Summary measures developed for comparing strategies across potential threshold values for multiple outcomes include: expected net loss (ENL) planes quantifying differences in expected net benefit; the ENL contour identifying preferred strategies minimising ENL and their expected value of perfect information; and cost-effectiveness acceptability planes showing probability of strategies minimising ENL. Results Conventional analysis suggests PEACH is cost-effective when the threshold value per additional day at home ( 1) exceeds $1,068 or dominated by usual care when only the proportion of home deaths is considered. In contrast, neither alternative dominate in CDU space where cost and outcomes are jointly considered, with the optimal strategy depending on threshold values. For example, PEACH minimises ENL when 1=$2,000 and 2=$2,000 (threshold value for dying at home), with a 51.6% chance of PEACH being cost-effective. Conclusion Comparison in CDU space and associated summary measures have distinct advantages to multiple domain comparisons, aiding

Task uncertainty can account for mixing and switch costs in task-switching.

Directory of Open Access Journals (Sweden)

Patrick S Cooper

Full Text Available Cognitive control is required in situations that involve uncertainty or change, such as when resolving conflict, selecting responses and switching tasks. Recently, it has been suggested that cognitive control can be conceptualised as a mechanism which prioritises goal-relevant information to deal with uncertainty. This hypothesis has been supported using a paradigm that requires conflict resolution. In this study, we examine whether cognitive control during task switching is also consistent with this notion. We used information theory to quantify the level of uncertainty in different trial types during a cued task-switching paradigm. We test the hypothesis that differences in uncertainty between task repeat and task switch trials can account for typical behavioural effects in task-switching. Increasing uncertainty was associated with less efficient performance (i.e., slower and less accurate, particularly on switch trials and trials that afford little opportunity for advance preparation. Interestingly, both mixing and switch costs were associated with a common episodic control process. These results support the notion that cognitive control may be conceptualised as an information processor that serves to resolve uncertainty in the environment.

Dynamic Uncertainty for Compensated Second-Order Systems

Directory of Open Access Journals (Sweden)

Clemens Elster

2010-08-01

Full Text Available The compensation of LTI systems and the evaluation of the according uncertainty is of growing interest in metrology. Uncertainty evaluation in metrology ought to follow specific guidelines, and recently two corresponding uncertainty evaluation schemes have been proposed for FIR and IIR filtering. We employ these schemes to compare an FIR and an IIR approach for compensating a second-order LTI system which has relevance in metrology. Our results suggest that the FIR approach is superior in the sense that it yields significantly smaller uncertainties when real-time evaluation of uncertainties is desired.

An interval-based possibilistic programming method for waste management with cost minimization and environmental-impact abatement under uncertainty.

Science.gov (United States)

Li, Y P; Huang, G H

2010-09-15

Considerable public concerns have been raised in the past decades since a large amount of pollutant emissions from municipal solid waste (MSW) disposal of processes pose risks on surrounding environment and human health. Moreover, in MSW management, various uncertainties exist in the related costs, impact factors and objectives, which can affect the optimization processes and the decision schemes generated. In this study, an interval-based possibilistic programming (IBPP) method is developed for planning the MSW management with minimized system cost and environmental impact under uncertainty. The developed method can deal with uncertainties expressed as interval values and fuzzy sets in the left- and right-hand sides of constraints and objective function. An interactive algorithm is provided for solving the IBPP problem, which does not lead to more complicated intermediate submodels and has a relatively low computational requirement. The developed model is applied to a case study of planning a MSW management system, where mixed integer linear programming (MILP) technique is introduced into the IBPP framework to facilitate dynamic analysis for decisions of timing, sizing and siting in terms of capacity expansion for waste-management facilities. Three cases based on different waste-management policies are examined. The results obtained indicate that inclusion of environmental impacts in the optimization model can change the traditional waste-allocation pattern merely based on the economic-oriented planning approach. The results obtained can help identify desired alternatives for managing MSW, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty. Copyright 2010 Elsevier B.V. All rights reserved.

Integrative evaluation for sustainable decisions of urban wastewater system management under uncertainty

Science.gov (United States)

Hadjimichael, A.; Corominas, L.; Comas, J.

2017-12-01

With sustainable development as their overarching goal, urban wastewater system (UWS) managers need to take into account multiple social, economic, technical and environmental facets related to their decisions. In this complex decision-making environment, uncertainty can be formidable. It is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behavior. This inherent uncertainty suggests that wiser decisions would be made under an adaptive and iterative decision-making regime. No decision-support framework has been presented in the literature to effectively addresses all these needs. The objective of this work is to describe such a conceptual framework to evaluate and compare alternative solutions for various UWS challenges within an adaptive management structure. Socio-economic aspects such as externalities are taken into account, along with other traditional criteria as necessary. Robustness, reliability and resilience analyses test the performance of the system against present and future variability. A valuation uncertainty analysis incorporates uncertain valuation assumptions in the decision-making process. The framework is demonstrated with an application to a case study presenting a typical problem often faced by managers: poor river water quality, increasing population, and more stringent water quality legislation. The application of the framework made use of: i) a cost-benefit analysis including monetized environmental benefits and damages; ii) a robustness analysis of system performance against future conditions; iii) reliability and resilience analyses of the system given contextual variability; and iv) a valuation uncertainty analysis of model parameters. The results suggest that the installation of bigger volumes would give rise to increased benefits despite larger capital costs, as well as increased robustness and resilience. Population numbers appear to affect the estimated benefits most, followed by

Uncertainty in the learning rates of energy technologies. An experiment in a global multi-regional energy system model

International Nuclear Information System (INIS)

Rout, Ullash K.; Blesl, Markus; Fahl, Ulrich; Remme, Uwe; Voss, Alfred

2009-01-01

The diffusion of promising energy technologies in the market depends on their future energy production-cost development. When analyzing these technologies in an integrated assessment model using endogenous technological learning, the uncertainty in the assumed learning rates (LRs) plays a crucial role in the production-cost development and model outcomes. This study examines the uncertainty in LRs of some energy technologies under endogenous global learning implementation and presents a floor-cost modeling procedure to systematically regulate the uncertainty in LRs of energy technologies. The article narrates the difficulties of data assimilation, as compatible with mixed integer programming segmentations, and comprehensively presents the causes of uncertainty in LRs. This work is executed using a multi-regional and long-horizon energy system model based on 'TIMES' framework. All regions receive an economic advantage to learn in a common domain, and resource-ample regions obtain a marginal advantage for better exploitation of the learning technologies, due to a lower supply-side fuel-cost development. The lowest learning investment associated with the maximum LR mobilizes more deployment of the learning technologies. The uncertainty in LRs has an impact on the diffusion of energy technologies tested, and therefore this study scrutinizes the role of policy support for some of the technologies investigated. (author)

The Impact of Shale Gas on the Cost and Feasibility of Meeting Climate Targets—A Global Energy System Model Analysis and an Exploration of Uncertainties

Directory of Open Access Journals (Sweden)

Sheridan Few

2017-01-01

Full Text Available There exists considerable uncertainty over both shale and conventional gas resource availability and extraction costs, as well as the fugitive methane emissions associated with shale gas extraction and its possible role in mitigating climate change. This study uses a multi-region energy system model, TIAM (TIMES integrated assessment model, to consider the impact of a range of conventional and shale gas cost and availability assessments on mitigation scenarios aimed at achieving a limit to global warming of below 2 °C in 2100, with a 50% likelihood. When adding shale gas to the global energy mix, the reduction to the global energy system cost is relatively small (up to 0.4%, and the mitigation cost increases by 1%–3% under all cost assumptions. The impact of a “dash for shale gas”, of unavailability of carbon capture and storage, of increased barriers to investment in low carbon technologies, and of higher than expected leakage rates, are also considered; and are each found to have the potential to increase the cost and reduce feasibility of meeting global temperature goals. We conclude that the extraction of shale gas is not likely to significantly reduce the effort required to mitigate climate change under globally coordinated action, but could increase required mitigation effort if not handled sufficiently carefully.

How Much Does it Cost to Expand a Protected Area System? Some Critical Determining Factors and Ranges of Costs for Queensland

Science.gov (United States)

Adams, Vanessa M.; Segan, Daniel B.; Pressey, Robert L.

2011-01-01

Many governments have recently gone on record promising large-scale expansions of protected areas to meet global commitments such as the Convention on Biological Diversity. As systems of protected areas are expanded to be more comprehensive, they are more likely to be implemented if planners have realistic budget estimates so that appropriate funding can be requested. Estimating financial budgets a priori must acknowledge the inherent uncertainties and assumptions associated with key parameters, so planners should recognize these uncertainties by estimating ranges of potential costs. We explore the challenge of budgeting a priori for protected area expansion in the face of uncertainty, specifically considering the future expansion of protected areas in Queensland, Australia. The government has committed to adding ∼12 million ha to the reserve system, bringing the total area protected to 20 million ha by 2020. We used Marxan to estimate the costs of potential reserve designs with data on actual land value, market value, transaction costs, and land tenure. With scenarios, we explored three sources of budget variability: size of biodiversity objectives; subdivision of properties; and legal acquisition routes varying with tenure. Depending on the assumptions made, our budget estimates ranged from $214 million to $2.9 billion. Estimates were most sensitive to assumptions made about legal acquisition routes for leasehold land. Unexpected costs (costs encountered by planners when real-world costs deviate from assumed costs) responded non-linearly to inability to subdivide and percentage purchase of private land. A financially conservative approach - one that safeguards against large cost increases while allowing for potential financial windfalls - would involve less optimistic assumptions about acquisition and subdivision to allow Marxan to avoid expensive properties where possible while meeting conservation objectives. We demonstrate how a rigorous analysis can inform

Probabilistic Mass Growth Uncertainties

Science.gov (United States)

Plumer, Eric; Elliott, Darren

2013-01-01

Mass has been widely used as a variable input parameter for Cost Estimating Relationships (CER) for space systems. As these space systems progress from early concept studies and drawing boards to the launch pad, their masses tend to grow substantially, hence adversely affecting a primary input to most modeling CERs. Modeling and predicting mass uncertainty, based on historical and analogous data, is therefore critical and is an integral part of modeling cost risk. This paper presents the results of a NASA on-going effort to publish mass growth datasheet for adjusting single-point Technical Baseline Estimates (TBE) of masses of space instruments as well as spacecraft, for both earth orbiting and deep space missions at various stages of a project's lifecycle. This paper will also discusses the long term strategy of NASA Headquarters in publishing similar results, using a variety of cost driving metrics, on an annual basis. This paper provides quantitative results that show decreasing mass growth uncertainties as mass estimate maturity increases. This paper's analysis is based on historical data obtained from the NASA Cost Analysis Data Requirements (CADRe) database.

Summary of cost projection for regulatory uncertainties in the back end of the fuel cycle

International Nuclear Information System (INIS)

Raudenbush, M.H.; Geller, L.

1977-01-01

Fuel recycle cost deviations resulting from regulatory changes in the back end of the fuel cycle are examined from a number of different data sources, and three potentially large cost uncertainties are identified; HLW disposal, alpha-waste criteria, and in-plant material control/accountability for safeguards. Present and past methods of regulatory cost effectiveness determinations are critiqued and in some cases found wanting

Predicted costs of environmental controls for a commercial oil shale industry. Volume II. A subjective self-assessment of uncertainty in the predicted costs

Energy Technology Data Exchange (ETDEWEB)

Jovanovich, A.P.; Stone, M.L.; Taylor, G.C.

1979-07-01

The uncertainties in Volume I without extensive additional engineering effort were identified and quantified. Substantial uncertainty was found in several critical variables, allowing a broad range of possible values. Calculations of the cost impact associated with such broad ranges, however, did not always result in significant differences. Seven major areas of pollution control activity were judged to warrant the assessment effort. Three of these areas were found to contain significant uncertainty and additional research is suggested. These areas are: H/sub 2/S removal from the retort gas stream (Stretford process); organic removal from process wastewaters (bio-oxidation or other alternatives); and slurry backfilling of spent Modified In Situ (MIS) retorts. The overall results of the assessment and analysis process are summarized in Table 1-1 in terms of total cost for pollution control. The distributions have been divided into three ranges in this table. A center range is given which contains 80% to 90% probability, and the costs outside this range with probabilities are given. The full distributions can be found in Section 5.0. The subjective probability distributions are a quantification of opinion. The probability of encountering costs below the low figure or above the high figure for each process and scenario is judged to be nearly zero.

Robustness of dynamic systems with parameter uncertainties

CERN Document Server

Balemi, S; Truöl, W

1992-01-01

Robust Control is one of the fastest growing and promising areas of research today. In many practical systems there exist uncertainties which have to be considered in the analysis and design of control systems. In the last decade methods were developed for dealing with dynamic systems with unstructured uncertainties such as HOO_ and £I-optimal control. For systems with parameter uncertainties, the seminal paper of V. L. Kharitonov has triggered a large amount of very promising research. An international workshop dealing with all aspects of robust control was successfully organized by S. P. Bhattacharyya and L. H. Keel in San Antonio, Texas, USA in March 1991. We organized the second international workshop in this area in Ascona, Switzer­ land in April 1992. However, this second workshop was restricted to robust control of dynamic systems with parameter uncertainties with the objective to concentrate on some aspects of robust control. This book contains a collection of papers presented at the International W...

Sensitivity of nuclear fuel cycle cost to uncertainties in nuclear data

International Nuclear Information System (INIS)

Harris, D.R.; Becker, M.; Parvez, A.; Ryskamp, J.M.

1979-01-01

A sensitivity analysis system is developed for assessing the economic implications of uncertainties in nuclear data and related computational methods for light water power reactors. Results of the sensitivity analysis indicate directions for worthwhile improvements in data and methods. Benefits from improvements in data and methods are related to reduction of margins provided by designers to ensure meeting reactor and fuel objectives. Sensitivity analyses are carried out using the batch depletion code FASTCELL, the core analysis code FASTCORE, and the reactor cost code COSTR. FASTCELL depletes a cell using methods comparable to industry cell codes except for a few-group treatment of cell flux distribution. FASTCORE is used with the Haling strategy of fixed power sharing among batches in the core. COSTR computes costs using components and techniques as in industry costing codes, except that COSTR uses fixed payment schedules. Sensitivity analyses are carried out for large commercial boiling and pressurized water reactors. Each few-group nuclear parameter is changed, and initial enrichment is also changed so as to keep the end-of-cycle core multiplication factor unchanged, i.e., to preserve cycle time at the demand power. Sensitivities of equilibrium fuel cycle cost are determined with respect to approx. 300 few-group nuclear parameters, both for a normal fuel cycle and for a throwaway fuel cycle. Particularly large dollar implications are found for thermal and resonance range cross sections in fissile and fertile materials. Sensitivities constrained by adjustment of fission neutron yield so as to preserve agreement with zero exposure integral data also are computed

Securitization of residential solar photovoltaic assets: Costs, risks and uncertainty

International Nuclear Information System (INIS)

Alafita, T.; Pearce, J.M.

2014-01-01

Limited access to low-cost financing is an impediment to high-velocity technological diffusion and high grid penetration of solar photovoltaic (PV) technology. Securitization of solar assets provides a potential solution to this problem. This paper assesses the viability of solar asset-backed securities (ABS) as a lower cost financing mechanism and identifies policies that could facilitate implementation of securitization. First, traditional solar financing is examined to provide a baseline for cost comparisons. Next, the securitization process is modeled. The model enables identification of several junctures at which risk and uncertainty influence costs. Next, parameter values are assigned and used to generate cost estimates. Results show that, under reasonable assumptions, securitization of solar power purchase agreements (PPA) can significantly reduce project financing costs, suggesting that securitization is a viable mechanism for improving the financing of PV projects. The clear impediment to the successful launch of a solar ABS is measuring and understanding the riskiness of underlying assets. This study identifies three classes of policy intervention that lower the cost of ABS by reducing risk or by improving the measurement of risk: (i) standardization of contracts and the contracting process, (ii) improved access to contract and equipment performance data, and (iii) geographic diversification. - Highlights: • Limited access to low-cost financing is hampering penetration of solar PV. • Solar asset-backed securities (ABS) provide a low cost financing mechanism. • Results for securitization of solar leases and power purchase agreements (PPA). • Securitization can significantly reduce project financing costs. • Identifies policy intervention that lower cost of ABS by reducing risk

Incorporating Forecast Uncertainty in Utility Control Center

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2014-07-09

Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception.

Science.gov (United States)

Shimansky, Y P

2011-05-01

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

Science.gov (United States)

Nguyen, Nhan T.

2012-01-01

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

Low cost metamodel for robust design of periodic nonlinear coupled micro-systems

Directory of Open Access Journals (Sweden)

Chikhaoui K.

2016-01-01

Full Text Available To achieve robust design, in presence of uncertainty, nonlinearity and structural periodicity, a metamodel combining the Latin Hypercube Sampling (LHS method for uncertainty propagation and an enriched Craig-Bampton Component Mode Synthesis approach (CB-CMS for model reduction is proposed. Its application to predict the time responses of a stochastic periodic nonlinear micro-system proves its efficiency in terms of accuracy and reduction of computational cost.

Determination of cost effective waste management system receipt rates

International Nuclear Information System (INIS)

McKee, R.W.; Huber, H.D.

1991-01-01

A comprehensive logistics and cost analysis has been carried out to determine if there are potential benefits to the high-level waste management system for receipt rates other than the current 3,000 MTU/yr design-basis receipt rate. The scope of the analysis includes both a Repository-Only System and a Storage-Only or Basic MRS System. To allow for current uncertainties in facility startup scheduling, cases considering repository startup dates of 2010 and 2015 and MRS startup dates of 1998 and three years prior to the repository have been evaluated. Receipt rates ranging from 1,500 to 6,000 MTU/yr have been considered for both the MRS and the repository. Higher receipt rates appear to be economically justified for both the repository and an MRS. For a repository-only system, minimum costs are found at a repository receipt rate of 6,000 MTU/yr. When a storage-only MRS is included in the system, minimum system costs are also achieved at a repository receipt rate of 6,000 MTU/yr. However, the MRS receipt rate for minimum system costs depends on the MRS startup date and ranges from 3,500 to 6,000 MTU/yr. With a 1998 MRS and a 2010 repository, the added cost of providing the MRS is offset by at-reactor storage cost reductions and the total system cost of $10.0 billion is virtually the same as for the repository-only system

Hump-shape Uncertainty, Agency Costs and Aggregate Fluctuations

OpenAIRE

Lee, Gabriel; Kevin, Salyer; Strobel, Johannes

2016-01-01

Previously measured uncertainty shocks using the U.S. data show a hump-shape time path: Uncertainty rises for two years before its decline. Current literature on the effects uncertainty on macroeconomics, including housing, has not accounted for this observation. Consequently, the literature on uncertainty and macroeconomics is divided on the effcts and the propagation mechanism of uncertainty on aggregate uctuations. This paper shows that when uncertainty rises and falls over time, th...

Uncertainties affecting fund collection, management and final utilisation

International Nuclear Information System (INIS)

Soederberg, Olof

2006-01-01

The paper presents, on a general level, major uncertainties in financing systems aiming at providing secure funding for future costs for decommissioning. The perspective chosen is that of a fund collector/manager. The paper also contains a description of how these uncertainties are dealt within the Swedish financing system and particularly from the perspective of the Board of the Swedish Nuclear Waste Fund. It is concluded that existing uncertainties are a good reason not to postpone decommissioning activities to a distant future. This aspect is important also when countries have in place financing systems that have been constructed in order to be robust against identified uncertainties. (author)

Different approaches to overcome uncertainties of production systems

Science.gov (United States)

Azizi, Amir; Sorooshian, Shahryar

2015-05-01

This study presented a comprehensive review on the understanding of uncertainty and the current approaches that have been proposed to handle the uncertainties in the production systems. This paper classified proposed approaches into 11 groups. The paper studied 114 scholarly papers through various international journals. The paper added the latest findings to the body of knowledge to the current reservoir of understanding of the production uncertainties. Thus, the paper prepared the needs of researchers and practitioners for easy references in this area. This review also provided an excellent source to continue further studies on how to deal with the uncertainties of production system.

Planning uncertainties, market risks and new environmental choices: Winning least cost planning combinations

International Nuclear Information System (INIS)

Violette, D.; Lang, C.

1990-01-01

Utility demand and supply-side planners will face new challenges from environmental regulations. Under current proposals, every ton of pollutant will have a cost to utilities, not just the tons that put them over the allowable limit. Planners will have to account for these new costs. To do this, planners need to start tracking emissions implementation actions today, and begin strategies for future regulatory changes. Current legislative proposals include a tax on the carbon content of fuels to curb emissions of greenhouse gases and substantial reductions in sulfur dioxide and nitrogen oxide emissions. The important issue for planners is the flexible compliance requirements within these regulatory changes. The acid rain proposals, for example, include a market-based emissions trading system for emissions allowances. Whenever there is a competitive market, there are market risks, and potential winners and losers. Utilities need to be prepared to analyze and mitigate these risks. Integrated least cost planing is one way a utility will have to meet this challenge. Planning involves uncertainty and risk. The wide array of compliance choices create countless combinations of strategies for utilities to comply with the new emissions regulations. This paper discusses new compliance strategies, demand-side management (DSM) as a compliance strategy, solutions to DSM traps, and the compliance strategy game

Cost analysis of seawater uranium recovered by a polymeric adsorbent system

International Nuclear Information System (INIS)

Schneider, E.; Lindner, H.; Sachde, D.; Flicker, M.

2014-01-01

In tandem with its adsorbent development and marine testing efforts, the United States Department of Energy, Office of Nuclear Energy, routinely updates and expands its cost analysis of technologies for extracting uranium from seawater. If informed by repeatable data from field tests, a rigorous cost analysis can convincingly establish seawater uranium as a “backstop” to conventional uranium resources. A backstop provides an essentially unlimited supply of an otherwise exhaustible resource. Its role is to remove the uncertainty around the long-term sustainability of the resource. The cost analysis ultimately aims to demonstrate a uranium production cost that is sustainable for the nuclear power industry, with no insurmountable technical or environmental roadblocks. It is also a tool for guiding further R&D, identifying inputs and performance factors where further development would offer the greatest reduction in costs and/or uncertainties. A life cycle discounted cash flow methodology is used to calculate the uranium production cost and its uncertainty from the costs of fundamental inputs including chemicals and materials, labor, equipment, energy carriers and facilities. The inputs themselves are defined by process flow models of the adsorbent fabrication and grafting, mooring at sea, recovery, and elution and purification steps in the seawater uranium recovery process. Pacific Northwest National Laboratory (PNNL) has carried out marine tests of the Oak Ridge National Laboratory amidoxime grafted polymer adsorbent in natural seawater. Multiple test campaigns demonstrated that after 60 days of immersion the uranium uptake averaged 3090 ± 310 μg U/g of adsorbent. Past ocean experiments on similar material by the Japan Atomic Energy Agency (JAEA) demonstrated that the adsorbent may be used in the sea six times before being replaced, with 5% uptake degradation per reuse. The mooring and recovery system envisioned for the adsorbent is similar to one proposed by

Accounting for uncertainty in marine reserve design.

Science.gov (United States)

Halpern, Benjamin S; Regan, Helen M; Possingham, Hugh P; McCarthy, Michael A

2006-01-01

Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties

Directory of Open Access Journals (Sweden)

Ryusuke Konishi

2018-01-01

Full Text Available In deregulated electricity markets, minimizing the procurement costs of electricity is a critical problem for procurement agencies (PAs. However, uncertainty is inevitable for PAs and includes multiple factors such as market prices, photovoltaic system (PV output and demand. This study focuses on settlements in multi-period markets (a day-ahead market and a real-time market and the installation of energy storage systems (ESSs. ESSs can be utilized for time arbitrage in the day-ahead market and to reduce the purchasing/selling of electricity in the real-time market. However, the high costs of an ESS mean the size of the system needs to be minimized. In addition, when determining the size of an ESS, it is important to identify the size appropriate for each role. Therefore, we employ the concept of a “slow” and a “fast” ESS to quantify the size of a system’s role, based on the values associated with the various uncertainties. Because the problem includes nonlinearity and non-convexity, we solve it within a realistic computational burden by reformulating the problem using reasonable assumptions. Therefore, this study identifies the optimal sizes of ESSs and procurement, taking into account the uncertainties of prices in multi-period markets, PV output and demand.

Switching Fuzzy Guaranteed Cost Control for Nonlinear Networked Control Systems

Directory of Open Access Journals (Sweden)

Linqin Cai

2014-01-01

Full Text Available This paper deals with the problem of guaranteed cost control for a class of nonlinear networked control systems (NCSs with time-varying delay. A guaranteed cost controller design method is proposed to achieve the desired control performance based on the switched T-S fuzzy model. The switching mechanism is introduced to handle the uncertainties of NCSs. Based on Lyapunov functional approach, some sufficient conditions for the existence of state feedback robust guaranteed cost controller are presented. Simulation results show that the proposed method is effective to guarantee system’s global asymptotic stability and quality of service (QoS.

Deep uncertainty and broad heterogeneity in country-level social cost of carbon

Science.gov (United States)

Ricke, K.; Drouet, L.; Caldeira, K.; Tavoni, M.

2017-12-01

The social cost of carbon (SCC) is a commonly employed metric of the expected economic damages expected from carbon dioxide (CO2) emissions. Recent estimates of SCC range from approximately 10/tonne of CO2 to as much as 1000/tCO2, but these have been computed at the global level. While useful in an optimal policy context, a world-level approach obscures the heterogeneous geography of climate damages and vast differences in country-level contributions to global SCC, as well as climate and socio-economic uncertainties, which are much larger at the regional level. For the first time, we estimate country-level contributions to SCC using recent climate and carbon-cycle model projections, empirical climate-driven economic damage estimations, and information from the Shared Socio-economic Pathways. Central specifications show high global SCC values (median: 417 /tCO2, 66% confidence intervals: 168 - 793 /tCO2) with country-level contributions ranging from -11 (-8 - -14) /tCO2 to 86 (50 - 158) /tCO2. We quantify climate-, scenario- and economic damage- driven uncertainties associated with the calculated values of SCC. We find that while the magnitude of country-level social cost of carbon is highly uncertain, the relative positioning among countries is consistent. Countries incurring large fractions of the global cost include India, China, and the United States. The share of SCC distributed among countries is robust, indicating climate change winners and losers from a geopolitical perspective.

Site utility system optimization with operation adjustment under uncertainty

International Nuclear Information System (INIS)

Sun, Li; Gai, Limei; Smith, Robin

2017-01-01

Highlights: • Uncertainties are classified into time-based and probability-based uncertain factors. • Multi-period operation and recourses deal with uncertainty implementation. • Operation scheduling are specified at the design stage to deal with uncertainties. • Steam mains superheating affects steam distribution and power generation in the system. - Abstract: Utility systems must satisfy process energy and power demands under varying conditions. The system performance is decided by the system configuration and individual equipment operating load for boilers, gas turbines, steam turbines, condensers, and let down valves. Steam mains conditions in terms of steam pressures and steam superheating also play important roles on steam distribution in the system and power generation by steam expansion in steam turbines, and should be included in the system optimization. Uncertainties such as process steam power demand changes and electricity price fluctuations should be included in the system optimization to eliminate as much as possible the production loss caused by steam power deficits due to uncertainties. In this paper, uncertain factors are classified into time-based and probability-based uncertain factors, and operation scheduling containing multi-period equipment load sharing, redundant equipment start up, and electricity import to compensate for power deficits, have been presented to deal with the happens of uncertainties, and are formulated as a multi-period item and a recourse item in the optimization model. There are two case studies in this paper. One case illustrates the system design to determine system configuration, equipment selection, and system operation scheduling at the design stage to deal with uncertainties. The other case provides operational optimization scenarios for an existing system, especially when the steam superheating varies. The proposed method can provide practical guidance to system energy efficiency improvement.

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

Science.gov (United States)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

Science.gov (United States)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: an interim report

Science.gov (United States)

Wood, Alexander

2004-01-01

deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncer

Sensitivity of nuclear fuel-cycle cost to uncertainties in nuclear data. Final report

International Nuclear Information System (INIS)

Becker, M.; Harris, D.R.

1980-11-01

An improved capability for assessing the economic implications of uncertainties in nuclear data and methods on the power reactor fuel cycle was developed. This capability is applied to the sensitivity analysis of fuel-cycle cost with respect to changes in nuclear data and related computational methods. Broad group sensitivities for both a typical BWR and a PWR are determined under the assumption of a throwaway fuel cycle as well as for a scenario under which reprocessing is allowed. Particularly large dollar implications are found for the thermal and resonance cross sections of fissile and fertile materials. Sensitivities for the throwaway case are found to be significantly larger than for the recycle case. Constrained sensitivities obtained for cases in which information from critical experiments or other benchmarks is used in the design calculation to adjust a parameter such as anti ν are compared with unconstrained sensitivities. Sensitivities of various alternate fuel cycles were examined. These included the extended-burnup (18-month) LWR cycle, the mixed-oxide (plutonium) cycle, uranium-thorium and denatured uranium-thorium cycles, as well as CANDU-type reactor cycles. The importance of the thermal capture and fission cross sections of 239 Pu is shown to be very large in all cases. Detailed, energy dependent sensitivity profiles are provided for the thermal range (below 1.855 eV). Finally, sensitivity coefficients are combined with data uncertainties to determine the impact of such uncertainties on fuel-cycle cost parameters

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

Science.gov (United States)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

The economics of planning electricity transmission to accommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty

International Nuclear Information System (INIS)

Weijde, Adriaan Hendrik van der; Hobbs, Benjamin F.

2012-01-01

Aggressive development of renewable electricity sources will require significant expansions in transmission infrastructure. We present a stochastic two-stage optimisation model that captures the multistage nature of transmission planning under uncertainty and use it to evaluate interregional grid reinforcements in Great Britain (GB). In our model, a proactive transmission planner makes investment decisions in two time periods, each time followed by a market response. Uncertainty is represented by economic, technology, and regulatory scenarios, and first-stage investments must be made before it is known which scenario will occur. The model allows us to identify expected cost-minimising first-stage investments, as well as estimate the value of information, the cost of ignoring uncertainty, and the value of flexibility. Our results show that ignoring risk in planning transmission for renewables has quantifiable economic consequences, and that considering uncertainty can yield decisions that have lower expected costs than traditional deterministic planning methods. In the GB case, the value of information and cost of disregarding uncertainty in transmission planning were of the same order of magnitude (approximately £100 M, in present worth terms). Further, the best plan under a risk-neutral decision criterion can differ from the best under risk-aversion. Finally, a traditional sensitivity analysis-based robustness analysis also yields different results than the stochastic model, although the former's expected cost is not much higher.

H 2 guaranteed cost control of discrete linear systems

Directory of Open Access Journals (Sweden)

Colmenares W.

2000-01-01

Full Text Available This paper presents necessary and sufficient conditions for the existence of a quadratically stabilizing output feedback controller which also assures H 2 guaranteed cost performance on a discrete linear uncertain system where the uncertainty is of the norm bounded type. The conditions are presented as a collection of linear matrix inequalities.The solution, however requires a search over a scalar parameter space.

Computational uncertainties in silicon dioxide/plutonium intermediate neutron spectrum systems

International Nuclear Information System (INIS)

Jaegers, P.J.

1997-01-01

In the past several years, several proposals have been made for the long-term stabilization and storage of surplus fissile materials. Many of these proposed scenarios involve systems that have an intermediate neutron energy spectrum. Such intermediate-energy systems are dominated by scattering and fission events induced by neutrons ranging in energy from 1 eV to 100keV. To ensure adequate safety margins and cost effectiveness, it is necessary to have benchmark data for these intermediate-energy spectrum systems; however, a review of the nuclear criticality benchmarks indicates that no formal benchmarks are available. Nuclear data uncertainties have been reported for some types of intermediate-energy spectrum systems. Using a variety of Monte Carlo computer codes and cross-section sets, reported significant variations in the calculated k ∞ of intermediate-energy spectrum metal/ 235 U systems. We discuss the characteristics of intermediate neutron spectrum systems and some of the computational differences that can occur in calculating the k eff of these systems

Probabilistic cost estimating of nuclear power plant construction projects

International Nuclear Information System (INIS)

Finch, W.C.; Perry, L.W.; Postula, F.D.

1978-01-01

This paper shows how to identify and isolate cost accounts by developing probability trees down to component levels as justified by value and cost uncertainty. Examples are given of the procedure for assessing uncertainty in all areas contributing to cost: design, factory equipment pricing, and field labor and materials. The method of combining these individual uncertainties is presented so that the cost risk can be developed for components, systems and the total plant construction project. Formats which enable management to use the probabilistic cost estimate information for business planning and risk control are illustrated. Topics considered include code estimate performance, cost allocation, uncertainty encoding, probabilistic cost distributions, and interpretation. Effective cost control of nuclear power plant construction projects requires insight into areas of greatest cost uncertainty and a knowledge of the factors which can cause costs to vary from the single value estimates. It is concluded that probabilistic cost estimating can provide the necessary assessment of uncertainties both as to the cause and the consequences

Achieving 95% probability level using best estimate codes and the code scaling, applicability and uncertainty (CSAU) [Code Scaling, Applicability and Uncertainty] methodology

International Nuclear Information System (INIS)

Wilson, G.E.; Boyack, B.E.; Duffey, R.B.; Griffith, P.; Katsma, K.R.; Lellouche, G.S.; Rohatgi, U.S.; Wulff, W.; Zuber, N.

1988-01-01

Issue of a revised rule for loss of coolant accident/emergency core cooling system (LOCA/ECCS) analysis of light water reactors will allow the use of best estimate (BE) computer codes in safety analysis, with uncertainty analysis. This paper describes a systematic methodology, CSAU (Code Scaling, Applicability and Uncertainty), which will provide uncertainty bounds in a cost effective, auditable, rational and practical manner. 8 figs., 2 tabs

Uncertainty analysis of nonlinear systems employing the first-order reliability method

International Nuclear Information System (INIS)

Choi, Chan Kyu; Yoo, Hong Hee

2012-01-01

In most mechanical systems, properties of the system elements have uncertainties due to several reasons. For example, mass, stiffness coefficient of a spring, damping coefficient of a damper or friction coefficients have uncertain characteristics. The uncertain characteristics of the elements have a direct effect on the system performance uncertainty. It is very important to estimate the performance uncertainty since the performance uncertainty is directly related to manufacturing yield and consumer satisfaction. Due to this reason, the performance uncertainty should be estimated accurately and considered in the system design. In this paper, performance measures are defined for nonlinear vibration systems and the performance measure uncertainties are estimated employing the first order reliability method (FORM). It was found that the FORM could provide good results in spite of the system nonlinear characteristics. Comparing to the results obtained by Monte Carlo Simulation (MCS), the accuracy of the uncertainty analysis results obtained by the FORM is validated

On treatment of uncertainty in system planning

International Nuclear Information System (INIS)

Flage, R.; Aven, T.

2009-01-01

In system planning and operation considerable efforts and resources are spent to reduce uncertainties, as a part of project management, uncertainty management and safety management. The basic idea seems to be that uncertainties are purely negative and should be reduced. In this paper we challenge this way of thinking, using a common industry practice as an example. In accordance with this industry practice, three uncertainty interval categories are used: ±40% intervals for the feasibility phase, ±30% intervals for the concept development phase and ±20% intervals for the engineering phase. The problem is that such a regime could easily lead to a conservative management regime encouraging the use of existing methods and tools, as new activities and novel solutions and arrangements necessarily mean increased uncertainties. In the paper we suggest an alternative approach based on uncertainty and risk descriptions, but having no predefined uncertainty reduction structures. The approach makes use of risk assessments and economic optimisation tools such as the expected net present value, but acknowledges the need for broad risk management processes which extend beyond the analyses. Different concerns need to be balanced, including economic aspects, uncertainties and risk, and practicability

Cost benefit risk - a concept for management of integrated urban wastewater systems?

DEFF Research Database (Denmark)

Hauger, Mikkel B.; Rauch, W.; Linde, Jens Jørgen

2002-01-01

Urban wastewater systems should be evaluated and analysed from an integrated point of view, taking all parts of the system, that is sewer system, wastewater treatment plant and receiving waters into consideration. Risk and parameter uncertainties are aspects that hardly ever have been addressed...... in the evaluation and design of urban wastewater systems. In this paper we present and discuss a probabilistic approach for evaluation of the performance of urban wastewater systems. Risk analysis together with the traditional cost-benefit analysis is a special variant of multi-criteria analysis that seeks to find...... the most feasible improvement alternative for an urban wastewater system. The most feasible alternative in this context is the alternative that has the best performance, meaning that the alternative has the lowest sum of costs, benefits and risks. The sum is expressed as the Net Present Cost (NPC). To use...

A multi-fuel management model for a community-level district heating system under multiple uncertainties

International Nuclear Information System (INIS)

Fu, D.Z.; Zheng, Z.Y.; Shi, H.B.; Xiao, Rui; Huang, G.H.; Li, Y.P.

2017-01-01

In this study, an interval two-stage double-stochastic single-sided fuzzy chance-constrained programming model is developed for supporting fuel management of a community-level district heating system (DHS) fed with both traditional fossil fuels and renewable biofuels under multiple uncertainties. The proposed model is based on the integration of interval parameter programming and single-sided fuzzy chance-constrained programming within an improved stochastic programming framework to tackle the uncertainties expressed as crisp intervals, fuzzy relationship, and probability distributions. Through transforming and solving the model, the related fuzzy and stochastic information can be effectively reflected in the generated solutions. A real fuel management case of a DHS located in Junpu New District of Dalian is utilized to demonstrate the model applicability. The obtained solutions provides an effective linkage in terms of both ‘‘quality’’ and ‘‘quantity’’ aspects for fuel management under various scenarios associated with multiple factors, and thus can help the decision makers to identify desired fuel allotment patterns. Moreover, this study is also useful for decision makers to address the other challenges (e.g. the imbalance between fuel supply and demand, the contradiction between air-pollution emission and environmental protection, as well as the tradeoff between the total heating cost and system satisfaction degree) generated in the fuel management processes. - Highlights: • A feasible two-stage stochastic programming method is improved. • A multi-fuel management model is developed under multiple uncertainties. • The fuel supply pattern for a district heating system can be obtained. • The variation tendencies of the pollutant emissions are examined. • Tradeoff analyses between system satisfaction degree and cost are carried out.

Mitigating Provider Uncertainty in Service Provision Contracts

Science.gov (United States)

Smith, Chris; van Moorsel, Aad

Uncertainty is an inherent property of open, distributed and multiparty systems. The viability of the mutually beneficial relationships which motivate these systems relies on rational decision-making by each constituent party under uncertainty. Service provision in distributed systems is one such relationship. Uncertainty is experienced by the service provider in his ability to deliver a service with selected quality level guarantees due to inherent non-determinism, such as load fluctuations and hardware failures. Statistical estimators utilized to model this non-determinism introduce additional uncertainty through sampling error. Inability of the provider to accurately model and analyze uncertainty in the quality level guarantees can result in the formation of sub-optimal service provision contracts. Emblematic consequences include loss of revenue, inefficient resource utilization and erosion of reputation and consumer trust. We propose a utility model for contract-based service provision to provide a systematic approach to optimal service provision contract formation under uncertainty. Performance prediction methods to enable the derivation of statistical estimators for quality level are introduced, with analysis of their resultant accuracy and cost.

Uncertainties in early-stage capital cost estimation of process design – a case study on biorefinery design

DEFF Research Database (Denmark)

Cheali, Peam; Gernaey, Krist; Sin, Gürkan

2015-01-01

Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which......) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design...

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs

DEFF Research Database (Denmark)

Hao, Yin; Pizzol, Massimo; Xu, Linyu

2017-01-01

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO2 and NOx. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society....... The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well...... as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both...

Economic potential of fuel recycling options: A lifecycle cost analysis of future nuclear system transition in China

International Nuclear Information System (INIS)

Gao, Ruxing; Choi, Sungyeol; Il Ko, Won; Kim, Sungki

2017-01-01

In today's profit-driven market, how best to pursue advanced nuclear fuel cycle technologies while maintaining the cost competitiveness of nuclear electricity is of crucial importance to determine the implementation of spent fuel reprocessing and recycling in China. In this study, a comprehensive techno-economic analysis is undertaken to evaluate the economic feasibility of ongoing national projects and the technical compatibility with China's future fuel cycle transition. We investigated the dynamic impacts of technical and economic uncertainties in the lifecycle of a nuclear system. The electricity generation costs associated with four potential fuel cycle transition scenarios were simulated by probabilistic and deterministic approaches and then compared in detail. The results showed that the total cost of a once-through system is lowest compared those of other advanced systems involving reprocessing and recycling. However, thanks to the consequential uncertainties caused by the further progress toward technology maturity, the economic potential of fuel recycling options was proven through a probabilistic uncertainty analysis. Furthermore, it is recommended that a compulsory executive of closed fuel cycle policy would pose some investment risk in the near term, though the execution of a series of R&D initiatives with a flexible roadmap would be valuable in the long run. - Highlights: • Real-time economic performance of the four scenarios of China's nuclear fuel cycle system transition until 2100. • Systematic assessments of techno-economic feasibility for ongoing national reprocessing projects. • Investigation the cost impact on nuclear electricity generation caused by uncertainties through probabilistic analysis. • Recommendation for sustainable implementation of fuel cycle R&D initiative ingrate with flexible roadmap in the long run.

Research of Uncertainty Reasoning in Pineapple Disease Identification System

Science.gov (United States)

Liu, Liqun; Fan, Haifeng

In order to deal with the uncertainty of evidences mostly existing in pineapple disease identification system, a reasoning model based on evidence credibility factor was established. The uncertainty reasoning method is discussed,including: uncertain representation of knowledge, uncertain representation of rules, uncertain representation of multi-evidences and update of reasoning rules. The reasoning can fully reflect the uncertainty in disease identification and reduce the influence of subjective factors on the accuracy of the system.

Uncertainties in the daily operation of a district heating plant

DEFF Research Database (Denmark)

Sorknæs, Peter

Studies have found that district heating (DH) systems should play an important role in future sustainable energy systems, but that DH has to adapt to lower heat demands. This means adapting to reduced operation hours for units essential for DHs integration in other parts of the energy system......, such as CHP. It will therefore likely be increasingly important to increase the value per operation hour. The value can be increased by offering balancing for the electricity system. This in turn increases the uncertainties in the daily operation planning of the DH system. In this paper the Danish DH plant...... Ringkøbing District Heating is used as a case to investigate what costs market uncertainties can incur on a DH plant. It is found that the market uncertainties in a 4 months simulated period increased Ringkøbing District Heatings costs by less than 1%. Several factors are however not included in this paper....

Using finite mixture models in thermal-hydraulics system code uncertainty analysis

Energy Technology Data Exchange (ETDEWEB)

Carlos, S., E-mail: scarlos@iqn.upv.es [Department d’Enginyeria Química i Nuclear, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain); Sánchez, A. [Department d’Estadística Aplicada i Qualitat, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain); Ginestar, D. [Department de Matemàtica Aplicada, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain); Martorell, S. [Department d’Enginyeria Química i Nuclear, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain)

2013-09-15

Highlights: • Best estimate codes simulation needs uncertainty quantification. • The output variables can present multimodal probability distributions. • The analysis of multimodal distribution is performed using finite mixture models. • Two methods to reconstruct output variable probability distribution are used. -- Abstract: Nuclear Power Plant safety analysis is mainly based on the use of best estimate (BE) codes that predict the plant behavior under normal or accidental conditions. As the BE codes introduce uncertainties due to uncertainty in input parameters and modeling, it is necessary to perform uncertainty assessment (UA), and eventually sensitivity analysis (SA), of the results obtained. These analyses are part of the appropriate treatment of uncertainties imposed by current regulation based on the adoption of the best estimate plus uncertainty (BEPU) approach. The most popular approach for uncertainty assessment, based on Wilks’ method, obtains a tolerance/confidence interval, but it does not completely characterize the output variable behavior, which is required for an extended UA and SA. However, the development of standard UA and SA impose high computational cost due to the large number of simulations needed. In order to obtain more information about the output variable and, at the same time, to keep computational cost as low as possible, there has been a recent shift toward developing metamodels (model of model), or surrogate models, that approximate or emulate complex computer codes. In this way, there exist different techniques to reconstruct the probability distribution using the information provided by a sample of values as, for example, the finite mixture models. In this paper, the Expectation Maximization and the k-means algorithms are used to obtain a finite mixture model that reconstructs the output variable probability distribution from data obtained with RELAP-5 simulations. Both methodologies have been applied to a separated

US firms still restructuring, cutting costs under oil price uncertainty

International Nuclear Information System (INIS)

Koen, A.D.

1994-01-01

Despite more than a decade of downsizing, continuing uncertainty in oil markets is forcing US petroleum companies into another round of cutting and restructuring operations. Wellhead gas prices in the US, although still volatile, in the past 2 years have risen to levels adequate to allow profits for most producers in that sector. Higher gas reserves valuations have strengthened producers' overall balance sheets. But the slide in oil prices from the middle of fourth quarter 1993 until the recent upswing the past month has withered producers' financial performances and reserves values. With little prospect of significantly higher oil prices anytime soon, US companies feel they have little choice but to continue pressing cost cutting moves in order to sustain profits in the near term while at the same time earnings a higher return on investment in the long term. Petroleum company executives are overlooking almost no operating or investment strategy thought capable of bolstering the bottom line. Because no two US oil and gas companies are alike, each profit protection plan is a unique mix of similar solutions. Oil and gas production companies most often try to lower operating costs by vigorously selling noncore properties or business units and reducing staff. The paper discusses measures taken by oil and gas companies to lower costs

The role of social cost-benefit analysis in societal decision-making under large uncertainties with application to robbery at a cash depot

International Nuclear Information System (INIS)

Jones-Lee, M.; Aven, T.

2009-01-01

Social cost-benefit analysis is a well-established method for guiding decisions about safety investments, particularly in situations in which it is possible to make accurate predictions of future performance. However, its direct applicability to situations involving large degrees of uncertainty is less obvious and this raises the question of the extent to which social cost-benefit analysis can provide a useful input to the decision framework that has been explicitly developed to deal with safety decisions in which uncertainty is a major factor, namely risk analysis. This is the main focus of the arguments developed in this paper. In particular, we provide new insights by examining the fundamentals of both approaches and our principal conclusion is that social cost-benefit analysis and risk analysis represent complementary input bases to the decision-making process, and even in the case of large uncertainties social cost-benefit analysis may provide very useful decision support. What is required is the establishment of a proper contextual framework which structures and gives adequate weight to the uncertainties. An application to the possibility of a robbery at a cash depot is examined as a practical example.

Vulnerability analysis of power systems considering uncertainty in variables using fuzzy logic type 2

Directory of Open Access Journals (Sweden)

Julian Alexander Melo Rodriguez

2016-09-01

Full Text Available Objectives: This paper presents a new methodology for analyzing the vulnerability of power systems including uncertainty in some variables. Method: The methodology optimizes a Bi-level mixed integer model. Costs associated with power generation and load shedding are minimized at the lowest level whereas at the higher level the damage in the power system, represented by the load shedding, is maximized. Fuzzy logic type 2 is used to model the uncertainty in both linguistic variables and numeric variables. The linguistic variables model the factors of the geographical environment while numeric variables model parameters of the power system. Results: The methodology was validated by using a modified IEEE RTS-96 test system. The results show that by including particularities of the geographical environment different vulnerabilities are detected in the power system. Moreover, it was possible to identify that the most critical component is the line 112-123 because it had 16 attacks in 18 scenarios, and that the maximum load shedding of the system varies from 145 to 1258 MW. Conclusions: This methodology can be used to coordinate and refine protection plans of the power system infrastructure. Funding: EMC-UN research group.

Reliability/Cost Evaluation on Power System connected with Wind Power for the Reserve Estimation

DEFF Research Database (Denmark)

Lee, Go-Eun; Cha, Seung-Tae; Shin, Je-Seok

2012-01-01

Wind power is ideally a renewable energy with no fuel cost, but has a risk to reduce reliability of the whole system because of uncertainty of the output. If the reserve of the system is increased, the reliability of the system may be improved. However, the cost would be increased. Therefore...... the reserve needs to be estimated considering the trade-off between reliability and economic aspects. This paper suggests a methodology to estimate the appropriate reserve, when wind power is connected to the power system. As a case study, when wind power is connected to power system of Korea, the effects...

Modeling multibody systems with uncertainties. Part II: Numerical applications

Energy Technology Data Exchange (ETDEWEB)

Sandu, Corina, E-mail: csandu@vt.edu; Sandu, Adrian; Ahmadian, Mehdi [Virginia Polytechnic Institute and State University, Mechanical Engineering Department (United States)

2006-04-15

This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects .In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.

Modeling multibody systems with uncertainties. Part II: Numerical applications

International Nuclear Information System (INIS)

Sandu, Corina; Sandu, Adrian; Ahmadian, Mehdi

2006-01-01

This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects .In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties

A possibilistic model to determine the cost of environmental quality in mid/short term planning of an electricity distribution system

International Nuclear Information System (INIS)

Schweickardt, Gustavo Alejandro; Gimenez Alvarez, Juan Manuel

2012-01-01

This work presents a Possibilistic Optimization Model to determine the Dynamic Environmental Quality Cost, applied on an Electricity Distribution System and measured as Network System Visual Impact. The Mid/Short Term Planning is the Regulatory Control Period. A multicriteria optimization approach is proposed, and for each criterion, non-stochastic uncertainties are recognized and represented by mean the introduction of Fuzzy Sets. In this way, a possibility in the occurrence of criteria variables values is established. In addition, as consequence of uncertainties of criteria preference ranking, a Model to obtain criteria Priority Vector is introduced. The Environmental Quality Cost determination is based in the relationship between the Investment Cost and an Impact Index of Network System Environmental Quality, proposed in this work. Finally, a simulation on a real system and the most important conclusions are presented.

Approaches for Managing Uncertainty in Learning Management Systems

OpenAIRE

Radwan, Nouran M.; Senousy, M. Badr; Riad, Alaa El Din M.

2016-01-01

The notion of uncertainty in expert systems is dealing with vague data, incomplete information, and imprecise knowledge. Different uncertainty types which are imprecision, vagueness, ambiguity, and inconsistence need different handling models. Uncertain knowledge representation and analysis is an essential issue.

Quantifying uncertainty in LCA-modelling of waste management systems

DEFF Research Database (Denmark)

Clavreul, Julie; Guyonnet, D.; Christensen, Thomas Højlund

2012-01-01

Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present...... the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining...

The economic implications of carbon cycle uncertainty

International Nuclear Information System (INIS)

Smith, Steven J.; Edmonds, James A.

2006-01-01

This paper examines the implications of uncertainty in the carbon cycle for the cost of stabilizing carbon dioxide concentrations. Using a state of the art integrated assessment model, we find that uncertainty in our understanding of the carbon cycle has significant implications for the costs of a climate stabilization policy, with cost differences denominated in trillions of dollars. Uncertainty in the carbon cycle is equivalent to a change in concentration target of up to 100 ppmv. The impact of carbon cycle uncertainties are smaller than those for climate sensitivity, and broadly comparable to the effect of uncertainty in technology availability

Implications of nuclear data uncertainties to reactor design

International Nuclear Information System (INIS)

Greebler, P.; Hutchins, B.A.; Cowan, C.L.

1970-01-01

Uncertainties in nuclear data require significant allowances to be made in the design and the operating conditions of reactor cores and of shielded-reactor-plant and fuel-processing systems. These allowances result in direct cost increases due to overdesign of components and equipment and reduced core and fuel operating performance. Compromising the allowances for data uncertainties has indirect cost implications due to increased risks of failure to meet plant and fuel performance objectives, with warrantees involved in some cases, and to satisfy licensed safety requirements. Fast breeders are the most sensitive power reactors to the uncertainties in nuclear data over the neutron energy range of interest for fission reactors, and this paper focuses on the implications of the data uncertainties to design and operation of fast breeder reactors and fuel-processing systems. The current status of uncertainty in predicted physics parameters due to data uncertainties is reviewed and compared with the situation in 1966 and that projected for within the next two years due to anticipated data improvements. Implications of the uncertainties in the predicted physics parameters to design and operation are discussed for both a near-term prototype or demonstration breeder plant (∼300 MW(e)) and a longer-term large (∼1000 MW(e)) plant. Significant improvements in the nuclear data have been made during the past three years, the most important of these to fast power reactors being the 239 Pu alpha below 15 keV. The most important remaining specific data uncertainties are illustrated by their individual contributions to the computational uncertainty of selected physics parameters, and recommended priorities and accuracy requirements for improved data are presented

Water supply infrastructure planning under multiple uncertainties: A differentiated approach

Science.gov (United States)

Fletcher, S.; Strzepek, K.

2017-12-01

Many water planners face increased pressure on water supply systems from increasing demands from population and economic growth in combination with uncertain water supply. Supply uncertainty arises from short-term climate variability and long-term climate change as well as uncertainty in groundwater availability. Social and economic uncertainties - such as sectoral competition for water, food and energy security, urbanization, and environmental protection - compound physical uncertainty. Further, the varying risk aversion of stakeholders and water managers makes it difficult to assess the necessity of expensive infrastructure investments to reduce risk. We categorize these uncertainties on two dimensions: whether they can be updated over time by collecting additional information, and whether the uncertainties can be described probabilistically or are "deep" uncertainties whose likelihood is unknown. Based on this, we apply a decision framework that combines simulation for probabilistic uncertainty, scenario analysis for deep uncertainty, and multi-stage decision analysis for uncertainties that are reduced over time with additional information. In light of these uncertainties and the investment costs of large infrastructure, we propose the assessment of staged, modular infrastructure and information updating as a hedge against risk. We apply this framework to cases in Melbourne, Australia and Riyadh, Saudi Arabia. Melbourne is a surface water system facing uncertain population growth and variable rainfall and runoff. A severe drought from 1997 to 2009 prompted investment in a 150 MCM/y reverse osmosis desalination plan with a capital cost of 3.5 billion. Our analysis shows that flexible design in which a smaller portion of capacity is developed initially with the option to add modular capacity in the future can mitigate uncertainty and reduce the expected lifetime costs by up to 1 billion. In Riyadh, urban water use relies on fossil groundwater aquifers and

Uncertainty of Water-hammer Loads for Safety Related Systems

Energy Technology Data Exchange (ETDEWEB)

Lee, Seung Chan; Yoon, Duk Joo [Korea Hydro and Nuclear Power Co., LT., Daejeon (Korea, Republic of)

2013-10-15

In this study, the basic methodology is base on ISO GUM (Guide to the Expression of Uncertainty in Measurements). For a given gas void volumes in the discharge piping, the maximum pressure of water hammer is defined in equation. From equation, uncertainty parameter is selected as U{sub s} (superficial velocity for the specific pipe size and corresponding area) of equation. The main uncertainty parameter (U{sub s}) is estimated by measurement method and Monte Carlo simulation. Two methods are in good agreement with the extended uncertainty. Extended uncertainty of the measurement and Monte Carlo simulation is 1.30 and 1.34 respectively in 95% confidence interval. In 99% confidence interval, the uncertainties are 1.95 and 1.97 respectively. NRC Generic Letter 2008-01 requires nuclear power plant operators to evaluate the possibility of noncondensable gas accumulation for the Emergency Core Cooling System. Specially, gas accumulation can result in system pressure transient in pump discharge piping at a pump start. Consequently, this evolves into a gas water, a water-hammer event and the force imbalances on the piping segments. In this paper, MCS (Monte Carlo Simulation) method is introduced in estimating the uncertainty of water hammer. The aim is to evaluate the uncertainty of the water hammer estimation results carried out by KHNP CRI in 2013.

Optimal Time to Invest Energy Storage System under Uncertainty Conditions

Directory of Open Access Journals (Sweden)

Yongma Moon

2014-04-01

Full Text Available This paper proposes a model to determine the optimal investment time for energy storage systems (ESSs in a price arbitrage trade application under conditions of uncertainty over future profits. The adoption of ESSs can generate profits from price arbitrage trade, which are uncertain because the future marginal prices of electricity will change depending on supply and demand. In addition, since the investment is optional, an investor can delay adopting an ESS until it becomes profitable, and can decide the optimal time. Thus, when we evaluate this investment, we need to incorporate the investor’s option which is not captured by traditional evaluation methods. In order to incorporate these aspects, we applied real option theory to our proposed model, which provides an optimal investment threshold. Our results concerning the optimal time to invest show that if future profits that are expected to be obtained from arbitrage trade become more uncertain, an investor needs to wait longer to invest. Also, improvement in efficiency of ESSs can reduce the uncertainty of arbitrage profit and, consequently, the reduced uncertainty enables earlier ESS investment, even for the same power capacity. Besides, when a higher rate of profits is expected and ESS costs are higher, an investor needs to wait longer. Also, by comparing a widely used net present value model to our real option model, we show that the net present value method underestimates the value for ESS investment and misleads the investor to make an investment earlier.

Optimal sizing of small wind/battery systems considering the DC bus voltage stability effect on energy capture, wind speed variability, and load uncertainty

International Nuclear Information System (INIS)

Lujano-Rojas, Juan M.; Dufo-López, Rodolfo; Bernal-Agustín, José L.

2012-01-01

Highlights: ► We propose a mathematical model for optimal sizing of small wind energy systems. ► No other previous work has considered all the aspects included in this paper. ► The model considers several parameters about batteries. ► Wind speed variability is considered by means of ARMA model. ► The results show how to minimize the expected energy that is not supplied. - Abstract: In this paper, a mathematical model for stochastic simulation and optimization of small wind energy systems is presented. This model is able to consider the operation of the charge controller, the coulombic efficiency during charge and discharge processes, the influence of temperature on the battery bank capacity, the wind speed variability, and load uncertainty. The joint effect of charge controller operation, ambient temperature, and coulombic efficiency is analyzed in a system installed in Zaragoza (Spain), concluding that if the analysis without considering these factors is carried out, the reliability level of the physical system could be lower than expected, and an increment of 25% in the battery bank capacity would be required to reach a reliability level of 90% in the analyzed case. Also, the effect of the wind speed variability and load uncertainty in the system reliability is analyzed. Finally, the uncertainty in the battery bank lifetime and its effect on the net present cost are discussed. The results showed that, considering uncertainty of 17.5% in the battery bank lifetime calculated using the Ah throughput model, about 12% of uncertainty in the net present cost is expected. The model presented in this research could be a useful stochastic simulation and optimization tool that allows the consideration of important uncertainty factors in techno-economic analysis.

Measuring the environmental benefits of hydrogen transportation fuel cycles under uncertainty about external costs

International Nuclear Information System (INIS)

Chernyavs'ka, Liliya; Gulli, Francesco

2010-01-01

In this paper, we attempt to measure the environmental benefits of hydrogen deployment in the transportation sector. We compare the hydrogen pathways to the conventional transportation fuel cycles in terms of external costs, estimated using the results of the most accurate methodologies available in this field. The central values of performed analysis bring us ambiguous results. The external cost of the best conventional solution ('oil to diesel hybrid internal-combustion engine') in some cases is just higher and in others just lower than that of the best fossil fuel to hydrogen solution ('natural gas to hydrogen fuel cell'). Nevertheless, by accounting for the uncertainty about external costs, we are able to remove this ambiguity highlighting that the hydrogen pathway provides significant environmental benefits ,especially in densely populated areas, assuming 100% city driving.

Uncertainty assessment for accelerator-driven systems

International Nuclear Information System (INIS)

Finck, P. J.; Gomes, I.; Micklich, B.; Palmiotti, G.

1999-01-01

The concept of a subcritical system driven by an external source of neutrons provided by an accelerator ADS (Accelerator Driver System) has been recently revived and is becoming more popular in the world technical community with active programs in Europe, Russia, Japan, and the U.S. A general consensus has been reached in adopting for the subcritical component a fast spectrum liquid metal cooled configuration. Both a lead-bismuth eutectic, sodium and gas are being considered as a coolant; each has advantages and disadvantages. The major expected advantage is that subcriticality avoids reactivity induced transients. The potentially large subcriticality margin also should allow for the introduction of very significant quantities of waste products (minor Actinides and Fission Products) which negatively impact the safety characteristics of standard cores. In the U.S. these arguments are the basis for the development of the Accelerator Transmutation of Waste (ATW), which has significant potential in reducing nuclear waste levels. Up to now, neutronic calculations have not attached uncertainties on the values of the main nuclear integral parameters that characterize the system. Many of these parameters (e.g., degree of subcriticality) are crucial to demonstrate the validity and feasibility of this concept. In this paper we will consider uncertainties related to nuclear data only. The present knowledge of the cross sections of many isotopes that are not usually utilized in existing reactors (like Bi, Pb-207, Pb-208, and also Minor Actinides and Fission Products) suggests that uncertainties in the integral parameters will be significantly larger than for conventional reactor systems, and this raises concerns on the neutronic performance of those systems

Nuclear costs: indicators and uncertainties

International Nuclear Information System (INIS)

Leveque, Francois

2013-01-01

In order to identify whether it is better to build a gas plant, a nuclear plant or a wind farm, to identify the technology leading to the lowest KWh cost, to identify under which conditions nuclear production is profitable for a private investor, and to identify whether taking the dismantling cost and the waste storage cost into account modifies the nuclear competitiveness with respect to general interest, the author first discusses the different costs of the nuclear sector, their sensitivity to different factors. In a second part, he proposes a retrospective discussion of cost dynamics. Then, as nuclear technology seems characterized by always increasing costs, and as this trend may last, notably because of safety concerns, the author proposes an analysis of the poor competitiveness of nuclear with respect to its cost

A critical evaluation of deterministic methods in size optimisation of reliable and cost effective standalone hybrid renewable energy systems

International Nuclear Information System (INIS)

Maheri, Alireza

2014-01-01

Reliability of a hybrid renewable energy system (HRES) strongly depends on various uncertainties affecting the amount of power produced by the system. In the design of systems subject to uncertainties, both deterministic and nondeterministic design approaches can be adopted. In a deterministic design approach, the designer considers the presence of uncertainties and incorporates them indirectly into the design by applying safety factors. It is assumed that, by employing suitable safety factors and considering worst-case-scenarios, reliable systems can be designed. In fact, the multi-objective optimisation problem with two objectives of reliability and cost is reduced to a single-objective optimisation problem with the objective of cost only. In this paper the competence of deterministic design methods in size optimisation of reliable standalone wind–PV–battery, wind–PV–diesel and wind–PV–battery–diesel configurations is examined. For each configuration, first, using different values of safety factors, the optimal size of the system components which minimises the system cost is found deterministically. Then, for each case, using a Monte Carlo simulation, the effect of safety factors on the reliability and the cost are investigated. In performing reliability analysis, several reliability measures, namely, unmet load, blackout durations (total, maximum and average) and mean time between failures are considered. It is shown that the traditional methods of considering the effect of uncertainties in deterministic designs such as design for an autonomy period and employing safety factors have either little or unpredictable impact on the actual reliability of the designed wind–PV–battery configuration. In the case of wind–PV–diesel and wind–PV–battery–diesel configurations it is shown that, while using a high-enough margin of safety in sizing diesel generator leads to reliable systems, the optimum value for this margin of safety leading to a

A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

International Nuclear Information System (INIS)

Kim, Kihyung; Spakovsky, Michael R. von; Wang, M.; Nelson, Douglas J.

2011-01-01

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer's objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies. To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.

Managing Model Data Introduced Uncertainties in Simulator Predictions for Generation IV Systems via Optimum Experimental Design

Energy Technology Data Exchange (ETDEWEB)

Turinsky, Paul J [North Carolina State Univ., Raleigh, NC (United States); Abdel-Khalik, Hany S [North Carolina State Univ., Raleigh, NC (United States); Stover, Tracy E [North Carolina State Univ., Raleigh, NC (United States)

2011-03-01

An optimization technique has been developed to select optimized experimental design specifications to produce data specifically designed to be assimilated to optimize a given reactor concept. Data from the optimized experiment is assimilated to generate posteriori uncertainties on the reactor concept’s core attributes from which the design responses are computed. The reactor concept is then optimized with the new data to realize cost savings by reducing margin. The optimization problem iterates until an optimal experiment is found to maximize the savings. A new generation of innovative nuclear reactor designs, in particular fast neutron spectrum recycle reactors, are being considered for the application of closing the nuclear fuel cycle in the future. Safe and economical design of these reactors will require uncertainty reduction in basic nuclear data which are input to the reactor design. These data uncertainty propagate to design responses which in turn require the reactor designer to incorporate additional safety margin into the design, which often increases the cost of the reactor. Therefore basic nuclear data needs to be improved and this is accomplished through experimentation. Considering the high cost of nuclear experiments, it is desired to have an optimized experiment which will provide the data needed for uncertainty reduction such that a reactor design concept can meet its target accuracies or to allow savings to be realized by reducing the margin required due to uncertainty propagated from basic nuclear data. However, this optimization is coupled to the reactor design itself because with improved data the reactor concept can be re-optimized itself. It is thus desired to find the experiment that gives the best optimized reactor design. Methods are first established to model both the reactor concept and the experiment and to efficiently propagate the basic nuclear data uncertainty through these models to outputs. The representativity of the experiment

Understanding the Uncertainty of an Effectiveness-Cost Ratio in Educational Resource Allocation: A Bayesian Approach

Science.gov (United States)

Pan, Yilin

2016-01-01

Given the necessity to bridge the gap between what happened and what is likely to happen, this paper aims to explore how to apply Bayesian inference to cost-effectiveness analysis so as to capture the uncertainty of a ratio-type efficiency measure. The first part of the paper summarizes the characteristics of the evaluation data that are commonly…

Verification and uncertainty evaluation of HELIOS/MASTER nuclear design system

Energy Technology Data Exchange (ETDEWEB)

Song, Jae Seung; Kim, J. C.; Cho, B. O. [Korea Atomic Energy Research Institute, Taejon (Korea)

1999-03-01

A nuclear design system HELIOS/MASTER was established and core follow calculations were performed for Yonggwang Unit 1 cycles 1 through 7 and Yonggwang Unit 3 cycles 1 through 2. The accuracy of HELIOS/MASTER system was evaluated by estimations of uncertainties of reactivity and peaking factors and by comparisons of the maximum differences of isothermal temperature coefficient, inverse boron worth and control rod worth with the CASMO-3/MASTER uncertainties. The reactivity uncertainty was estimated by 362 pcm, and the uncertainties of three-dimensional, axially integrated radial, and planar peaking factors were evaluated by 0.048, 0.034, and 0.044 in relative power unit, respectively. The maximum differences of isothermal temperature coefficient, inverse boron worth and control rod worth were within the CASMO-3/MASTER uncertainties. 17 refs., 17 figs., 10 tabs. (Author)

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming.

Science.gov (United States)

Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun

2015-01-01

The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.

Robust SMES controller design for stabilization of inter-area oscillation considering coil size and system uncertainties

International Nuclear Information System (INIS)

Ngamroo, Issarachai

2010-01-01

It is well known that the superconducting magnetic energy storage (SMES) is able to quickly exchange active and reactive power with the power system. The SMES is expected to be the smart storage device for power system stabilization. Although the stabilizing effect of SMES is significant, the SMES is quite costly. Particularly, the superconducting magnetic coil size which is the essence of the SMES, must be carefully selected. On the other hand, various generation and load changes, unpredictable network structure, etc., cause system uncertainties. The power controller of SMES which is designed without considering such uncertainties, may not tolerate and loses stabilizing effect. To overcome these problems, this paper proposes the new design of robust SMES controller taking coil size and system uncertainties into account. The structure of the active and reactive power controllers is the 1st-order lead-lag compensator. No need for the exact mathematical representation, system uncertainties are modeled by the inverse input multiplicative perturbation. Without the difficulty of the trade-off of damping performance and robustness, the optimization problem of control parameters is formulated. The particle swarm optimization is used for solving the optimal parameters at each coil size automatically. Based on the normalized integral square error index and the consideration of coil current constraint, the robust SMES with the smallest coil size which still provides the satisfactory stabilizing effect, can be achieved. Simulation studies in the two-area four-machine interconnected power system show the superior robustness of the proposed robust SMES with the smallest coil size under various operating conditions over the non-robust SMES with large coil size.

Robust SMES controller design for stabilization of inter-area oscillation considering coil size and system uncertainties

Science.gov (United States)

Ngamroo, Issarachai

2010-12-01

It is well known that the superconducting magnetic energy storage (SMES) is able to quickly exchange active and reactive power with the power system. The SMES is expected to be the smart storage device for power system stabilization. Although the stabilizing effect of SMES is significant, the SMES is quite costly. Particularly, the superconducting magnetic coil size which is the essence of the SMES, must be carefully selected. On the other hand, various generation and load changes, unpredictable network structure, etc., cause system uncertainties. The power controller of SMES which is designed without considering such uncertainties, may not tolerate and loses stabilizing effect. To overcome these problems, this paper proposes the new design of robust SMES controller taking coil size and system uncertainties into account. The structure of the active and reactive power controllers is the 1st-order lead-lag compensator. No need for the exact mathematical representation, system uncertainties are modeled by the inverse input multiplicative perturbation. Without the difficulty of the trade-off of damping performance and robustness, the optimization problem of control parameters is formulated. The particle swarm optimization is used for solving the optimal parameters at each coil size automatically. Based on the normalized integral square error index and the consideration of coil current constraint, the robust SMES with the smallest coil size which still provides the satisfactory stabilizing effect, can be achieved. Simulation studies in the two-area four-machine interconnected power system show the superior robustness of the proposed robust SMES with the smallest coil size under various operating conditions over the non-robust SMES with large coil size.

Centralised, decentralised or hybrid sanitation systems? Economic evaluation under urban development uncertainty and phased expansion.

Science.gov (United States)

Roefs, Ivar; Meulman, Brendo; Vreeburg, Jan H G; Spiller, Marc

2017-02-01

Sanitation systems are built to be robust, that is, they are dimensioned to cope with population growth and other variability that occurs throughout their lifetime. It was recently shown that building sanitation systems in phases is more cost effective than one robust design. This phasing can take place by building small autonomous decentralised units that operate closer to the actual demand. Research has shown that variability and uncertainty in urban development does affect the cost effectiveness of this approach. Previous studies do not, however, consider the entire sanitation system from collection to treatment. The aim of this study is to assess the economic performance of three sanitation systems with different scales and systems characteristics under a variety of urban development pathways. Three systems are studied: (I) a centralised conventional activated sludge treatment, (II) a community on site source separation grey water and black water treatment and (III) a hybrid with grey water treatment at neighbourhood scale and black water treatment off site. A modelling approach is taken that combines a simulation of greenfield urban growth, a model of the wastewater collection and treatment infrastructure design properties and a model that translates design parameters into discounted asset lifetime costs. Monte Carlo simulations are used to evaluate the economic performance under uncertain development trends. Results show that the conventional system outperforms both of the other systems when total discounted lifetime costs are assessed, because it benefits from economies of scale. However, when population growth is lower than expected, the source-separated system is more cost effective, because of reduced idle capacity. The hybrid system is not competitive under any circumstance due to the costly double piping and treatment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Role of nuclear fusion in future energy systems and the environment under future uncertainties

International Nuclear Information System (INIS)

Tokimatsu, Koji; Fujino, Jun'ichi; Konishi, Satoshi; Ogawa, Yuichi; Yamaji, Kenji

2003-01-01

Debates about whether or not to invest heavily in nuclear fusion as a future innovative energy option have been made within the context of energy technology development strategies. This is because the prospects for nuclear fusion are quite uncertain and the investments therefore carry the risk of quite large regrets, even though investment is needed in order to develop the technology. The timeframe by which nuclear fusion could become competitive in the energy market has not been adequately studied, nor has roles of the nuclear fusion in energy systems and the environment. The present study has two objectives. One is to reveal the conditions under which nuclear fusion could be introduced economically (hereafter, we refer to such introductory conditions as breakeven prices) in future energy systems. The other objective is to evaluate the future roles of nuclear fusion in energy systems and in the environment. Here we identify three roles that nuclear fusion will take on when breakeven prices are achieved: (i) a portion of the electricity market in 2100, (ii) reduction of annual global total energy systems cost, and (iii) mitigation of carbon tax (shadow price of carbon) under CO 2 constraints. Future uncertainties are key issues in evaluating nuclear fusion. Here we treated the following uncertainties: energy demand scenarios, introduction timeframe for nuclear fusion, capacity projections of nuclear fusion, CO 2 target in 2100, capacity utilization ratio of options in energy/environment technologies, and utility discount rates. From our investigations, we conclude that the presently designed nuclear fusion reactors may be ready for economical introduction into energy systems beginning around 2050-2060, and we can confirm that the favorable introduction of the reactors would reduce both the annual energy systems cost and the carbon tax (the shadow price of carbon) under a CO 2 concentration constraint

Development of an integrated model for energy systems planning and carbon dioxide mitigation under uncertainty - Tradeoffs between two-level decision makers.

Science.gov (United States)

Jin, S W; Li, Y P; Xu, L P

2018-07-01

A bi-level fuzzy programming (BFLP) method was developed for energy systems planning (ESP) and carbon dioxide (CO 2 ) mitigation under uncertainty. BFLP could handle fuzzy information and leader-follower problem in decision-making processes. It could also address the tradeoffs among different decision makers in two decision-making levels through prioritizing the most important goal. Then, a BFLP-ESP model was formulated for planning energy system of Beijing, in which the upper-level objective is to minimize CO 2 emission and the lower-level objective is to minimize the system cost. Results provided a range of decision alternatives that corresponded to a tradeoff between system optimality and reliability under uncertainty. Compared to the single-level model with a target to minimize system cost, the amounts of pollutant/CO 2 emissions from BFLP-ESP were reduced since the study system would prefer more clean energies (i.e. natural gas, LPG and electricity) to replace coal fuel. Decision alternatives from BFLP were more beneficial for supporting Beijing to adjust its energy mix and enact its emission-abatement policy. Results also revealed that the low-carbon policy for power plants (e.g., shutting down all coal-fired power plants) could lead to a potentially increment of imported energy for Beijing, which would increase the risk of energy shortage. The findings could help decision makers analyze the interactions between different stakeholders in ESP and provide useful information for policy design under uncertainty. Copyright © 2018 Elsevier Inc. All rights reserved.

The ultimate uncertainty--intergenerational planning.

Science.gov (United States)

Starr, C

2000-12-01

The philosophic and practical aspects of intergenerational planning for a 50-100-year time frame are reviewed, with recognition of its speculative quality. Society's near term choice of future physical pathways based on comparative quantitative benefit/cost/risk analyses of alternatives is usually modified by the intervention of a variety of time-dependent, nontechnical value systems. Further, the continuous competition among society's disparate technical systems, capital investment choices, and planning objectives all contribute to the uncertainty of the intergenerational outcome of any plan. Nevertheless, the quantitative planning process provides an essential base. Benefit/cost/risk projections are discussed for both the case with a historical database and the case without such a historical base. The end-objectives and continuous nature of such benefit/cost/risk analyses are described.

Determination of uncertainty of automated emission measuring systems under field conditions using a second method as a reference

Energy Technology Data Exchange (ETDEWEB)

Puustinen, H.; Aunela-Tapola, L.; Tolvanen, M.; Vahlman, T. [VTT Chemical Technology, Espoo (Finland). Environmental Technology; Kovanen, K. [VTT Building Technology, Espoo (Finland). Building Physics, Building Services and Fire Technology

1999-09-01

This report presents a procedure to determine the uncertainty of an automated emission measuring system (AMS) by comparing the results with a second method (REF). The procedure determines the uncertainty of AMS by comparing the final concentration and emission results of AMS and REF. In this way, the data processing of the plant is included in the result evaluation. This procedure assumes that the uncertainty of REF is known and determined in due form. The uncertainty determination has been divided into two cases; varying and nearly constant concentration. The suggested procedure calculates the uncertainty of AMS at the 95 % confidence level by a tabulated t-value. A minimum of three data pairs is required. However, a higher amount of data pairs is desirable, since a low amount of data pairs results in a higher uncertainty of AMS. The uncertainty of AMS is valid only within the range of concentrations at which the tests were carried out. Statistical data processing shows that the uncertainty of the reference method has a significant effect on the uncertainty of AMS, which always becomes larger than the uncertainty of REF. This should be taken into account when testing whether AMS fulfils the given uncertainty limits. Practical details, concerning parallel measurements at the plant, and the costs of the measurement campaign, have been taken into account when suggesting alternative ways for implementing the comparative measurements. (orig.) 6 refs.

Costs and potentials of energy conservation in China's coal-fired power industry: A bottom-up approach considering price uncertainties

International Nuclear Information System (INIS)

Chen, Hao; Kang, Jia-Ning; Liao, Hua; Tang, Bao-Jun; Wei, Yi-Ming

2017-01-01

Energy conservation technologies in the coal-fired power sector are important solutions for the environmental pollution and climate change issues. However, a unified framework for estimating their costs and potentials is still needed due to the wide technology choices, especially considering their economic feasibility under fuel and carbon price uncertainties. Therefore, this study has employed a bottom-up approach to analyze the costs and potentials of 32 key technologies’ new promotions during the 13th Five-Year Plan period (2016–2020), which combines the conservation supply curve (CSC) approach and break-even analysis. Findings show that (1) these 32 technologies have a total coal conservation potential of 275.77 Mt with a cost of 238.82 billion yuan, and their break-even coal price is 866 yuan/ton. (2) steam-water circulation system has the largest energy conservation potential in the coal-fired power industry. (3) considering the co-benefits will facilitate these technologies’ promotions, because their break-even coal prices will decrease by 2.35 yuan/ton when the carbon prices increase by 1 yuan/ton. (4) discount rates have the largest impacts on the technologies’ cost-effectiveness, while the future generation level affect their energy conservation potentials most. - Highlights: • The 32 technologies can save 275.77 Mt coal with a cost of 238.82 billion yuan. • The steam-water circulation system has the largest energy conservation potential. • Considering the co-benefits will facilitate the technology promotions • Discount rates have the largest impacts on the technologies’ cost-effectiveness.

Using interpolation to estimate system uncertainty in gene expression experiments.

Directory of Open Access Journals (Sweden)

Lee J Falin

Full Text Available The widespread use of high-throughput experimental assays designed to measure the entire complement of a cell's genes or gene products has led to vast stores of data that are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured intervals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within unmeasured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. Although the context for developing the algorithm was gene expression measurements taken over a time series, the approach can be readily applied to any set of quantitative systems biology measurements taken following quantitative (i.e. non-categorical treatments. In principle, the method could also be applied to combinations of treatments, in which case it could greatly simplify the task of exploring the large combinatorial space of future possible measurements.

Japanese Cost Accounting Systems - analysis of the cost accounting systems of the Japanese cost accounting standard

OpenAIRE

Peter, Winter

2005-01-01

This paper aims at providing an insight into Japanese cost accounting. Firstly, the development of cost accounting in Japan is delineated. Subsequently, the cost accounting systems codified in the Japanese cost accounting standard are analysed based on the classification according to Hoitsch/Schmitz. Lastly, a critical appraisal of the cost accounting systems of the Japanese cost accounting standard as well as a comparison to German and American cost accounting systems are conducted.

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

Energy Technology Data Exchange (ETDEWEB)

Wang, Ying; Zhou, Zhi; Botterud, Audun; Zhang, Kaifeng

2018-01-01

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study

International Nuclear Information System (INIS)

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2016-01-01

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones. - Highlights: • An extensive comparative study for renewable integration is presented. • A novel scenario generation method is proposed. • Wind and solar uncertainties are represented by a list of prediction intervals. • Unit commitment and dispatch costs are discussed considering reserve and risk.

Cost of photovoltaic energy systems as determined by balance-of-system costs

Science.gov (United States)

Rosenblum, L.

1978-01-01

The effect of the balance-of-system (BOS), i.e., the total system less the modules, on photo-voltaic energy system costs is discussed for multikilowatt, flat-plate systems. Present BOS costs are in the range of 10 to 16 dollars per peak watt (1978 dollars). BOS costs represent approximately 50% of total system cost. The possibility of future BOS cost reduction is examined. It is concluded that, given the nature of BOS costs and the lack of comprehensive national effort focussed on cost reduction, it is unlikely that BOS costs will decline greatly in the next several years. This prognosis is contrasted with the expectations of the Department of Energy National Photovoltaic Program goals and pending legislation in the Congress which require a BOS cost reduction of an order of magnitude or more by the mid-1980s.

Preliminary Uncertainty Analysis for SMART Digital Core Protection and Monitoring System

International Nuclear Information System (INIS)

Koo, Bon Seung; In, Wang Kee; Hwang, Dae Hyun

2012-01-01

The Korea Atomic Energy Research Institute (KAERI) developed on-line digital core protection and monitoring systems, called SCOPS and SCOMS as a part of SMART plant protection and monitoring system. SCOPS simplified the protection system by directly connecting the four RSPT signals to each core protection channel and eliminated the control element assembly calculator (CEAC) hardware. SCOMS adopted DPCM3D method in synthesizing core power distribution instead of Fourier expansion method being used in conventional PWRs. The DPCM3D method produces a synthetic 3-D power distribution by coupling a neutronics code and measured in-core detector signals. The overall uncertainty analysis methodology which is used statistically combining uncertainty components of SMART core protection and monitoring system was developed. In this paper, preliminary overall uncertainty factors for SCOPS/SCOMS of SMART initial core were evaluated by applying newly developed uncertainty analysis method

Derivative-free optimization under uncertainty applied to costly simulators

International Nuclear Information System (INIS)

Pauwels, Benoit

2016-01-01

The modeling of complex phenomena encountered in industrial issues can lead to the study of numerical simulation codes. These simulators may require extensive execution time (from hours to days), involve uncertain parameters and even be intrinsically stochastic. Importantly within the context of simulation-based optimization, the derivatives of the outputs with respect to the inputs may be inexistent, inaccessible or too costly to approximate reasonably. This thesis is organized in four chapters. The first chapter discusses the state of the art in derivative-free optimization and uncertainty modeling. The next three chapters introduce three independent - although connected - contributions to the field of derivative-free optimization in the presence of uncertainty. The second chapter addresses the emulation of costly stochastic simulation codes - stochastic in the sense simulations run with the same input parameters may lead to distinct outputs. Such was the matter of the CODESTOCH project carried out at the Summer mathematical research center on scientific computing and its applications (CEMRACS) during the summer of 2013, together with two Ph.D. students from Electricity of France (EDF) and the Atomic Energy and Alternative Energies Commission (CEA). We designed four methods to build emulators for functions whose values are probability density functions. These methods were tested on two toy functions and applied to industrial simulation codes concerned with three complex phenomena: the spatial distribution of molecules in a hydrocarbon system (IFPEN), the life cycle of large electric transformers (EDF) and the repercussions of a hypothetical accidental in a nuclear plant (CEA). Emulation was a preliminary process towards optimization in the first two cases. In the third chapter we consider the influence of inaccurate objective function evaluations on direct search - a classical derivative-free optimization method. In real settings inaccuracy may never vanish

Evaluation of uncertainty in dosimetry of irradiator system

International Nuclear Information System (INIS)

Santos, Gelson P.; Potiens, Maria P.A.; Vivolo, Vitor

2005-01-01

This paper describes the study of uncertainties in the estimates of dosimetry irradiator system STS 0B85 of LCI IPEN/CNEN-SP. This study is relevant for determination of best measurement capability when the laboratory performs routine calibrations of measuring radiation next the optimal measures designed to radioprotection. It is also a requirement for obtaining the accreditation of the laboratory by the INMETRO. For this dosimetry was used a reference system of the laboratory composed of a electrometer and a spherical ionization chamber of 1 liter. Measurements were made at five distances selected so to include the whole range of the optical bench tests and using three attenuators filters so as to extend the measurement capability. The magnitude used for evaluation was the rate of air kerma for 1 37C s and 6 0C o beams. Were carried out four series of measurements. It was verified the inverse square law to these series and their sets of uncertainty. Unfiltered, with one and two filters series showed good agreement with the inverse square low and the maximum uncertainty obtained was approximately 1.7%. In series with all the filters was a major deviation of the inverse square law and wide increase in uncertainty to measurements at the end of the optical bench

Sustainable infrastructure system modeling under uncertainties and dynamics

Science.gov (United States)

Huang, Yongxi

potential risks caused by feedstock seasonality and demand uncertainty. Facility spatiality, time variation of feedstock yields, and demand uncertainty are integrated into a two-stage stochastic programming (SP) framework. In the study of Transitional Energy System Modeling under Uncertainty, a multistage stochastic dynamic programming is established to optimize the process of building and operating fuel production facilities during the transition. Dynamics due to the evolving technologies and societal changes and uncertainty due to demand fluctuations are the major issues to be addressed.

Generalization of uncertainty relation for quantum and stochastic systems

Science.gov (United States)

Koide, T.; Kodama, T.

2018-06-01

The generalized uncertainty relation applicable to quantum and stochastic systems is derived within the stochastic variational method. This relation not only reproduces the well-known inequality in quantum mechanics but also is applicable to the Gross-Pitaevskii equation and the Navier-Stokes-Fourier equation, showing that the finite minimum uncertainty between the position and the momentum is not an inherent property of quantum mechanics but a common feature of stochastic systems. We further discuss the possible implication of the present study in discussing the application of the hydrodynamic picture to microscopic systems, like relativistic heavy-ion collisions.

Climate change in the framework of external costs of energy systems

International Nuclear Information System (INIS)

Mayerhofer, P.

1994-01-01

Due to the continuing controversies concerning external costs of energy systems, the Commission of the European Union and the US-Department of Energy initiated a common project, External Costs of Fuel Cycles. The purpose of this project is to develop methodologies for the assessment of site-specific external costs on a marginal basis using a damage-function approach. For the assessment of climate change itself physical models are available. Here, the PC-model IMAGE developed by RIVM has been used for the time period up to 2100. Beyond this year, the carbon cycle is modeled with a response function. For the derivation of damage costs, a linear relationship between the global temperature change and the damage costs has been assumed. Thus, damage costs in the range of 0.10 to 0.53 US-cents/kWh for a discount rate of 3% are assessed for typical German fossil energy systems. The highest uncertainties are attached to the discount rate, the range for the climate sensitivity, and the forms of the global warming damage function. These points also have the strongest influence on the results. Hence, future research should be directed to the further analysis of these points. 30 refs., 5 figs., 5 tabs

Application of fuzzy system theory in addressing the presence of uncertainties

Energy Technology Data Exchange (ETDEWEB)

Yusmye, A. Y. N. [Institute of Engineering Mathematics, Universiti Malaysia Perlis Kampus Pauh Putra, 02600, Arau, Perlis (Malaysia); Goh, B. Y.; Adnan, N. F.; Ariffin, A. K. [Department of Mechanical and Materials, Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor (Malaysia)

2015-02-03

In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.

Application of fuzzy system theory in addressing the presence of uncertainties

International Nuclear Information System (INIS)

Yusmye, A. Y. N.; Goh, B. Y.; Adnan, N. F.; Ariffin, A. K.

2015-01-01

In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method

A statistical view of uncertainty in expert systems

International Nuclear Information System (INIS)

Spiegelhalter, D.J.

1986-01-01

The constructors of expert systems interpret ''uncertainty'' in a wide sense and have suggested a variety of qualitative and quantitative techniques for handling the concept, such as the theory of ''endorsements,'' fuzzy reasoning, and belief functions. After a brief selective review of procedures that do not adhere to the laws of probability, it is argued that a subjectivist Bayesian view of uncertainty, if flexibly applied, can provide many of the features demanded by expert systems. This claim is illustrated with a number of examples of probabilistic reasoning, and a connection drawn with statistical work on the graphical representation of multivariate distributions. Possible areas of future research are outlined

Uncertainty analysis of a nondestructive radioassay system for transuranic waste

International Nuclear Information System (INIS)

Harker, Y.D.; Blackwood, L.G.; Meachum, T.R.; Yoon, W.Y.

1996-01-01

Radioassay of transuranic waste in 207 liter drums currently stored at the Idaho National Engineering Laboratory is achieved using a Passive Active Neutron (PAN) nondestructive assay system. In order to meet data quality assurance requirements for shipping and eventual permanent storage of these drums at the Waste Isolation Pilot Plant in Carlsbad, New Mexico, the total uncertainty of the PAN system measurements must be assessed. In particular, the uncertainty calculations are required to include the effects of variations in waste matrix parameters and related variables on the final measurement results. Because of the complexities involved in introducing waste matrix parameter effects into the uncertainty calculations, standard methods of analysis (e.g., experimentation followed by propagation of errors) could not be implemented. Instead, a modified statistical sampling and verification approach was developed. In this modified approach the total performance of the PAN system is simulated using computer models of the assay system and the resultant output is compared with the known input to assess the total uncertainty. This paper describes the simulation process and illustrates its application to waste comprised of weapons grade plutonium-contaminated graphite molds

Planning low-carbon electricity systems under uncertainty considering operational flexibility and smart grid technologies.

Science.gov (United States)

Moreno, Rodrigo; Street, Alexandre; Arroyo, José M; Mancarella, Pierluigi

2017-08-13

Electricity grid operators and planners need to deal with both the rapidly increasing integration of renewables and an unprecedented level of uncertainty that originates from unknown generation outputs, changing commercial and regulatory frameworks aimed to foster low-carbon technologies, the evolving availability of market information on feasibility and costs of various technologies, etc. In this context, there is a significant risk of locking-in to inefficient investment planning solutions determined by current deterministic engineering practices that neither capture uncertainty nor represent the actual operation of the planned infrastructure under high penetration of renewables. We therefore present an alternative optimization framework to plan electricity grids that deals with uncertain scenarios and represents increased operational details. The presented framework is able to model the effects of an array of flexible, smart grid technologies that can efficiently displace the need for conventional solutions. We then argue, and demonstrate via the proposed framework and an illustrative example, that proper modelling of uncertainty and operational constraints in planning is key to valuing operationally flexible solutions leading to optimal investment in a smart grid context. Finally, we review the most used practices in power system planning under uncertainty, highlight the challenges of incorporating operational aspects and advocate the need for new and computationally effective optimization tools to properly value the benefits of flexible, smart grid solutions in planning. Such tools are essential to accelerate the development of a low-carbon energy system and investment in the most appropriate portfolio of renewable energy sources and complementary enabling smart technologies.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

Understanding uncertainty propagation in life cycle assessments of waste management systems

DEFF Research Database (Denmark)

Bisinella, Valentina; Conradsen, Knut; Christensen, Thomas Højlund

2015-01-01

Uncertainty analysis in Life Cycle Assessments (LCAs) of waste management systems often results obscure and complex, with key parameters rarely determined on a case-by-case basis. The paper shows an application of a simplified approach to uncertainty coupled with a Global Sensitivity Analysis (GSA......) perspective on three alternative waste management systems for Danish single-family household waste. The approach provides a fast and systematic method to select the most important parameters in the LCAs, understand their propagation and contribution to uncertainty....

LOFT experimental measurements uncertainty analyses. Volume XX. Fluid-velocity measurement using pulsed-neutron activation

International Nuclear Information System (INIS)

Lassahn, G.D.; Taylor, D.J.N.

1982-08-01

Analyses of uncertainty components inherent in pulsed-neutron-activation (PNA) measurements in general and the Loss-of-Fluid-Test (LOFT) system in particular are given. Due to the LOFT system's unique conditions, previously-used techniques were modified to make the volocity measurement. These methods render a useful, cost-effective measurement with an estimated uncertainty of 11% of reading

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

2010-09-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique

Calorimetric and reactor coolant system flow uncertainty

International Nuclear Information System (INIS)

Bates, L.; McLean, T.

1991-01-01

This paper describes a methodology for the quantification of errors associated with the determination of a feedwater flow, secondary power, and Reactor Coolant System (RCS) flow used at the Trojan Nuclear Plant to ensure compliance with regulatory requirements. The sources of error in Plant indications and process measurement are identified and tracked, using examples, through the mathematical processes necessary to calculate the uncertainty in the RCS flow measurement. An error of approximately 1.4 percent is calculated for secondary power. This error results, along with the consideration of other errors, in an uncertainty of approximately 3 percent in the RCS flow determination

Systems/cost summary

International Nuclear Information System (INIS)

Grand, P.; Danby, G.; Keane, J.; Spiro, J.; Sutter, D.; Cole, F.; Hoyer, E.; Freytag, K.; Burke, R.

1977-01-01

The purpose of the meeting was to discuss and develop cost-estimating methods for heavy-ion fusion accelerator systems. The group did not consider that its purpose was to make technical judgements on proposed systems, but to develop methods for making reasonable cost estimates of these systems. Such estimates will, it is hoped, provide material for systems studies, will help in guiding research and development efforts by identifying ''high-leverage'' subsystems (areas that account for a significant part of total system cost and that might be reduced in cost by further technical development) and to begin to provide data to aid in an eventual decision on the optimum type of accelerator for heavy-ion fusion

Modeling Multibody Systems with Uncertainties. Part I: Theoretical and Computational Aspects

International Nuclear Information System (INIS)

Sandu, Adrian; Sandu, Corina; Ahmadian, Mehdi

2006-01-01

This study explores the use of generalized polynomial chaos theory for modeling complex nonlinear multibody dynamic systems in the presence of parametric and external uncertainty. The polynomial chaos framework has been chosen because it offers an efficient computational approach for the large, nonlinear multibody models of engineering systems of interest, where the number of uncertain parameters is relatively small, while the magnitude of uncertainties can be very large (e.g., vehicle-soil interaction). The proposed methodology allows the quantification of uncertainty distributions in both time and frequency domains, and enables the simulations of multibody systems to produce results with 'error bars'. The first part of this study presents the theoretical and computational aspects of the polynomial chaos methodology. Both unconstrained and constrained formulations of multibody dynamics are considered. Direct stochastic collocation is proposed as less expensive alternative to the traditional Galerkin approach. It is established that stochastic collocation is equivalent to a stochastic response surface approach. We show that multi-dimensional basis functions are constructed as tensor products of one-dimensional basis functions and discuss the treatment of polynomial and trigonometric nonlinearities. Parametric uncertainties are modeled by finite-support probability densities. Stochastic forcings are discretized using truncated Karhunen-Loeve expansions. The companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part II: Numerical Applications' illustrates the use of the proposed methodology on a selected set of test problems. The overall conclusion is that despite its limitations, polynomial chaos is a powerful approach for the simulation of multibody systems with uncertainties

Development of Uncertainty Analysis Method for SMART Digital Core Protection and Monitoring System

International Nuclear Information System (INIS)

Koo, Bon Seung; In, Wang Kee; Hwang, Dae Hyun

2012-01-01

The Korea Atomic Energy Research Institute has developed a system-integrated modular advanced reactor (SMART) for a seawater desalination and electricity generation. Online digital core protection and monitoring systems, called SCOPS and SCOMS respectively were developed. SCOPS calculates minimum DNBR and maximum LPD based on the several online measured system parameters. SCOMS calculates the variables of limiting conditions for operation. KAERI developed overall uncertainty analysis methodology which is used statistically combining uncertainty components of SMART core protection and monitoring system. By applying overall uncertainty factors in on-line SCOPS/SCOMS calculation, calculated LPD and DNBR are conservative with a 95/95 probability/confidence level. In this paper, uncertainty analysis method is described for SMART core protection and monitoring system

Robust Performance of Systems with Structured Uncertainties in State Space

OpenAIRE

Zhou, K.; Khargonekar, P.P.; Stoustrup, Jakob; Niemann, H.H.

1995-01-01

This paper considers robust performance analysis and state feedback design for systems with time-varying parameter uncertainties. The notion of a strongly robust % performance criterion is introduced, and its applications in robust performance analysis and synthesis for nominally linear systems with time-varying uncertainties are discussed and compared with the constant scaled small gain criterion. It is shown that most robust performance analysis and synthesisproblems under this strongly rob...

Optimization under Uncertainty

KAUST Repository

Lopez, Rafael H.

2016-01-06

The goal of this poster is to present the main approaches to optimization of engineering systems in the presence of uncertainties. We begin by giving an insight about robust optimization. Next, we detail how to deal with probabilistic constraints in optimization, the so called the reliability based design. Subsequently, we present the risk optimization approach, which includes the expected costs of failure in the objective function. After that the basic description of each approach is given, the projects developed by CORE are presented. Finally, the main current topic of research of CORE is described.

Linear systems with unstructured multiplicative uncertainty: Modeling and robust stability analysis.

Directory of Open Access Journals (Sweden)

Radek Matušů

Full Text Available This article deals with continuous-time Linear Time-Invariant (LTI Single-Input Single-Output (SISO systems affected by unstructured multiplicative uncertainty. More specifically, its aim is to present an approach to the construction of uncertain models based on the appropriate selection of a nominal system and a weight function and to apply the fundamentals of robust stability investigation for considered sort of systems. The initial theoretical parts are followed by three extensive illustrative examples in which the first order time-delay, second order and third order plants with parametric uncertainty are modeled as systems with unstructured multiplicative uncertainty and subsequently, the robust stability of selected feedback loops containing constructed models and chosen controllers is analyzed and obtained results are discussed.

Role Of Environment Uncertainty On Management Information System-Literature Approach

Directory of Open Access Journals (Sweden)

Christine Dwi Karya Susilawati

2015-08-01

Full Text Available The purpose of this research is to find role of environmental uncertainty on the quality of management information system with the literature review. Environmental uncertainty is the inability of the individual to capture the environmental factors from outside the company who are not sure which affect the companies that will impact on the quality of management information system application when it lowered the individuals inability to capture the environmental factors from outside uncertain it will improve the quality of management information system application.

An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty

International Nuclear Information System (INIS)

Xie, Y.L.; Li, Y.P.; Huang, G.H.; Li, Y.F.

2010-01-01

In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a multi-stage context. Moreover, it can reflect dynamic decisions for facility-capacity expansion during the planning horizon. The developed model is applied to a case of planning GHG-emission mitigation, demonstrating that IFSP is applicable to reflecting complexities of multi-uncertainty, dynamic and interactive energy management systems, and capable of addressing the problem of GHG-emission reduction. A number of scenarios corresponding to different GHG-emission mitigation levels are examined; the results suggest that reasonable solutions have been generated. They can be used for generating plans for energy resource/electricity allocation and capacity expansion and help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.

Estimation of Uncertainty in Aerosol Concentration Measured by Aerosol Sampling System

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Chan; Song, Yong Jae; Jung, Woo Young; Lee, Hyun Chul; Kim, Gyu Tae; Lee, Doo Yong [FNC Technology Co., Yongin (Korea, Republic of)

2016-10-15

FNC Technology Co., Ltd has been developed test facilities for the aerosol generation, mixing, sampling and measurement under high pressure and high temperature conditions. The aerosol generation system is connected to the aerosol mixing system which injects SiO{sub 2}/ethanol mixture. In the sampling system, glass fiber membrane filter has been used to measure average mass concentration. Based on the experimental results using main carrier gas of steam and air mixture, the uncertainty estimation of the sampled aerosol concentration was performed by applying Gaussian error propagation law. FNC Technology Co., Ltd. has been developed the experimental facilities for the aerosol measurement under high pressure and high temperature. The purpose of the tests is to develop commercial test module for aerosol generation, mixing and sampling system applicable to environmental industry and safety related system in nuclear power plant. For the uncertainty calculation of aerosol concentration, the value of the sampled aerosol concentration is not measured directly, but must be calculated from other quantities. The uncertainty of the sampled aerosol concentration is a function of flow rates of air and steam, sampled mass, sampling time, condensed steam mass and its absolute errors. These variables propagate to the combination of variables in the function. Using operating parameters and its single errors from the aerosol test cases performed at FNC, the uncertainty of aerosol concentration evaluated by Gaussian error propagation law is less than 1%. The results of uncertainty estimation in the aerosol sampling system will be utilized for the system performance data.

Optimal operation method coping with uncertainty in multi-area small power systems

Directory of Open Access Journals (Sweden)

Shota Tobaru

2017-07-01

Full Text Available Japan contains a vast number of isolated islands. Majority of these islands are poweredby diesel generators (DGs, which are operationally not economical. Therefore, the introduction of renewableenergy systems (RESs into these area is very much vital. However, the variability of RESs asa result of weather condition as well as load demand , battery energy storage system (BESS is broughtinto play. Demand response (DR programs have also been so attractive in the energy management systemsfor the past decades. Among them, the real-time pricing (RTP has been one of the most effectivedemand response program being utilized. This program encourages the customer to increase or reducethe load consumption by varying the electricity price. Also, due to the increase in power transactionmarket, Japan electric power exchange (JEPX has established spot (day-ahead, intraday hour-ahead,and forward market programs. This paper utilizes day-ahead and hour-ahead markets, since these marketscan make it possible to deal with uncertainty related to generated power fluctuations. Therefore,this paper presents the optimal operation method coping with the uncertainties of RESs in multi-areasmall power systems. The proposed method enables flexibility to correspond to the forecasting error byproviding two kinds of power markets among multi-area small power systems and trading the shortageand surplus powers. Furthermore, it accomplishes a stable power supply and demand by RTP. Thus, theproposed method was able to reduce operational cost for multi-area small power systems. The processof creating operational plan for RTP, power trading at the markets and the unit commitment of DGs arealso presented in this paper. Simulation results corroborate the merit of the proposed program.

Cost estimating issues in the Russian integrated system planning context

International Nuclear Information System (INIS)

Allentuck, J.

1996-01-01

An important factor in the credibility of an optimal capacity expansion plan is the accuracy of cost estimates given the uncertainty of future economic conditions. This paper examines the problems associated with estimating investment and operating costs in the Russian nuclear power context over the period 1994 to 2010

Uncertainties in life cycle assessment of waste management systems

DEFF Research Database (Denmark)

Clavreul, Julie; Christensen, Thomas Højlund

2011-01-01

Life cycle assessment has been used to assess environmental performances of waste management systems in many studies. The uncertainties inherent to its results are often pointed out but not always quantified, which should be the case to ensure a good decisionmaking process. This paper proposes...... a method to assess all parameter uncertainties and quantify the overall uncertainty of the assessment. The method is exemplified in a case study, where the goal is to determine if anaerobic digestion of organic waste is more beneficial than incineration in Denmark, considering only the impact on global...... warming. The sensitivity analysis pointed out ten parameters particularly highly influencing the result of the study. In the uncertainty analysis, the distributions of these ten parameters were used in a Monte Carlo analysis, which concluded that incineration appeared more favourable than anaerobic...

Systems/cost: summary

International Nuclear Information System (INIS)

Grand, P.; Danby, G.; Keane, J.; Spiro, J.; Sutter, D.; Cole, F.; Hoyer, E.; Freytag, K.; Burke, R.

1978-01-01

The purpose of the meeting was to discuss and develop cost-estimating methods for heavy-ion fusion accelerator systems. The group did not consider that its purpose was to make technical judgments on proposed systems, but to develop methods for making reasonable cost estimates of these systems. Such estimates will, it is hoped, provide material for systems studies, will help in guiding research and development efforts by identifying high-leverage subsystems (areas that account for a significant part of total system cost and that might be reduced in cost by further technical development) and to begin to provide data to aid in an eventual decision on the optimum type of accelerator for heavy-ion fusion. The systems considered as examples are: (1) injection system; (2) Wideroe linac; (3) Alvarez linac; (4) induction linac; (5) superconducting accumulator ring; (6) synchrotron; (7) final rf bunching; and (8) final beam transport to target

The future of nuclear power in France: an analysis of the costs of phasing-out

International Nuclear Information System (INIS)

Malischek, Raimund; Trüby, Johannes

2016-01-01

Nuclear power is an important pillar in electricity generation in France. However, the French nuclear power plant fleet is ageing, and the possibility of reducing the technology's share in power generation or even a complete phase-out has been increasingly discussed. This paper focuses on three inter-related questions: First, what are the costs of phasing-out nuclear power in France? Second, who has to bear these costs, i.e., how much of the costs will be passed on to the rest of the European power system? And third, what effect does the uncertainty regarding future nuclear policy in France have on system costs? Applying a stochastic optimization model for the European electricity system, the analysis showed that additional system costs in France of a nuclear phase-out amount up to 76 billion €_2_0_1_0. Additional costs are mostly borne by the French power system. Surprisingly, the analysis found that the costs of uncertainty are rather limited. Based on the results, it can be concluded that a commitment regarding nuclear policy reform is only mildly beneficial in terms of system cost savings. - Highlights: • Analysis of different nuclear policy and phase-out scenarios in France. • Nuclear policy uncertainty in France is treated using stochastic programming. • Costs of a nuclear phase-out in France are significant, amounting up to 76 bill €. • Costs of a phase-out are hardly passed on to the rest of the European power system. • Costs of uncertainty are low, implying little benefit of nuclear policy commitment.

Total Measurement Uncertainty for the Plutonium Finishing Plant (PFP) Segmented Gamma Scan Assay System

CERN Document Server

Fazzari, D M

2001-01-01

This report presents the results of an evaluation of the Total Measurement Uncertainty (TMU) for the Canberra manufactured Segmented Gamma Scanner Assay System (SGSAS) as employed at the Hanford Plutonium Finishing Plant (PFP). In this document, TMU embodies the combined uncertainties due to all of the individual random and systematic sources of measurement uncertainty. It includes uncertainties arising from corrections and factors applied to the analysis of transuranic waste to compensate for inhomogeneities and interferences from the waste matrix and radioactive components. These include uncertainty components for any assumptions contained in the calibration of the system or computation of the data. Uncertainties are propagated at 1 sigma. The final total measurement uncertainty value is reported at the 95% confidence level. The SGSAS is a gamma assay system that is used to assay plutonium and uranium waste. The SGSAS system can be used in a stand-alone mode to perform the NDA characterization of a containe...

Estimation of Model Uncertainties in Closed-loop Systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2008-01-01

This paper describe a method for estimation of parameters or uncertainties in closed-loop systems. The method is based on an application of the dual YJBK (after Youla, Jabr, Bongiorno and Kucera) parameterization of all systems stabilized by a given controller. The dual YJBK transfer function...

Uncertainty analysis of time-dependent nonlinear systems: theory and application to transient thermal hydraulics

International Nuclear Information System (INIS)

Barhen, J.; Bjerke, M.A.; Cacuci, D.G.; Mullins, C.B.; Wagschal, G.G.

1982-01-01

An advanced methodology for performing systematic uncertainty analysis of time-dependent nonlinear systems is presented. This methodology includes a capability for reducing uncertainties in system parameters and responses by using Bayesian inference techniques to consistently combine prior knowledge with additional experimental information. The determination of best estimates for the system parameters, for the responses, and for their respective covariances is treated as a time-dependent constrained minimization problem. Three alternative formalisms for solving this problem are developed. The two ''off-line'' formalisms, with and without ''foresight'' characteristics, require the generation of a complete sensitivity data base prior to performing the uncertainty analysis. The ''online'' formalism, in which uncertainty analysis is performed interactively with the system analysis code, is best suited for treatment of large-scale highly nonlinear time-dependent problems. This methodology is applied to the uncertainty analysis of a transient upflow of a high pressure water heat transfer experiment. For comparison, an uncertainty analysis using sensitivities computed by standard response surface techniques is also performed. The results of the analysis indicate the following. Major reduction of the discrepancies in the calculation/experiment ratios is achieved by using the new methodology. Incorporation of in-bundle measurements in the uncertainty analysis significantly reduces system uncertainties. Accuracy of sensitivities generated by response-surface techniques should be carefully assessed prior to using them as a basis for uncertainty analyses of transient reactor safety problems

Effects of utility demand-side management programs on uncertainty

International Nuclear Information System (INIS)

Hirst, E.

1994-01-01

Electric utilities face a variety of uncertainties that complicate their long-term resource planning. These uncertainties include future economic and load growths, fuel prices, environmental and economic regulations, performance of existing power plants, cost and availability of purchased power, and the costs and performance of new demand and supply resources. As utilities increasingly turn to demand-side management (DSM) programs to provide resources, it becomes more important to analyze the interactions between these programs and the uncertainties facing utilities. This paper uses a dynamic planning model to quantify the uncertainty effects of supply-only vs DSM + supply resource portfolios. The analysis considers four sets of uncertainties: economic growth, fuel prices, the costs to build new power plants, and the costs to operate DSM programs. The two types of portfolios are tested against these four sets of uncertainties for the period 1990 to 2010. Sensitivity, scenario, and worst-case analysis methods are used. The sensitivity analyses show that the DSM + supply resource portfolio is less sensitive to unanticipated changes in economic growth, fuel prices, and power-plant construction costs than is the supply-only portfolio. The supply-only resource mix is better only with respect to uncertainties about the costs of DSM programs. The base-case analysis shows that including DSM programs in the utility's resource portfolio reduces the net present value of revenue requirements (NPV-RR) by 490 million dollars. The scenario-analysis results show an additional 30 million dollars (6%) in benefits associated with reduction in these uncertainties. In the worst-case analysis, the DSM + supply portfolio again reduces the cost penalty associated with guessing wrong for both cases, when the utility plans for high needs and learns it has low needs and vice versa. 20 refs

Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors

International Nuclear Information System (INIS)

Kim, Seunghyok; Koo, Jamin; Lee, Chang Jun; Yoon, En Sup

2012-01-01

During the last few decades, energy planning has focused on meeting domestic demand at lower total costs. However, global warming and the shared recognition of it have transformed the problem of energy planning into a more complex task with a greater number of issues to be considered. Since the key issue is to reduce greenhouse effects, governments around the world have begun to make investments in renewable energy systems (e.g., hydro, wind, solar, and/or biomass power). The relatively high costs of renewable energy systems and the uncertain outlook of their rate of diffusion in the market make it difficult to heavily rely on them. The uncertain variations in production cost over time are especially challenging. To handle uncertainties, the concept of the learning rate was adopted in this study so as to compute the costs of energy systems in the future and Monte Carlo simulation was performed. The aim of this study was to optimize plans of conventional and prospective renewable energy systems with respect to production cost. The production cost included capital, fixed, variable, and external costs. For the case study, the energy situation in South Korea was used. The results of the case study where the proposed methodology was applied could provide useful insights economically and strategies of sustainable energy management for ambiguous environments. -- Highlights: ► We propose energy planning method for sustainability. ► We consider uncertainties such as learning rate, fuel prices, and CO 2 prices. ► We consider the possibility of CO 2 trading. ► The proposed method is applied to South Korea case. ► The added capacities of energy systems depend on uncertainties.

Robust Guaranteed Cost Observer Design for Singular Markovian Jump Time-Delay Systems with Generally Incomplete Transition Probability

Directory of Open Access Journals (Sweden)

Yanbo Li

2014-01-01

Full Text Available This paper is devoted to the investigation of the design of robust guaranteed cost observer for a class of linear singular Markovian jump time-delay systems with generally incomplete transition probability. In this singular model, each transition rate can be completely unknown or only its estimate value is known. Based on stability theory of stochastic differential equations and linear matrix inequality (LMI technique, we design an observer to ensure that, for all uncertainties, the resulting augmented system is regular, impulse free, and robust stochastically stable with the proposed guaranteed cost performance. Finally, a convex optimization problem with LMI constraints is formulated to design the suboptimal guaranteed cost filters for linear singular Markovian jump time-delay systems with generally incomplete transition probability.

Iso-uncertainty control in an experimental fluoroscopy system

International Nuclear Information System (INIS)

Siddique, S.; Fiume, E.; Jaffray, D. A.

2014-01-01

Purpose: X-ray fluoroscopy remains an important imaging modality in a number of image-guided procedures due to its real-time nature and excellent spatial detail. However, the radiation dose delivered raises concerns about its use particularly in lengthy treatment procedures (>0.5 h). The authors have previously presented an algorithm that employs feedback of geometric uncertainty to control dose while maintaining a desired targeting uncertainty during fluoroscopic tracking of fiducials. The method was tested using simulations of motion against controlled noise fields. In this paper, the authors embody the previously reported method in a physical prototype and present changes to the controller required to function in a practical setting. Methods: The metric for feedback used in this study is based on the trace of the covariance of the state of the system, tr(C). The state is defined here as the 2D location of a fiducial on a plane parallel to the detector. A relationship between this metric and the tube current is first developed empirically. This relationship is extended to create a manifold that incorporates a latent variable representing the estimated background attenuation. The manifold is then used within the controller to dynamically adjust the tube current and maintain a specified targeting uncertainty. To evaluate the performance of the proposed method, an acrylic sphere (1.6 mm in diameter) was tracked at tube currents ranging from 0.5 to 0.9 mA (0.033 s) at a fixed energy of 80 kVp. The images were acquired on a Varian Paxscan 4030A (2048 × 1536 pixels, ∼100 cm source-to-axis distance, ∼160 cm source-to-detector distance). The sphere was tracked using a particle filter under two background conditions: (1) uniform sheets of acrylic and (2) an acrylic wedge. The measured tr(C) was used in conjunction with a learned manifold to modulate the tube current in order to maintain a specified uncertainty as the sphere traversed regions of varying thickness

Mechanics and uncertainty

CERN Document Server

Lemaire, Maurice

2014-01-01

Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.

Reliability assessment of complex electromechanical systems under epistemic uncertainty

International Nuclear Information System (INIS)

Mi, Jinhua; Li, Yan-Feng; Yang, Yuan-Jian; Peng, Weiwen; Huang, Hong-Zhong

2016-01-01

The appearance of macro-engineering and mega-project have led to the increasing complexity of modern electromechanical systems (EMSs). The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems. Uncertainty, dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments, lack of data and random interference. This paper presents a comprehensive study on the reliability assessment of complex systems. In view of the dynamic characteristics within the system, it makes use of the advantages of the dynamic fault tree (DFT) for characterizing system behaviors. The lifetime of system units can be expressed as bounded closed intervals by incorporating field failures, test data and design expertize. Then the coefficient of variation (COV) method is employed to estimate the parameters of life distributions. An extended probability-box (P-Box) is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data. By mapping the DFT into an equivalent Bayesian network (BN), relevant reliability parameters and indexes have been calculated. Furthermore, the Monte Carlo (MC) simulation method is utilized to compute the DFT model with consideration of system replacement policy. The results show that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems. - Highlights: • A comprehensive study on the reliability assessment of complex system is presented. • An extended probability-box is proposed to convey the present of epistemic uncertainty. • The dynamic fault tree model is built. • Bayesian network and Monte Carlo simulation methods are used. • The reliability assessment of a complex electromechanical system is performed.

Reducing prediction uncertainty of weather controlled systems

NARCIS (Netherlands)

Doeswijk, T.G.

2007-01-01

In closed agricultural systems the weather acts both as a disturbance and as a resource. By using weather forecasts in control strategies the effects of disturbances can be minimized whereas the resources can be utilized. In this situation weather forecast uncertainty and model based control are

Changes in cost system design and intensity of use in times of crisis: Evidence from Dutch local government

NARCIS (Netherlands)

Schoute, M.; Budding, G.T.

2017-01-01

Purpose: This study examines whether changes in environmental and funding uncertainty during the first three years after the outbreak of the global financial crisis (which we presume to have increased significantly) are associated with changes in cost system design and intensity of use.

Numerical Continuation Methods for Intrusive Uncertainty Quantification Studies

Energy Technology Data Exchange (ETDEWEB)

Safta, Cosmin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Najm, Habib N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Phipps, Eric Todd [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-09-01

Rigorous modeling of engineering systems relies on efficient propagation of uncertainty from input parameters to model outputs. In recent years, there has been substantial development of probabilistic polynomial chaos (PC) Uncertainty Quantification (UQ) methods, enabling studies in expensive computational models. One approach, termed ”intrusive”, involving reformulation of the governing equations, has been found to have superior computational performance compared to non-intrusive sampling-based methods in relevant large-scale problems, particularly in the context of emerging architectures. However, the utility of intrusive methods has been severely limited due to detrimental numerical instabilities associated with strong nonlinear physics. Previous methods for stabilizing these constructions tend to add unacceptably high computational costs, particularly in problems with many uncertain parameters. In order to address these challenges, we propose to adapt and improve numerical continuation methods for the robust time integration of intrusive PC system dynamics. We propose adaptive methods, starting with a small uncertainty for which the model has stable behavior and gradually moving to larger uncertainty where the instabilities are rampant, in a manner that provides a suitable solution.

Uncertainty analysis methods for estimation of reliability of passive system of VHTR

International Nuclear Information System (INIS)

Han, S.J.

2012-01-01

An estimation of reliability of passive system for the probabilistic safety assessment (PSA) of a very high temperature reactor (VHTR) is under development in Korea. The essential approach of this estimation is to measure the uncertainty of the system performance under a specific accident condition. The uncertainty propagation approach according to the simulation of phenomenological models (computer codes) is adopted as a typical method to estimate the uncertainty for this purpose. This presentation introduced the uncertainty propagation and discussed the related issues focusing on the propagation object and its surrogates. To achieve a sufficient level of depth of uncertainty results, the applicability of the propagation should be carefully reviewed. For an example study, Latin-hypercube sampling (LHS) method as a direct propagation was tested for a specific accident sequence of VHTR. The reactor cavity cooling system (RCCS) developed by KAERI was considered for this example study. This is an air-cooled type passive system that has no active components for its operation. The accident sequence is a low pressure conduction cooling (LPCC) accident that is considered as a design basis accident for the safety design of VHTR. This sequence is due to a large failure of the pressure boundary of the reactor system such as a guillotine break of coolant pipe lines. The presentation discussed the obtained insights (benefit and weakness) to apply an estimation of reliability of passive system

Uncertainty analysis technique of dynamic response and cumulative damage properties of piping system

International Nuclear Information System (INIS)

Suzuki, Kohei; Aoki, Shigeru; Hara, Fumio; Hanaoka, Masaaki; Yamashita, Tadashi.

1982-01-01

It is a technologically important subject to establish the method of uncertainty analysis statistically examining the variation of the earthquake response and damage properties of equipment and piping system due to the change of input load and the parameters of structural system, for evaluating the aseismatic capability and dynamic structural reliability of these systems. The uncertainty in the response and damage properties when equipment and piping system are subjected to excessive vibration load is mainly dependent on the irregularity of acting input load such as the unsteady vibration of earthquakes, and structural uncertainty in forms and dimensions. This study is the basic one to establish the method for evaluating the uncertainty in the cumulative damage property at the time of resonant vibration of piping system due to the disperse of structural parameters with a simple model. First, the piping models with simple form were broken by resonant vibration, and the uncertainty in the cumulative damage property was evaluated. Next, the response analysis using an elasto-plastic mechanics model was performed by numerical simulation. Finally, the method of uncertainty analysis for response and damage properties by the perturbation method utilizing equivalent linearization was proposed, and its propriety was proved. (Kako, I.)

Critical mid-term uncertainties in long-term decarbonisation pathways

International Nuclear Information System (INIS)

Usher, Will; Strachan, Neil

2012-01-01

Over the next decade, large energy investments are required in the UK to meet growing energy service demands and legally binding emission targets under a pioneering policy agenda. These are necessary despite deep mid-term (2025–2030) uncertainties over which national policy makers have little control. We investigate the effect of two critical mid-term uncertainties on optimal near-term investment decisions using a two-stage stochastic energy system model. The results show that where future fossil fuel prices are uncertain: (i) the near term hedging strategy to 2030 differs from any one deterministic fuel price scenario and is structurally dissimilar to a simple ‘average’ of the deterministic scenarios, and (ii) multiple recourse strategies from 2030 are perturbed by path dependencies caused by hedging investments. Evaluating the uncertainty under a decarbonisation agenda shows that fossil fuel price uncertainty is very expensive at around £20 billion. The addition of novel mitigation options reduces the value of fossil fuel price uncertainty to £11 billion. Uncertain biomass import availability shows a much lower value of uncertainty at £300 million. This paper reveals the complex relationship between the flexibility of the energy system and mitigating the costs of uncertainty due to the path-dependencies caused by the long-life times of both infrastructures and generation technologies. - Highlights: ► Critical mid-term uncertainties affect near-term investments in UK energy system. ► Deterministic scenarios give conflicting near-term actions. ► Stochastic scenarios give one near-term hedging strategy. ► Technologies exhibit path dependency or flexibility. ► Fossil fuel price uncertainty is very expensive, biomass availability uncertainty is not.

Starling flock networks manage uncertainty in consensus at low cost.

Directory of Open Access Journals (Sweden)

George F Young

Full Text Available Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors. We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks.

Starling Flock Networks Manage Uncertainty in Consensus at Low Cost

Science.gov (United States)

Young, George F.; Scardovi, Luca; Cavagna, Andrea; Giardina, Irene; Leonard, Naomi E.

2013-01-01

Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors). We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks. PMID:23382667

(Value Stream Costing As A New Costing System)

OpenAIRE

Karcıoğlu, Reşar; Nuray, Meral

2010-01-01

In recent years, the number of lean company which use lean manufacturing system is rising. This companies use the standard costing while appropriate for traditional bathch manufacturing. But standard costing system fails to support the goals of lean manufacturing system. A different method of costing based upon the characteristics of the value stream is needed to fulfill the needs of the lean company. This systemis Value Stream Costing. When a lean company moves to value stream management, th...

Adaptive robust fault-tolerant control for linear MIMO systems with unmatched uncertainties

Science.gov (United States)

Zhang, Kangkang; Jiang, Bin; Yan, Xing-Gang; Mao, Zehui

2017-10-01

In this paper, two novel fault-tolerant control design approaches are proposed for linear MIMO systems with actuator additive faults, multiplicative faults and unmatched uncertainties. For time-varying multiplicative and additive faults, new adaptive laws and additive compensation functions are proposed. A set of conditions is developed such that the unmatched uncertainties are compensated by actuators in control. On the other hand, for unmatched uncertainties with their projection in unmatched space being not zero, based on a (vector) relative degree condition, additive functions are designed to compensate for the uncertainties from output channels in the presence of actuator faults. The developed fault-tolerant control schemes are applied to two aircraft systems to demonstrate the efficiency of the proposed approaches.

Probabilistic costing of transmission services

International Nuclear Information System (INIS)

Wijayatunga, P.D.C.

1992-01-01

Costing of transmission services of electrical utilities is required for transactions involving the transport of energy over a power network. The calculation of these costs based on Short Run Marginal Costing (SRMC) is preferred over other methods proposed in the literature due to its economic efficiency. In the research work discussed here, the concept of probabilistic costing of use-of-system based on SRMC which emerges as a consequence of the uncertainties in a power system is introduced using two different approaches. The first approach, based on the Monte Carlo method, generates a large number of possible system states by simulating random variables in the system using pseudo random number generators. A second approach to probabilistic use-of-system costing is proposed based on numerical convolution and multi-area representation of the transmission network. (UK)

Users manual for the FORSS sensitivity and uncertainty analysis code system

International Nuclear Information System (INIS)

Lucius, J.L.; Weisbin, C.R.; Marable, J.H.; Drischler, J.D.; Wright, R.Q.; White, J.E.

1981-01-01

FORSS is a code system used to study relationships between nuclear reaction cross sections, integral experiments, reactor performance parameter predictions and associated uncertainties. This report describes the computing environment and the modules currently used to implement FORSS Sensitivity and Uncertainty Methodology

Users manual for the FORSS sensitivity and uncertainty analysis code system

Energy Technology Data Exchange (ETDEWEB)

Lucius, J.L.; Weisbin, C.R.; Marable, J.H.; Drischler, J.D.; Wright, R.Q.; White, J.E.

1981-01-01

FORSS is a code system used to study relationships between nuclear reaction cross sections, integral experiments, reactor performance parameter predictions and associated uncertainties. This report describes the computing environment and the modules currently used to implement FORSS Sensitivity and Uncertainty Methodology.

Robust Performance of Systems with Structured Uncertainties in State Space

DEFF Research Database (Denmark)

Zhou, Kemin; Khargonekar, Pramod P.; Stoustrup, Jakob

1995-01-01

This paper considers robust performance analysis and state feedback design for systems with time-varying parameter uncertainties. The notion of a strongly robust % performance criterion is introduced, and its applications in robust performance analysis and synthesis for nominally linear systems...... with time-varying uncertainties are discussed and compared with the constant scaled small gain criterion. It is shown that most robust performance analysis and synthesis problems under this strongly robust % performance criterion can be transformed into linear matrix inequality problems, and can be solved...

An Economic Assessment of Local Farm Multi-Purpose Surface Water Retention Systems under Future Climate Uncertainty

Directory of Open Access Journals (Sweden)

Pamela Berry

2017-03-01

Full Text Available Regions dependent on agricultural production are concerned about the uncertainty associated with climate change. Extreme drought and flooding events are predicted to occur with greater frequency, requiring mitigation strategies to reduce their negative impacts. Multi-purpose local farm water retention systems can reduce water stress during drought periods by supporting irrigation. The retention systems’ capture of excess spring runoff and extreme rainfall events also reduces flood potential downstream. Retention systems may also be used for biomass production and nutrient retention. A sub-watershed scale retention system was analysed using a dynamic simulation model to predict the economic advantages in the future. Irrigated crops using water from the downstream reservoir at Pelly’s Lake, Manitoba, Canada, experienced a net decrease in gross margin in the future due to the associated irrigation and reservoir infrastructure costs. However, the multi-purpose benefits of the retention system at Pelly’s Lake of avoided flood damages, nutrient retention, carbon sequestration, and biomass production provide an economic benefit of $25,507.00/hectare of retention system/year. Multi-purpose retention systems under future climate uncertainty provide economic and environmental gains when used to avoid flood damages, for nutrient retention and carbon sequestration, and biomass production. The revenue gained from these functions can support farmers willing to invest in irrigation while providing economic and environmental benefits to the region.

Nuclear Data Uncertainties in 2004: A Perspective

International Nuclear Information System (INIS)

Smith, Donald L.

2005-01-01

Interest in nuclear data uncertainties is growing robustly after having languished for several years. Renewed attention to this topic is being motivated by the practical need for assuring that nuclear systems will be safe, reliable, and cost effective, according to the individual requirements of each specific nuclear technology. Furthermore, applications are emerging in certain areas of basic nuclear science, e.g., in astrophysics, where, until recently, attention has focused mainly on understanding basic concepts and physics principles rather than on dealing with detailed quantitative information. The availability of fast computers and the concurrent development of sophisticated software enable nuclear data uncertainty information to be used more effectively than ever before. For example, data uncertainties and associated methodologies play useful roles in advanced data measurement, analysis, and evaluation procedures. Unfortunately, the current inventory of requisite uncertainty information is rather limited when measured against these evolving demands. Consequently, there is a real need to generate more comprehensive and reasonable nuclear data uncertainty information, and to make this available relatively soon in suitable form for use in the computer codes employed for nuclear analyses and the development of advanced nuclear energy systems. This conference contribution discusses several conceptual and technical issues that need to be addressed in meeting this demand during the next few years. The role of data uncertainties in several areas of nuclear science will also be mentioned briefly. Finally, the opportunities that ultimately will be afforded by the availability of more extensive and reasonable uncertainty information, and some technical challenges to master, will also be explored in this paper

Monte Carlo approaches for uncertainty quantification of criticality for system dimensions

International Nuclear Information System (INIS)

Kiedrowski, B.C.; Brown, F.B.

2013-01-01

One of the current challenges in nuclear engineering computations is the issue of performing uncertainty analysis for either calculations or experimental measurements. This paper specifically focuses on the issue of estimating the uncertainties arising from geometric tolerances. For this paper, two techniques for uncertainty quantification are studied. The first is the forward propagation technique, which can be thought of as a 'brute force' approach; uncertain system parameters are randomly sampled, the calculation is run, and uncertainties are found from the empirically obtained distribution of results. This approach need make no approximations in principle, but is very computationally expensive. The other approach investigated is the adjoint-based approach; system sensitivities are computed via a single Monte Carlo calculation and those are used with a covariance matrix to provide a linear estimate of the uncertainty. Demonstration calculations are performed with the MCNP6 code for both techniques. The 2 techniques are tested on 2 cases: the first case is a solid, bare cylinder of Pu-metal while the second case is a can of plutonium nitrate solution. The results show that the forward and adjoint approaches appear to agree in some cases where the responses are not non-linearly correlated. In other cases, the uncertainties in the effective multiplication k disagree for reasons not yet known

Uncertainty in techno-economic estimates of cellulosic ethanol production due to experimental measurement uncertainty

Directory of Open Access Journals (Sweden)

Vicari Kristin J

2012-04-01

Full Text Available Abstract Background Cost-effective production of lignocellulosic biofuels remains a major financial and technical challenge at the industrial scale. A critical tool in biofuels process development is the techno-economic (TE model, which calculates biofuel production costs using a process model and an economic model. The process model solves mass and energy balances for each unit, and the economic model estimates capital and operating costs from the process model based on economic assumptions. The process model inputs include experimental data on the feedstock composition and intermediate product yields for each unit. These experimental yield data are calculated from primary measurements. Uncertainty in these primary measurements is propagated to the calculated yields, to the process model, and ultimately to the economic model. Thus, outputs of the TE model have a minimum uncertainty associated with the uncertainty in the primary measurements. Results We calculate the uncertainty in the Minimum Ethanol Selling Price (MESP estimate for lignocellulosic ethanol production via a biochemical conversion process: dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis and co-fermentation of the resulting sugars to ethanol. We perform a sensitivity analysis on the TE model and identify the feedstock composition and conversion yields from three unit operations (xylose from pretreatment, glucose from enzymatic hydrolysis, and ethanol from fermentation as the most important variables. The uncertainty in the pretreatment xylose yield arises from multiple measurements, whereas the glucose and ethanol yields from enzymatic hydrolysis and fermentation, respectively, are dominated by a single measurement: the fraction of insoluble solids (fIS in the biomass slurries. Conclusions We calculate a $0.15/gal uncertainty in MESP from the TE model due to uncertainties in primary measurements. This result sets a lower bound on the error bars of

Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

Science.gov (United States)

Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

2016-12-01

Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate

Wind power forecasting accuracy and uncertainty in Finland

Energy Technology Data Exchange (ETDEWEB)

Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

2013-04-15

Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

Fuzzy Activity Based Life Cycle Costing For Repairable Equipment

Directory of Open Access Journals (Sweden)

Mulubrhan Freselam

2016-01-01

Full Text Available Life-cycle cost (LCC is the much known method used for decision making that considers all costs in the life of a system or equipment. Predicting LCCs is fraught with potential errors, owing to the uncertainty in future events, future costs, interest rates, and even hidden costs. These uncertainties have a direct impact on the decision making. Activity based LCC is used to identify the activities and cost drivers in acquisition, operation and maintenance phase. This activity based LCC is integrated with fuzzy set theory and interval mathematics to model these uncertainties. Day–Stout–Warren (DSW algorithm and the vertex method are then used to evaluate competing alternatives. A case of two pumps (Pump A and Pump B are taken and their LCC is analysed using the developed model. The equivalent annual cost of Pump B is greater than Pump A, which leads the decision maker to choose Pump A over Pump B.

Implementation of a Cost-Accounting System for Visibility of Weapon Systems Life-Cycle Costs

National Research Council Canada - National Science Library

Ugone, Mary

2001-01-01

... costs through activity-based costing and management. The system must deliver timely, integrated data for management purposes to permit understanding of total weapon costs, provide a basis for estimating costs of future systems, and feed other tools for life-cycle cost management.

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter

Multifidelity, Multidisciplinary Design Under Uncertainty with Non-Intrusive Polynomial Chaos

Science.gov (United States)

West, Thomas K., IV; Gumbert, Clyde

2017-01-01

The primary objective of this work is to develop an approach for multifidelity uncertainty quantification and to lay the framework for future design under uncertainty efforts. In this study, multifidelity is used to describe both the fidelity of the modeling of the physical systems, as well as the difference in the uncertainty in each of the models. For computational efficiency, a multifidelity surrogate modeling approach based on non-intrusive polynomial chaos using the point-collocation technique is developed for the treatment of both multifidelity modeling and multifidelity uncertainty modeling. Two stochastic model problems are used to demonstrate the developed methodologies: a transonic airfoil model and multidisciplinary aircraft analysis model. The results of both showed the multifidelity modeling approach was able to predict the output uncertainty predicted by the high-fidelity model as a significant reduction in computational cost.

Typology of Uncertainties in the Development Process of Product-Service Systems

DEFF Research Database (Denmark)

Ramirez Hernandez, Tabea; Kreye, Melanie; Pigosso, Daniela Cristina Antelmi

This paper investigates uncertainty in the development of Product-Service Systems (PSS) – a complex combination of product and services. This research is important because practitioners struggle with managing the high uncertainties arising from the complexity of parallel product and service...... development in compound clusters of stakeholders. Yet, scholars have not analyzed these challenges extensively. Based on a combination of innovation management and servitization literature a conceptual framework is offered, detailing five uncertainty types relevant for PSS-development: environmental...

Risk classification and uncertainty propagation for virtual water distribution systems

International Nuclear Information System (INIS)

Torres, Jacob M.; Brumbelow, Kelly; Guikema, Seth D.

2009-01-01

While the secrecy of real water distribution system data is crucial, it poses difficulty for research as results cannot be publicized. This data includes topological layouts of pipe networks, pump operation schedules, and water demands. Therefore, a library of virtual water distribution systems can be an important research tool for comparative development of analytical methods. A virtual city, 'Micropolis', has been developed, including a comprehensive water distribution system, as a first entry into such a library. This virtual city of 5000 residents is fully described in both geographic information systems (GIS) and EPANet hydraulic model frameworks. A risk classification scheme and Monte Carlo analysis are employed for an attempted water supply contamination attack. Model inputs to be considered include uncertainties in: daily water demand, seasonal demand, initial storage tank levels, the time of day a contamination event is initiated, duration of contamination event, and contaminant quantity. Findings show that reasonable uncertainties in model inputs produce high variability in exposure levels. It is also shown that exposure level distributions experience noticeable sensitivities to population clusters within the contaminant spread area. High uncertainties in exposure patterns lead to greater resources needed for more effective mitigation strategies.

Model Uncertainty for Bilinear Hysteretic Systems

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1984-01-01

. The statistical uncertainty -due to lack of information can e.g. be taken into account by describing the variables by predictive density functions, Veneziano [2). In general, model uncertainty is the uncertainty connected with mathematical modelling of the physical reality. When structural reliability analysis...... is related to the concept of a failure surface (or limit state surface) in the n-dimensional basic variable space then model uncertainty is at least due to the neglected variables, the modelling of the failure surface and the computational technique used. A more precise definition is given in section 2...

A genetic-algorithm-aided stochastic optimization model for regional air quality management under uncertainty.

Science.gov (United States)

Qin, Xiaosheng; Huang, Guohe; Liu, Lei

2010-01-01

A genetic-algorithm-aided stochastic optimization (GASO) model was developed in this study for supporting regional air quality management under uncertainty. The model incorporated genetic algorithm (GA) and Monte Carlo simulation techniques into a general stochastic chance-constrained programming (CCP) framework and allowed uncertainties in simulation and optimization model parameters to be considered explicitly in the design of least-cost strategies. GA was used to seek the optimal solution of the management model by progressively evaluating the performances of individual solutions. Monte Carlo simulation was used to check the feasibility of each solution. A management problem in terms of regional air pollution control was studied to demonstrate the applicability of the proposed method. Results of the case study indicated the proposed model could effectively communicate uncertainties into the optimization process and generate solutions that contained a spectrum of potential air pollutant treatment options with risk and cost information. Decision alternatives could be obtained by analyzing tradeoffs between the overall pollutant treatment cost and the system-failure risk due to inherent uncertainties.

Uncertainty, loss aversion, and markets for energy efficiency

International Nuclear Information System (INIS)

Greene, David L.

2011-01-01

Increasing energy efficiency is critical to mitigating greenhouse gas emissions from fossil-fuel combustion, reducing oil dependence, and achieving a sustainable global energy system. The tendency of markets to neglect apparently cost-effective energy efficiency options has been called the 'efficiency gap' or 'energy paradox.' The market for energy efficiency in new, energy-using durable goods, however, appears to have a bias that leads to undervaluation of future energy savings relative to their expected value. This paper argues that the bias is chiefly produced by the combination of substantial uncertainty about the net value of future fuel savings and the loss aversion of typical consumers. This framework relies on the theory of context-dependent preferences. The uncertainty-loss aversion bias against energy efficiency is quantifiable, making it potentially correctible by policy measures. The welfare economics of such policies remains unresolved. Data on the costs of increased fuel economy of new passenger cars, taken from a National Research Council study, illustrate how an apparently cost-effective increase in energy efficiency would be uninteresting to loss-averse consumers.

Integrating uncertainty into public energy research and development decisions

Science.gov (United States)

Anadón, Laura Díaz; Baker, Erin; Bosetti, Valentina

2017-05-01

Public energy research and development (R&D) is recognized as a key policy tool for transforming the world's energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the integration of technological uncertainty into energy policy: expert elicitations, integrated assessment models, and decision frameworks. Here we review efforts to incorporate all three approaches to facilitate public energy R&D decision-making under uncertainty. We highlight emerging insights that are robust across elicitations, models, and frameworks, relating to the allocation of public R&D investments, and identify gaps and challenges that remain.

Electricity system expansion studies to consider uncertainties and interactions in restructured markets

Science.gov (United States)

Jin, Shan

This dissertation concerns power system expansion planning under different market mechanisms. The thesis follows a three paper format, in which each paper emphasizes a different perspective. The first paper investigates the impact of market uncertainties on a long term centralized generation expansion planning problem. The problem is modeled as a two-stage stochastic program with uncertain fuel prices and demands, which are represented as probabilistic scenario paths in a multi-period tree. Two measurements, expected cost (EC) and Conditional Value-at-Risk (CVaR), are used to minimize, respectively, the total expected cost among scenarios and the risk of incurring high costs in unfavorable scenarios. We sample paths from the scenario tree to reduce the problem scale and determine the sufficient number of scenarios by computing confidence intervals on the objective values. The second paper studies an integrated electricity supply system including generation, transmission and fuel transportation with a restructured wholesale electricity market. This integrated system expansion problem is modeled as a bi-level program in which a centralized system expansion decision is made in the upper level and the operational decisions of multiple market participants are made in the lower level. The difficulty of solving a bi-level programming problem to global optimality is discussed and three problem relaxations obtained by reformulation are explored. The third paper solves a more realistic market-based generation and transmission expansion problem. It focuses on interactions among a centralized transmission expansion decision and decentralized generation expansion decisions. It allows each generator to make its own strategic investment and operational decisions both in response to a transmission expansion decision and in anticipation of a market price settled by an Independent System Operator (ISO) market clearing problem. The model poses a complicated tri-level structure

Optimization Under Uncertainty of Site-Specific Turbine Configurations

Science.gov (United States)

Quick, J.; Dykes, K.; Graf, P.; Zahle, F.

2016-09-01

Uncertainty affects many aspects of wind energy plant performance and cost. In this study, we explore opportunities for site-specific turbine configuration optimization that accounts for uncertainty in the wind resource. As a demonstration, a simple empirical model for wind plant cost of energy is used in an optimization under uncertainty to examine how different risk appetites affect the optimal selection of a turbine configuration for sites of different wind resource profiles. If there is unusually high uncertainty in the site wind resource, the optimal turbine configuration diverges from the deterministic case and a generally more conservative design is obtained with increasing risk aversion on the part of the designer.

Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty

CERN Document Server

Starczewski, Janusz T

2013-01-01

This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory.            In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...

Plug and Play Robust Distributed Control with Ellipsoidal Parametric Uncertainty System

Directory of Open Access Journals (Sweden)

Hong Wang-jian

2016-01-01

Full Text Available We consider a continuous linear time invariant system with ellipsoidal parametric uncertainty structured into subsystems. Since the design of a local controller uses only information on a subsystem and its neighbours, we combine the plug and play idea and robust distributed control to propose one distributed control strategy for linear system with ellipsoidal parametric uncertainty. Firstly for linear system with ellipsoidal parametric uncertainty, a necessary and sufficient condition for robust state feedback control is proposed by means of linear matrix inequality. If this necessary and sufficient condition is satisfied, this robust state feedback gain matrix can be easily derived to guarantee robust stability and prescribed closed loop performance. Secondly the plug and play idea is introduced in the design process. Finally by one example of aircraft flutter model parameter identification, the efficiency of the proposed control strategy can be easily realized.

Climate policy uncertainty and investment risk

Energy Technology Data Exchange (ETDEWEB)

NONE

2007-06-21

Our climate is changing. This is certain. Less certain, however, is the timing and magnitude of climate change, and the cost of transition to a low-carbon world. Therefore, many policies and programmes are still at a formative stage, and policy uncertainty is very high. This book identifies how climate change policy uncertainty may affect investment behaviour in the power sector. For power companies, where capital stock is intensive and long-lived, those risks rank among the biggest and can create an incentive to delay investment. Our analysis results show that the risk premiums of climate change uncertainty can add 40% of construction costs of the plant for power investors, and 10% of price surcharges for the electricity end-users. This publication tells what can be done in policy design to reduce these costs. Incorporating the results of quantitative analysis, this publication also shows the sensitivity of different power sector investment decisions to different risks. It compares the effects of climate policy uncertainty with energy market uncertainty, showing the relative importance of these sources of risk for different technologies in different market types. Drawing on extensive consultation with power companies and financial investors, it also assesses the implications for policy makers, allowing the key messages to be transferred into policy designs. This book is a useful tool for governments to improve climate policy mechanisms and create more certainty for power investors.

Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security

International Nuclear Information System (INIS)

Pfenninger, Stefan; Keirstead, James

2015-01-01

Highlights: • We compare a large number of cost-optimal future power systems for Great Britain. • Scenarios are assessed on cost, emissions reductions, and energy security. • Up to 60% of variable renewable capacity is possible with little cost increase. • Higher shares require storage, imports or dispatchable renewables such as tidal range. - Abstract: Mitigating climate change is driving the need to decarbonize the electricity sector, for which various possible technological options exist, alongside uncertainty over which options are preferable in terms of cost, emissions reductions, and energy security. To reduce this uncertainty, we here quantify two questions for the power system of Great Britain (England, Wales and Scotland): First, when compared within the same high-resolution modeling framework, how much do different combinations of technologies differ in these three respects? Second, how strongly does the cost and availability of grid-scale storage affect overall system cost, and would it favor some technology combinations above others? We compare three main possible generation technologies: (1) renewables, (2) nuclear, and (3) fossil fuels (with/without carbon capture and storage). Our results show that across a wide range of these combinations, the overall costs remain similar, implying that different configurations are equally feasible both technically and economically. However, the most economically favorable scenarios are not necessarily favorable in terms of emissions or energy security. The availability of grid-scale storage in scenarios with little dispatchable generation can reduce overall levelized electricity cost by up to 50%, depending on storage capacity costs. The UK can rely on its domestic wind and solar PV generation at lower renewable shares, with levelized costs only rising more than 10% above the mean of 0.084 GBP/kWh for shares of 50% and below at a 70% share, which is 35% higher. However, for more than an 80% renewable

Uncertainty analysis for the BEACON-COLSS core monitoring system application

International Nuclear Information System (INIS)

Morita, T.; Boyd, W.A.; Seong, K.B.

2005-01-01

This paper will cover the measurement uncertainty analysis of BEACON-COLSS core monitoring system. The uncertainty evaluation is made by using a BEACON-COLSS simulation program. By simulating the BEACON on-line operation for analytically generated reactor conditions, accuracy of the 'Measured' results can be evaluated by comparing to analytically generated 'Truth'. The DNB power margin is evaluated based on the Combustion Engineering's Modified Statistical Combination of Uncertainties (MSCU) using the CETOPD code for the DNBR calculation. A BEACON-COLSS simulation program for the uncertainty evaluation function has been established for plant applications. Qualification work has been completed for two Combustion Engineering plants. Results of the BEACON-COLSS measured peaking factors and DNBR power margin are plant type dependent and are applicable to reload cores as long as the core geometry and detector layout are unchanged. (authors)

Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

Directory of Open Access Journals (Sweden)

Ying-Yi Hong

2015-03-01

Full Text Available This paper explores real power generation planning, considering distributed generation resources and energy storage in a small standalone power system. On account of the Kyoto Protocol and Copenhagen Accord, wind and photovoltaic (PV powers are considered as clean and renewable energies. In this study, a genetic algorithm (GA was used to determine the optimal capacities of wind-turbine-generators, PV, diesel generators and energy storage in a small standalone power system. The investment costs (installation, unit and maintenance costs of the distributed generation resources and energy storage and the cost of fuel for the diesel generators were minimized while the reliability requirement and CO2 emission limit were fulfilled. The renewable sources and loads were modeled by random variables because of their uncertainties. The equality and inequality constraints in the genetic algorithms were treated by cumulant effects and cumulative probability of random variables, respectively. The IEEE reliability data for an 8760 h load profile with a 150 kW peak load were used to demonstrate the applicability of the proposed method.

Economic evaluation of private power production under uncertainties

Energy Technology Data Exchange (ETDEWEB)

Weiguo Xing; Wu, F.F. [University of Hong Kong (China). Centre for Electrical Energy Systems

2003-02-01

Private power production is becoming an increasingly important source of electricity generation. In developing countries, build-operate-transfer (BOT) arrangement has emerged as a dominant form of private investment. Pricing private power production at its avoided cost is the breakeven point for the utility in economic evaluation, and uncertainties must be taken into account. In this paper, an approach of calculating the breakeven cost to the utility of a BOT power plant whose contract lasts for 10-25 years is proposed. The proposed approach requires the computation of production costs from long-term generation expansion planning (GEP) under future uncertainties. To facilitate the inclusion of constraints introduced by BOT plants in GEP and uncertainties, a genetic algorithm method is utilized in GEP. The breakeven cost is a useful measure in the economic evaluation of BOT power plants. An example is presented to illustrate the economic evaluation of BOT plants using the concept of breakeven cost.(author)

Technical and governance considerations for advanced metering infrastructure/smart meters: Technology, security, uncertainty, costs, benefits, and risks

International Nuclear Information System (INIS)

McHenry, Mark P.

2013-01-01

The fundamental role of policymakers when considering Advanced Metering Infrastructure (AMI), or ‘smart meters for energy and water infrastructure is to investigate a broad range of complex interrelated issues. These include alternative technical and non-technical options and deployment needs, the cost and benefits of the infrastructure (risks and mitigation measures), and the impact of a number of stakeholders: consumers, distributors, retailers, competitive market operators, competing technology companies, etc. The scale and number of potential variables in the AMI space is an almost unprecedented challenge to policymakers, with the anticipation of new ancillary products and services, associated market contestability, related regulatory and policy amendments, and the adequacy of consumer protection, education, and safety considerations requiring utmost due-diligence. Embarking on AMI investment entails significant technical, implementation, and strategic risk for governments and administering bodies, and an active effort is required to ensure AMI governance and planning maximises the potential benefits, and minimise uncertainties, costs, and risks to stakeholders. This work seeks to clarify AMI fundamentals and discusses the technical and related governance considerations from a dispassionate perspective, yet acknowledges many stakeholders tend to dichotomise debate, and obfuscate both advantages and benefits, and the converse. - Highlights: • AMI presents an almost unprecedented technical and governance policy challenge. • AMI enables vertical integration of electricity, gas, water, IT, and telco entities • AMI investments involve major technical, implementation, and strategic decisions. • Adequacy of consumer education, safety, privacy, and protection is paramount. • Policy must maximise AMI benefits and minimise uncertainties, costs, and risks

Development of a decision support tool for seasonal water supply management incorporating system uncertainties and operational constraints

Science.gov (United States)

Wang, H.; Asefa, T.

2017-12-01

A real-time decision support tool (DST) for water supply system would consider system uncertainties, e.g., uncertain streamflow and demand, as well as operational constraints and infrastructure outage (e.g., pump station shutdown, an offline reservoir due to maintenance). Such DST is often used by water managers for resource allocation and delivery for customers. Although most seasonal DST used by water managers recognize those system uncertainties and operational constraints, most use only historical information or assume deterministic outlook of water supply systems. This study presents a seasonal DST that incorporates rainfall/streamflow uncertainties, seasonal demand outlook and system operational constraints. Large scale climate-information is captured through a rainfall simulator driven by a Bayesian non-homogeneous Markov Chain Monte Carlo model that allows non-stationary transition probabilities contingent on Nino 3.4 index. An ad-hoc seasonal demand forecasting model considers weather conditions explicitly and socio-economic factors implicitly. Latin Hypercube sampling is employed to effectively sample probability density functions of flow and demand. Seasonal system operation is modelled as a mixed-integer optimization problem that aims at minimizing operational costs. It embeds the flexibility of modifying operational rules at different components, e.g., surface water treatment plants, desalination facilities, and groundwater pumping stations. The proposed framework is illustrated at a wholesale water supplier in Southeastern United States, Tampa Bay Water. The use of the tool is demonstrated in proving operational guidance in a typical drawdown and refill cycle of a regional reservoir. The DST provided: 1) probabilistic outlook of reservoir storage and chance of a successful refill by the end of rainy season; 2) operational expectations for large infrastructures (e.g., high service pumps and booster stations) throughout the season. Other potential use

Maximizing probable oil field profit: uncertainties on well spacing

International Nuclear Information System (INIS)

MacKay, J.A.; Lerche, I.

1997-01-01

The influence of uncertainties in field development costs, well costs, lifting costs, selling price, discount factor, and oil field reserves are evaluated for their impact on assessing probable ranges of uncertainty on present day worth (PDW), oil field lifetime τ 2/3 , optimum number of wells (OWI), and the minimum (n-) and maximum (n+) number of wells to produce a PDW ≥ O. The relative importance of different factors in contributing to the uncertainties in PDW, τ 2/3 , OWI, nsub(-) and nsub(+) is also analyzed. Numerical illustrations indicate how the maximum PDW depends on the ranges of parameter values, drawn from probability distributions using Monte Carlo simulations. In addition, the procedure illustrates the relative importance of contributions of individual factors to the total uncertainty, so that one can assess where to place effort to improve ranges of uncertainty; while the volatility of each estimate allows one to determine when such effort is needful. (author)

Establishing a cost model when estimating product cost in early design phases

OpenAIRE

Jeppsson, Johanna; Sjöberg, Jessica

2017-01-01

About 75% of the total product cost is determined in the early design phase, which means that the possibilities to affect costs are relatively small when the design phase is completed. For companies, it is therefore vital to conduct reliable cost estimates in the early design phase, when selecting between different design choices. When conducting a cost estimate there are many uncertainties. The aim with this study is therefore to explore how uncertainties regarding product cost can be consid...

Nuclear power costs

International Nuclear Information System (INIS)

1963-01-01

A report prepared by the IAEA Secretariat and presented to the seventh session of the Agency's General Conference says that information on nuclear power costs is now rapidly moving from the domain of uncertain estimates to that of tested factual data. As more and more nuclear power stations are being built and put into operation, more information on the actual costs incurred is becoming available. This is the fourth report on nuclear power costs to be submitted to the IAEA General Conference. The report last year gave cost information on 38 nuclear power projects, 17 of which have already gone into operation. Certain significant changes in the data given last year are included-in the present report; besides, information is given on seven new plants. The report is divided into two parts, the first on recent developments and current trends in nuclear power costs and the second on the use of the cost data for economic comparisons. Both stress the fact that the margin of uncertainty in the basic data has lately been drastically reduced. At the same time, it is pointed out, some degree of uncertainty is inherent in the assumptions made in arriving at over-all generating cost figures, especially when - as is usually the case - a nuclear plant is part of an integrated power system

Utilization of Software Tools for Uncertainty Calculation in Measurement Science Education

International Nuclear Information System (INIS)

Zangl, Hubert; Zine-Zine, Mariam; Hoermaier, Klaus

2015-01-01

Despite its importance, uncertainty is often neglected by practitioners in the design of system even in safety critical applications. Thus, problems arising from uncertainty may only be identified late in the design process and thus lead to additional costs. Although there exists numerous tools to support uncertainty calculation, reasons for limited usage in early design phases may be low awareness of the existence of the tools and insufficient training in the practical application. We present a teaching philosophy that addresses uncertainty from the very beginning of teaching measurement science, in particular with respect to the utilization of software tools. The developed teaching material is based on the GUM method and makes use of uncertainty toolboxes in the simulation environment. Based on examples in measurement science education we discuss advantages and disadvantages of the proposed teaching philosophy and include feedback from students

Stochastic Control Synthesis of Systems with Structured Uncertainty

Science.gov (United States)

Padula, Sharon L. (Technical Monitor); Crespo, Luis G.

2003-01-01

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Analysis of uncertainties in the IAEA/WHO TLD postal dose audit system

Energy Technology Data Exchange (ETDEWEB)

Izewska, J. [Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Wagramer Strasse 5, Vienna (Austria)], E-mail: j.izewska@iaea.org; Hultqvist, M. [Department of Medical Radiation Physics, Karolinska Institute, Stockholm University, Stockholm (Sweden); Bera, P. [Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Wagramer Strasse 5, Vienna (Austria)

2008-02-15

The International Atomic Energy Agency (IAEA) and the World Health Organisation (WHO) operate the IAEA/WHO TLD postal dose audit programme. Thermoluminescence dosimeters (TLDs) are used as transfer devices in this programme. In the present work the uncertainties in the dose determination from TLD measurements have been evaluated. The analysis of uncertainties comprises uncertainties in the calibration coefficient of the TLD system and uncertainties in factors correcting for dose response non-linearity, fading of TL signal, energy response and influence of TLD holder. The individual uncertainties have been combined to estimate the total uncertainty in the dose evaluated from TLD measurements. The combined relative standard uncertainty in the dose determined from TLD measurements has been estimated to be 1.2% for irradiations with Co-60 {gamma}-rays and 1.6% for irradiations with high-energy X-rays. Results from irradiations by the Bureau international des poids et mesures (BIPM), Primary Standard Dosimetry Laboratories (PSDLs) and Secondary Standards Dosimetry Laboratories (SSDLs) compare favourably with the estimated uncertainties, whereas TLD results of radiotherapy centres show higher standard deviations than those derived theoretically.

Robust stabilisation of time-varying delay systems with probabilistic uncertainties

Science.gov (United States)

Jiang, Ning; Xiong, Junlin; Lam, James

2016-09-01

For robust stabilisation of time-varying delay systems, only sufficient conditions are available to date. A natural question is as follows: if the existing sufficient conditions are not satisfied, and hence no controllers can be found, what can one do to improve the stability performance of time-varying delay systems? This question is addressed in this paper when there is a probabilistic structure on the parameter uncertainty set. A randomised algorithm is proposed to design a state-feedback controller, which stabilises the system over the uncertainty domain in a probabilistic sense. The capability of the designed controller is quantified by the probability of stability of the resulting closed-loop system. The accuracy of the solution obtained from the randomised algorithm is also analysed. Finally, numerical examples are used to illustrate the effectiveness and advantages of the developed controller design approach.

State Estimation for Sensor Monitoring System with Uncertainty and Disturbance

Directory of Open Access Journals (Sweden)

Jianhong Sun

2014-10-01

Full Text Available This paper considers the state estimation problem for the sensor monitoring system which contains system uncertainty and nonlinear disturbance. In the sensor monitoring system, states of each inner sensor node usually contains system uncertainty, and external noise often works as nonlinear item. Besides, information transmission in the system is also time consuming. All mentioned above may arouse in unstable of the monitoring system. In this case, states of sensors could be wrongly sampled. Under this circumstance, a proper mathematical model is proposed and by the use of Lipschitz condition, the nonlinear item is transformed to linear one. In addition, we suppose that all sensor nodes are distributed arranged, no interface occurs with each other. By establishing proper Lyapunov– Krasovskii functional, sufficient conditions are acquired by solving linear matrix inequality to make the error augmented system stable, and the gains of observers are also derived. Finally, an illustrated example is given to show that system observed value tracks system states well, which fully demonstrate the effectiveness of our result.

Intrinsic Uncertainties in Modeling Complex Systems.

Energy Technology Data Exchange (ETDEWEB)

Cooper, Curtis S; Bramson, Aaron L.; Ames, Arlo L.

2014-09-01

Models are built to understand and predict the behaviors of both natural and artificial systems. Because it is always necessary to abstract away aspects of any non-trivial system being modeled, we know models can potentially leave out important, even critical elements. This reality of the modeling enterprise forces us to consider the prospective impacts of those effects completely left out of a model - either intentionally or unconsidered. Insensitivity to new structure is an indication of diminishing returns. In this work, we represent a hypothetical unknown effect on a validated model as a finite perturba- tion whose amplitude is constrained within a control region. We find robustly that without further constraints, no meaningful bounds can be placed on the amplitude of a perturbation outside of the control region. Thus, forecasting into unsampled regions is a very risky proposition. We also present inherent difficulties with proper time discretization of models and representing in- herently discrete quantities. We point out potentially worrisome uncertainties, arising from math- ematical formulation alone, which modelers can inadvertently introduce into models of complex systems. Acknowledgements This work has been funded under early-career LDRD project #170979, entitled "Quantify- ing Confidence in Complex Systems Models Having Structural Uncertainties", which ran from 04/2013 to 09/2014. We wish to express our gratitude to the many researchers at Sandia who con- tributed ideas to this work, as well as feedback on the manuscript. In particular, we would like to mention George Barr, Alexander Outkin, Walt Beyeler, Eric Vugrin, and Laura Swiler for provid- ing invaluable advice and guidance through the course of the project. We would also like to thank Steven Kleban, Amanda Gonzales, Trevor Manzanares, and Sarah Burwell for their assistance in managing project tasks and resources.

A systems engineering cost analysis capability for use in assessing nuclear waste management system cost performance

International Nuclear Information System (INIS)

Shay, M.R.

1990-04-01

The System Engineering Cost Analysis (SECA) capability has been developed by the System Integration Branch of the US Department of Energy's Office of Civilian Radioactive Waste Management for use in assessing the cost performance of alternative waste management system configurations. The SECA capability is designed to provide rapid cost estimates of the waste management system for a given operational scenario and to permit aggregate or detailed cost comparisons for alternative waste system configurations. This capability may be used as an integral part of the System Integration Modeling System (SIMS) or, with appropriate input defining a scenario, as a separate cost analysis model

Uncertainty in relative cost investigation

International Nuclear Information System (INIS)

Bunn, D.; Viahos, K.

1989-01-01

One of the consequences of the privatization of the Central Electricity Generating Board has been a weakening of the economic case for nuclear generation over coal. Nuclear has higher capital, but lower operating costs than coal and is therefore favoured in capital budgeting by discounting at lower rates of return. In the Sizewell case (in 1987), discounting at the public sector rate of 5 per cent favoured nuclear. However, the private sector will require higher rates of return, thus rendering nuclear less attractive. Hence the imposition by the government of a diversity constraint on the privatized industry to ensure that contracts are made for a minimum fraction of non-fossil (essentially nuclear) energy. An electricity capacity planning model was developed to estimate the costs of imposing various non-fossil energy constraints on the planning decision of a privatized electricity supply industry, as a function of various discount rates. Using a large-scale linear programming technique, the model optimizes over a 50 year horizon the schedule of installation, and mix of generating capacity, both with and without a minimum non-fossil constraint. The conclusion is that the opportunity cost of diversity may be a complex joint substation of more than one type of plant (eg coal and gas) depending on the discount rate. (author)

Uncertainty modeling in vibration, control and fuzzy analysis of structural systems

CERN Document Server

Halder, Achintya; Ayyub, Bilal M

1997-01-01

This book gives an overview of the current state of uncertainty modeling in vibration, control, and fuzzy analysis of structural and mechanical systems. It is a coherent compendium written by leading experts and offers the reader a sampling of exciting research areas in several fast-growing branches in this field. Uncertainty modeling and analysis are becoming an integral part of system definition and modeling in many fields. The book consists of ten chapters that report the work of researchers, scientists and engineers on theoretical developments and diversified applications in engineering sy

Stochastic cost estimating in repository life-cycle cost analysis

International Nuclear Information System (INIS)

Tzemos, S.; Dippold, D.

1986-01-01

The conceptual development, the design, and the final construction and operation of a nuclear repository span many decades. Given this lengthy time frame, it is quite challenging to obtain a good approximation of the repository life-cycle cost. One can deal with this challenge by using an analytic method, the method of moments, to explicitly assess the uncertainty of the estimate. A series expansion is used to approximate the uncertainty distribution of the cost estimate. In this paper, the moment methodology is derived and is illustrated through a numerical example. The range of validity of the approximation is discussed. The method of moments is compared to the traditional stochastic cost estimating methods and found to provide more and better information on cost uncertainty. The tow methods converge to identical results as the number of convolved variables increases and approaches the range where the central limit theorem is valid

Planning regional energy system in association with greenhouse gas mitigation under uncertainty

Energy Technology Data Exchange (ETDEWEB)

Li, Y.P.; Huang, G.H. [Research Academy of Energy and Environmental Studies, North China Electric Power University, Beijing 102206 (China); Chen, X. [Key Laboratory of Oasis Ecology and Desert Environment, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011 (China)

2011-03-15

Greenhouse gas (GHG) concentrations are expected to continue to rise due to the ever-increasing use of fossil fuels and ever-boosting demand for energy. This leads to inevitable conflict between satisfying increasing energy demand and reducing GHG emissions. In this study, an integrated fuzzy-stochastic optimization model (IFOM) is developed for planning energy systems in association with GHG mitigation. Multiple uncertainties presented as probability distributions, fuzzy-intervals and their combinations are allowed to be incorporated within the framework of IFOM. The developed method is then applied to a case study of long-term planning of a regional energy system, where integer programming (IP) technique is introduced into the IFOM to facilitate dynamic analysis for capacity-expansion planning of energy-production facilities within a multistage context to satisfy increasing energy demand. Solutions related fuzzy and probability information are obtained and can be used for generating decision alternatives. The results can not only provide optimal energy resource/service allocation and capacity-expansion plans, but also help decision-makers identify desired policies for GHG mitigation with a cost-effective manner. (author)

Observation of quantum-memory-assisted entropic uncertainty relation under open systems, and its steering

Science.gov (United States)

Chen, Peng-Fei; Sun, Wen-Yang; Ming, Fei; Huang, Ai-Jun; Wang, Dong; Ye, Liu

2018-01-01

Quantum objects are susceptible to noise from their surrounding environments, interaction with which inevitably gives rise to quantum decoherence or dissipation effects. In this work, we examine how different types of local noise under an open system affect entropic uncertainty relations for two incompatible measurements. Explicitly, we observe the dynamics of the entropic uncertainty in the presence of quantum memory under two canonical categories of noisy environments: unital (phase flip) and nonunital (amplitude damping). Our study shows that the measurement uncertainty exhibits a non-monotonic dynamical behavior—that is, the amount of the uncertainty will first inflate, and subsequently decrease, with the growth of decoherence strengths in the two channels. In contrast, the uncertainty decreases monotonically with the growth of the purity of the initial state shared in prior. In order to reduce the measurement uncertainty in noisy environments, we put forward a remarkably effective strategy to steer the magnitude of uncertainty by means of a local non-unitary operation (i.e. weak measurement) on the qubit of interest. It turns out that this non-unitary operation can greatly reduce the entropic uncertainty, upon tuning the operation strength. Our investigations might thereby offer an insight into the dynamics and steering of entropic uncertainty in open systems.

Uncertainty quantification and validation of combined hydrological and macroeconomic analyses.

Energy Technology Data Exchange (ETDEWEB)

Hernandez, Jacquelynne; Parks, Mancel Jordan; Jennings, Barbara Joan; Kaplan, Paul Garry; Brown, Theresa Jean; Conrad, Stephen Hamilton

2010-09-01

Changes in climate can lead to instabilities in physical and economic systems, particularly in regions with marginal resources. Global climate models indicate increasing global mean temperatures over the decades to come and uncertainty in the local to national impacts means perceived risks will drive planning decisions. Agent-based models provide one of the few ways to evaluate the potential changes in behavior in coupled social-physical systems and to quantify and compare risks. The current generation of climate impact analyses provides estimates of the economic cost of climate change for a limited set of climate scenarios that account for a small subset of the dynamics and uncertainties. To better understand the risk to national security, the next generation of risk assessment models must represent global stresses, population vulnerability to those stresses, and the uncertainty in population responses and outcomes that could have a significant impact on U.S. national security.

Optimization under Uncertainty of Site-Specific Turbine Configurations: Preprint

Energy Technology Data Exchange (ETDEWEB)

Quick, Julian; Dykes, Katherine; Graf, Peter; Zahle, Frederik

2016-11-01

Uncertainty affects many aspects of wind energy plant performance and cost. In this study, we explore opportunities for site-specific turbine configuration optimization that accounts for uncertainty in the wind resource. As a demonstration, a simple empirical model for wind plant cost of energy is used in an optimization under uncertainty to examine how different risk appetites affect the optimal selection of a turbine configuration for sites of different wind resource profiles. If there is unusually high uncertainty in the site wind resource, the optimal turbine configuration diverges from the deterministic case and a generally more conservative design is obtained with increasing risk aversion on the part of the designer.

Personal Conflict Impairs Performance on an Unrelated Self-Control Task: Lingering Costs of Uncertainty and Conflict.

Science.gov (United States)

Alquist, Jessica L; Baumeister, Roy F; McGregor, Ian; Core, Tammy J; Benjamin, Ilil; Tice, Dianne M

2018-01-01

People have the ability to make important choices in their lives, but deliberating about these choices can have costs. The present study was designed to test the hypothesis that writing about conflicted personal goals and values (conflict condition) would impair self-control on an unrelated subsequent task as compared to writing about clear personal goals and values (clarity condition). Personal conflict activates the behavioral inhibition system (BIS; Hirsh, Mar, & Peterson, 2012), which may make it harder for participants to successfully execute self-control. In this large ( N =337), pre-registered study participants in the conflict condition performed worse on anagrams than participants in the clarity condition, and the effect of condition on anagram performance was mediated by a subjective uncertainty measure of BIS activation. This suggests that BIS activation leads to poor self-control. Moreover, given that conflict is inherent in the exercise of self-control, results point to BIS activation as a mechanism for why initial acts of self-control impair self-control on subsequent, unrelated tasks.

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

Directory of Open Access Journals (Sweden)

Hong Zhang

2018-04-01

Full Text Available With the rapid growth of electricity demands, many traditional distributed networks cannot cover their peak demands, especially in the evening. Additionally, with the interconnection of distributed electrical and thermal grids, system operational flexibility and energy efficiency can be affected as well. Therefore, by adding a portable energy system and a heat storage tank to the traditional distributed system, this paper proposes a newly defined distributed network to deal with the aforementioned problems. Simulation results show that by adding a portable energy system, fossil fuel energy consumption and daily operation cost can be reduced by 8% and 28.29%, respectively. Moreover, system peak load regulating capacity can be significantly improved. However, by introducing the portable energy system to the grid, system uncertainty can be increased to some extent. Therefore, chance constrained programming is proposed to control the system while considering system uncertainty. By applying Particle Swarm Optimization—Monte Carlo to solve the chance constrained programming, results show that power system economy and uncertainty can be compromised by selecting appropriate confidence levels α and β. It is also reported that by installing an extra heat storage tank, combined heat and power energy efficiency can be significantly improved and the installation capacity of the battery can be reduced.

Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

International Nuclear Information System (INIS)

Shah, Harsheel; Hosder, Serhat; Winter, Tyler

2015-01-01

The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presence of mixed (aleatory and multiple sources of epistemic) uncertainty to the reliability and performance assessment of complex engineering systems through the use of quantification of margins and uncertainties (QMU) methodology. This study focuses on quantifying the simulation uncertainties, both in the design condition and the performance boundaries along with the determination of margins. To address the possibility of multiple sources and intervals for epistemic uncertainty characterization, DSTE is used for uncertainty quantification. An approach to incorporate aleatory uncertainty in Dempster–Shafer structures is presented by discretizing the aleatory variable distributions into sets of intervals. In view of excessive computational costs for large scale applications and repetitive simulations needed for DSTE analysis, a stochastic response surface based on point-collocation non-intrusive polynomial chaos (NIPC) has been implemented as the surrogate for the model response. The technique is demonstrated on a model problem with non-linear analytical functions representing the outputs and performance boundaries of two coupled systems. Finally, the QMU approach is demonstrated on a multi-disciplinary analysis of a high speed civil transport (HSCT). - Highlights: • Quantification of margins and uncertainties (QMU) methodology with evidence theory. • Treatment of both inherent and epistemic uncertainties within evidence theory. • Stochastic expansions for representation of performance metrics and boundaries. • Demonstration of QMU on an analytical problem. • QMU analysis applied to an aerospace system (high speed civil transport)

The role of uncertainty analysis in dose reconstruction and risk assessment

International Nuclear Information System (INIS)

Hoffman, F.O.; Simon, S.L.; Thiessen. K.M.

1996-01-01

Dose reconstruction and risk assessment rely heavily on the use of mathematical models to extrapolate information beyond the realm of direct observation. Because models are merely approximations of real systems, their predictions are inherently uncertain. As a result, full disclosure of uncertainty in dose and risk estimates is essential to achieve scientific credibility and to build public trust. The need for formal analysis of uncertainty in model predictions was presented during the nineteenth annual meeting of the NCRP. At that time, quantitative uncertainty analysis was considered a relatively new and difficult subject practiced by only a few investigators. Today, uncertainty analysis has become synonymous with the assessment process itself. When an uncertainty analysis is used iteratively within the assessment process, it can guide experimental research to refine dose and risk estimates, deferring potentially high cost or high consequence decisions until uncertainty is either acceptable or irreducible. Uncertainty analysis is now mandated for all ongoing dose reconstruction projects within the United States, a fact that distinguishes dose reconstruction from other types of exposure and risk assessments. 64 refs., 6 figs., 1 tab

Roles of nuclear power system in the presence of uncertainties from global warming

International Nuclear Information System (INIS)

Kiriyama, Eriko; Iwata, Shuichi

2005-01-01

Environmental 'bads' like carbon dioxide are not simply incorporated in the market system. The costs involved, however, do not really reflect the inherent value of the environment, or what it is worth to them. This study focuses on the uncertainty of CO 2 emission credits. Assigning economic values to environmental goods and services is complex, but it is an area that is receiving considerable attention from environmental economists. The purpose of this study is to analyze the value of an investment in power generation assets that do not emit CO 2 . To deal with the CO 2 emission credit, we built new models based on the real option model by Pindyck (2000). In the modern, market-based financial systems that dominate the global economy, the value of a resource is represented by the price that an individual or a group is willing to pay for it. Managing CO 2 emission limitations will be a critical aspect of power generation systems. And it will be increasingly so as the emphasis on global environmental issues continues to rise. In order to secure the effectiveness of measures against global warming, we should reconsider the role of nuclear power systems. (author)

The future of nuclear power in France. An analysis of the costs of phasing-out

International Nuclear Information System (INIS)

Malischek, Raimund; Trueby, Johannes

2014-01-01

Nuclear power is an important pillar in electricity generation in France. However, France's nuclear power plant fleet is ageing, and the possibility of reducing its share in power generation or even a complete phaseout has been increasingly discussed. Our research therefore focuses on three questions: First, what are the costs of phasing-out nuclear power in France under different scenarios? Second, who has to bear these costs, i.e., how much of the costs will be passed on to the rest of the European power system? And third, what effect does the uncertainty regarding future nuclear policy in France have on system costs? Applying a stochastic optimization model for the European electricity system, we show that additional system costs in France of a nuclear phase-out amount up to 76 billion EURO 2010 . Additional costs are mostly borne by the French power system. Surprisingly, we find that the costs of uncertainty are rather limited. Based on our results, we conclude that a commitment regarding nuclear policy reform is only mildly beneficial in terms of system costs.

Cost Accounting System for fusion studies

International Nuclear Information System (INIS)

Hamilton, W.R.; Keeton, D.C.; Thomson, S.L.

1985-12-01

A Cost Accounting System that is applicable to all magnetic fusion reactor design studies has been developed. This system provides: (1) definitions of the elements of cost and methods for the combination of these elements to form a cost estimate; (2) a Code of Accounts that uses a functional arrangement for identification of the plant components; and (3) definitions and methods to analyze actual cost data so that the data can be directly reported into this Cost Accounting System. The purpose of the Cost Accounting System is to provide the structure for the development of a fusion cost data base and for the development of validated cost estimating procedures. This system has been developed through use at the Fusion Engineering Design Center (FEDC) and has been applied to different confinement concepts (tokamaks and tandem mirrors) and to different types of projects (experimental devices and commercial power plants). The use of this Cost Accounting System by all magnetic fusion projects will promote the development of a common cost data base, allow the direct comparison of cost estimates, and ultimately establish the cost credibility of the program

Cost Accounting System for fusion studies

Energy Technology Data Exchange (ETDEWEB)

Hamilton, W.R.; Keeton, D.C.; Thomson, S.L.

1985-12-01

A Cost Accounting System that is applicable to all magnetic fusion reactor design studies has been developed. This system provides: (1) definitions of the elements of cost and methods for the combination of these elements to form a cost estimate; (2) a Code of Accounts that uses a functional arrangement for identification of the plant components; and (3) definitions and methods to analyze actual cost data so that the data can be directly reported into this Cost Accounting System. The purpose of the Cost Accounting System is to provide the structure for the development of a fusion cost data base and for the development of validated cost estimating procedures. This system has been developed through use at the Fusion Engineering Design Center (FEDC) and has been applied to different confinement concepts (tokamaks and tandem mirrors) and to different types of projects (experimental devices and commercial power plants). The use of this Cost Accounting System by all magnetic fusion projects will promote the development of a common cost data base, allow the direct comparison of cost estimates, and ultimately establish the cost credibility of the program.

Change and uncertainty in quantum systems

International Nuclear Information System (INIS)

Franson, J.D.

1996-01-01

A simple inequality shows that any change in the expectation value of an observable quantity must be associated with some degree of uncertainty. This inequality is often more restrictive than the Heisenberg uncertainty principle. copyright 1996 The American Physical Society

Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability

International Nuclear Information System (INIS)

Chen, Hung-Cheng

2013-01-01

Highlights: ► This paper presents a methodology for the installation capacity optimization. ► Hybrid generation system is optimized by application of adaptive genetic algorithm. ► A cost investigation is made under various conditions and component characteristics. ► The optimization scheme is validated to meet the annual power load demand. -- Abstract: The aim of this work is to present an optimization methodology for the installation capacity of a stand-alone hybrid generation system, taking into consideration the cost and reliability. Firstly, on the basis of derived steady state models of a wind generator (WG), a photovoltaic array (PV), a battery and an inverter, the hybrid generation system is modeled for the purpose of capacity optimization. Secondly, the power system is analyzed for determining both the system structure and the operation control strategy. Thirdly, according to hourly weather database of wind speed, temperature and solar irradiation, annual power generation capacity is estimated for the system match design in order that an annual power load demand can be met. The capacity determination of a hybrid generation system becomes complicated as a result of the uncertainty in the renewable energy together with load demand and the nonlinearity of system components. Aimed at the power system reliability and the cost minimization, the capacity of a hybrid generation system is optimized by application of an adaptive genetic algorithm (AGA) to individual power generation units. A total cost investigation is made under various conditions, such as wind generator power curves, battery discharge depth and the loss of load probability (LOLP). At the end of this work, the capacity of a hybrid generation system is optimized at two installation sites, namely the offshore Orchid Island and Wuchi in Taiwan. The optimization scheme is validated to optimize power capacities of a photovoltaic array, a battery and a wind turbine generator with a relative

Modeling for waste management associated with environmental-impact abatement under uncertainty.

Science.gov (United States)

Li, P; Li, Y P; Huang, G H; Zhang, J L

2015-04-01

Municipal solid waste (MSW) treatment can generate significant amounts of pollutants, and thus pose a risk on human health. Besides, in MSW management, various uncertainties exist in the related costs, impact factors, and objectives, which can affect the optimization processes and the decision schemes generated. In this study, a life cycle assessment-based interval-parameter programming (LCA-IPP) method is developed for MSW management associated with environmental-impact abatement under uncertainty. The LCA-IPP can effectively examine the environmental consequences based on a number of environmental impact categories (i.e., greenhouse gas equivalent, acid gas emissions, and respiratory inorganics), through analyzing each life cycle stage and/or major contributing process related to various MSW management activities. It can also tackle uncertainties existed in the related costs, impact factors, and objectives and expressed as interval numbers. Then, the LCA-IPP method is applied to MSW management for the City of Beijing, the capital of China, where energy consumptions and six environmental parameters [i.e., CO2, CO, CH4, NOX, SO2, inhalable particle (PM10)] are used as systematic tool to quantify environmental releases in entire life cycle stage of waste collection, transportation, treatment, and disposal of. Results associated with system cost, environmental impact, and the related policy implication are generated and analyzed. Results can help identify desired alternatives for managing MSW flows, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty.

An Exact Soultion for the Investment and Market Value of a Firm Facing Uncertainty, Adjustment Costs, and Irreversibility

OpenAIRE

Andrew B. Abel; Janice C. Eberly

1993-01-01

This paper derives closed-form solutions for the investment and market value, under uncertainty, of competitive firms with constant returns to scale production and convex costs of adjustment. Solutions are derived for the case of irreversible investment as well as for reversible investment. Optimal investment is a non-decreasing function of q, the shadow value of capital. The conditions of optimality imply that q cannot contain a bubble; thus, optimal investment depends only on fundamentals. ...

Cost modelling of electricity-producing hot dry rock (HDR) geothermal systems in the United Kingdom

International Nuclear Information System (INIS)

Doherty, P.; Harrison, R.

1995-01-01

A detailed and comprehensive cost model for Hot Dry Rock (HDR) electricity producing systems has been developed in this study. The model takes account of the major aspects of the HDR system, parameterized in terms of the main physical and cost parameters of the resource and the utilization system. A doublet configuration is assumed, and the conceptual HDR system which is defined in the study is based upon the UK Department of Energy (DEn) HDR geothermal R and D programme. The model has been used to calculate the costs of HDR electricity for a UK defined base case which represents a consensus view of what might be achieved in Cornwall in the long term. At 14.2 p/kWh (1988 costs) this cost appears to be unacceptably high. A wide-ranging sensitivity study has also been carried out on the main resource, geometrical, and operational parameters of the HDR system centred around the UK base case. The sensitivity study shows the most important parameters to be thermal gradient and depth. The geometrical arrangement and the shape of the reservoir constitute major uncertainties in HDR systems. Their effect on temperature has a major influence on system performance, and therefore a range of theoretically possible geometries have been studied and the importance of geometrical effects on HDR electricity costs assessed. The most cost effective HDR arrangement in terms of optimized volumes and flow rates has been investigated for a world-wide range of thermal settings. The main conclusions from this study suggests that for HDR electricity to be economic, thermal gradients of 55 o C/km and above, well depths of 5 km or less, and production fluid temperatures of 210 o C and above are required. (UK)

ASSESSMENT OF UNCERTAINTY IN THE RADIATION DOSES FOR THE TECHA RIVER DOSIMETRY SYSTEM

Energy Technology Data Exchange (ETDEWEB)

Napier, Bruce A.; Degteva, M. O.; Anspaugh, L. R.; Shagina, N. B.

2009-10-23

In order to provide more accurate and precise estimates of individual dose (and thus more precise estimates of radiation risk) for the members of the ETRC, a new dosimetric calculation system, the Techa River Dosimetry System-2009 (TRDS-2009) has been prepared. The deterministic version of the improved dosimetry system TRDS-2009D was basically completed in April 2009. Recent developments in evaluation of dose-response models in light of uncertain dose have highlighted the importance of different types of uncertainties in the development of individual dose estimates. These include uncertain parameters that may be either shared or unshared within the dosimetric cohort, and also the nature of the type of uncertainty as aleatory or epistemic and either classical or Berkson. This report identifies the nature of the various input parameters and calculational methods incorporated in the Techa River Dosimetry System (based on the TRDS-2009D implementation), with the intention of preparing a stochastic version to estimate the uncertainties in the dose estimates. This report reviews the equations, databases, and input parameters, and then identifies the author’s interpretations of their general nature. It presents the approach selected so that the stochastic, Monte-Carlo, implementation of the dosimetry System - TRDS-2009MC - will provide useful information regarding the uncertainties of the doses.

Facts and feelings: Framing effects in responses to uncertainties about high-voltage power lines

OpenAIRE

de Vries, G.; de Bruijn, J.A.

2017-01-01

To ensure power supply security, electricity transmission system operators (TSOs) have to upscale high-voltage overhead power lines. However, upscaling frequently meets opposition. Opposition can be caused by uncertainties about risks and benefits and might lead to costly delays (Linder, 1995; Wiedemann, Boerner,& Claus, 2016). To minimize opposition, TSOs and related public services need to respond to these uncertainties in a credible and convincing (effective) way. Effective risk commun...

The relationship between cost system complexity, purposes of use, and cost system effectiveness

NARCIS (Netherlands)

Schoute, M.

2009-01-01

This paper uses survey data from 133 Dutch, medium-sized manufacturing firms to examine the associations between cost system complexity (in terms of the applied overhead absorption procedures), purposes of use, and cost system effectiveness. First, factor analysis identifies two underlying

Physical Uncertainty Bounds (PUB)

Energy Technology Data Exchange (ETDEWEB)

Vaughan, Diane Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Dean L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-03-19

This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.

Quality cost system in electronics

International Nuclear Information System (INIS)

Denzer, H.O.

1978-01-01

A description is presented of a formal cost of quality system used in an electronic manufacturing facility. The system elements and reports are illustrated. Examples of the use of a quality cost system to measure quality performance and to improve product quality are included. A comparison to industry averages for quality costs is made. The paper attempts to show that the collection and use of quality costs are an aid to management and can be accompanied by improved product quality

Decision-making under surprise and uncertainty: Arsenic contamination of water supplies

Science.gov (United States)

Randhir, Timothy O.; Mozumder, Pallab; Halim, Nafisa

2018-05-01

With ignorance and potential surprise dominating decision making in water resources, a framework for dealing with such uncertainty is a critical need in hydrology. We operationalize the 'potential surprise' criterion proposed by Shackle, Vickers, and Katzner (SVK) to derive decision rules to manage water resources under uncertainty and ignorance. We apply this framework to managing water supply systems in Bangladesh that face severe, naturally occurring arsenic contamination. The uncertainty involved with arsenic in water supplies makes the application of conventional analysis of decision-making ineffective. Given the uncertainty and surprise involved in such cases, we find that optimal decisions tend to favor actions that avoid irreversible outcomes instead of conventional cost-effective actions. We observe that a diversification of the water supply system also emerges as a robust strategy to avert unintended outcomes of water contamination. Shallow wells had a slight higher optimal level (36%) compare to deep wells and surface treatment which had allocation levels of roughly 32% under each. The approach can be applied in a variety of other cases that involve decision making under uncertainty and surprise, a frequent situation in natural resources management.

Review of monitoring uncertainty requirements in the CDM

International Nuclear Information System (INIS)

Shishlov, Igor; Bellassen, Valentin

2014-10-01

In order to ensure the environmental integrity of carbon offset projects, emission reductions certified under the Clean Development Mechanism (CDM) have to be 'real, measurable and additional', which is ensured through the monitoring, reporting and verification (MRV) process. MRV, however, comes at a cost that ranges from several cents to EUR1.20 and above per ton of CO 2 e depending on the project type. This article analyzes monitoring uncertainty requirements for carbon offset projects with a particular focus on the trade-off between monitoring stringency and cost. To this end, we review existing literature, scrutinize both overarching monitoring guidelines and the 10 most-used methodologies, and finally we analyze four case studies. We find that there is indeed a natural trade-off between the stringency and the cost of monitoring, which if not addressed properly may become a major barrier for the implementation of offset projects in some sectors. We demonstrate that this trade-off has not been systematically addressed in the overarching CDM guidelines and that there are only limited incentives to reduce monitoring uncertainty. Some methodologies and calculation tools as well as some other offset standards, however, do incorporate provisions for a trade-off between monitoring costs and stringency. These provisions may take the form of discounting emissions reductions based on the level of monitoring uncertainty - or more implicitly through allowing a project developer to choose between monitoring a given parameter and using a conservative default value. Our findings support the introduction of an uncertainty standard under the CDM for more comprehensive, yet cost-efficient, accounting for monitoring uncertainty in carbon offset projects. (authors)

Comparison between different uncertainty propagation methods in multivariate analysis: An application in the bivariate case

International Nuclear Information System (INIS)

Mullor, R.; Sanchez, A.; Martorell, S.; Martinez-Alzamora, N.

2011-01-01

Safety related systems performance optimization is classically based on quantifying the effects that testing and maintenance activities have on reliability and cost (R+C). However, R+C quantification is often incomplete in the sense that important uncertainties may not be considered. An important number of studies have been published in the last decade in the field of R+C based optimization considering uncertainties. They have demonstrated that inclusion of uncertainties in the optimization brings the decision maker insights concerning how uncertain the R+C results are and how this uncertainty does matter as it can result in differences in the outcome of the decision making process. Several methods of uncertainty propagation based on the theory of tolerance regions have been proposed in the literature depending on the particular characteristics of the variables in the output and their relations. In this context, the objective of this paper focuses on the application of non-parametric and parametric methods to analyze uncertainty propagation, which will be implemented on a multi-objective optimization problem where reliability and cost act as decision criteria and maintenance intervals act as decision variables. Finally, a comparison of results of these applications and the conclusions obtained are presented.

Comparison between different uncertainty propagation methods in multivariate analysis: An application in the bivariate case

Energy Technology Data Exchange (ETDEWEB)

Mullor, R. [Dpto. Estadistica e Investigacion Operativa, Universidad Alicante (Spain); Sanchez, A., E-mail: aisanche@eio.upv.e [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia, Camino de Vera s/n 46022 (Spain); Martorell, S. [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Martinez-Alzamora, N. [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia, Camino de Vera s/n 46022 (Spain)

2011-06-15

Safety related systems performance optimization is classically based on quantifying the effects that testing and maintenance activities have on reliability and cost (R+C). However, R+C quantification is often incomplete in the sense that important uncertainties may not be considered. An important number of studies have been published in the last decade in the field of R+C based optimization considering uncertainties. They have demonstrated that inclusion of uncertainties in the optimization brings the decision maker insights concerning how uncertain the R+C results are and how this uncertainty does matter as it can result in differences in the outcome of the decision making process. Several methods of uncertainty propagation based on the theory of tolerance regions have been proposed in the literature depending on the particular characteristics of the variables in the output and their relations. In this context, the objective of this paper focuses on the application of non-parametric and parametric methods to analyze uncertainty propagation, which will be implemented on a multi-objective optimization problem where reliability and cost act as decision criteria and maintenance intervals act as decision variables. Finally, a comparison of results of these applications and the conclusions obtained are presented.

Uncertainty in prediction and simulation of flow in sewer systems

DEFF Research Database (Denmark)

Breinholt, Anders

the uncertainty in the state variables. Additionally the observation noise is accounted for by a separate observation noise term. This approach is also referred to as stochastic grey-box modelling. A state dependent diffusion term was developed using a Lamperti transformation of the states, and implemented...... performance beyond the one-step. The reliability was satisfied for the one-step prediction but were increasingly biased as the prediction horizon was expanded, particularly in rainy periods. GLUE was applied for estimating uncertainty in such a way that the selection of behavioral parameter sets continued....... Conversely the parameter estimates of the stochastic approach are physically meaningful. This thesis has contributed to developing simplified rainfall-runoff models that are suitable for model predictive control of urban drainage systems that takes uncertainty into account....

Resistance Economics of Transgenic Crops under Uncertainty: A Real Option Approach

NARCIS (Netherlands)

Wesseler, J.H.H.

2003-01-01

The development of pest resistance is one of the many concerns about the long-term success of transgenic crops. This chapter discusses resistances as additional irreversible costs related to the release of transgenic crops. These irreversible costs, their uncertainty, and the uncertainty about

Robust Optimisation for Hydroelectric System Operation under Uncertainty

OpenAIRE

Apostolopoulou, D.; De Greve, Z.; McCulloch, M.

2018-01-01

In this paper, we propose an optimal dispatch scheme for a cascade hydroelectric power system that maximises the head levels of each dam, and minimises the spillage effects taking into account uncertainty in the net load variations, i.e., the difference between the load and the renewable resources, and inflows to the cascade. By maximising the head levels of each dam the volume of water stored, which is a metric of system resiliency, is maximised. In this regard, the operation of the cascade ...

Aleatoric and epistemic uncertainties in sampling based nuclear data uncertainty and sensitivity analyses

International Nuclear Information System (INIS)

Zwermann, W.; Krzykacz-Hausmann, B.; Gallner, L.; Klein, M.; Pautz, A.; Velkov, K.

2012-01-01

Sampling based uncertainty and sensitivity analyses due to epistemic input uncertainties, i.e. to an incomplete knowledge of uncertain input parameters, can be performed with arbitrary application programs to solve the physical problem under consideration. For the description of steady-state particle transport, direct simulations of the microscopic processes with Monte Carlo codes are often used. This introduces an additional source of uncertainty, the aleatoric sampling uncertainty, which is due to the randomness of the simulation process performed by sampling, and which adds to the total combined output sampling uncertainty. So far, this aleatoric part of uncertainty is minimized by running a sufficiently large number of Monte Carlo histories for each sample calculation, thus making its impact negligible as compared to the impact from sampling the epistemic uncertainties. Obviously, this process may cause high computational costs. The present paper shows that in many applications reliable epistemic uncertainty results can also be obtained with substantially lower computational effort by performing and analyzing two appropriately generated series of samples with much smaller number of Monte Carlo histories each. The method is applied along with the nuclear data uncertainty and sensitivity code package XSUSA in combination with the Monte Carlo transport code KENO-Va to various critical assemblies and a full scale reactor calculation. It is shown that the proposed method yields output uncertainties and sensitivities equivalent to the traditional approach, with a high reduction of computing time by factors of the magnitude of 100. (authors)

Uncertainty quantification of surface-water/groundwater exchange estimates in large wetland systems using Python

Science.gov (United States)

Hughes, J. D.; Metz, P. A.

2014-12-01

Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty. We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss

Groundwater detection monitoring system design under conditions of uncertainty

NARCIS (Netherlands)

Yenigül, N.B.

2006-01-01

Landfills represent a wide-spread and significant threat to groundwater quality. In this thesis a methodology was developed for the design of optimal groundwater moni-toring system design at landfill sites under conditions of uncertainty. First a decision analysis approach was presented for optimal

Implementation of unscented transform to estimate the uncertainty of a liquid flow standard system

Energy Technology Data Exchange (ETDEWEB)

Chun, Sejong; Choi, Hae-Man; Yoon, Byung-Ro; Kang, Woong [Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of)

2017-03-15

First-order partial derivatives of a mathematical model are an essential part of evaluating the measurement uncertainty of a liquid flow standard system according to the Guide to the expression of uncertainty in measurement (GUM). Although the GUM provides a straightforward method to evaluate the measurement uncertainty of volume flow rate, the first-order partial derivatives can be complicated. The mathematical model of volume flow rate in a liquid flow standard system has a cross-correlation between liquid density and buoyancy correction factor. This cross-correlation can make derivation of the first-order partial derivatives difficult. Monte Carlo simulation can be used as an alternative method to circumvent the difficulty in partial derivation. However, the Monte Carlo simulation requires large computational resources for a correct simulation because it considers the completeness issue whether an ideal or a real operator conducts an experiment to evaluate the measurement uncertainty. Thus, the Monte Carlo simulation needs a large number of samples to ensure that the uncertainty evaluation is as close to the GUM as possible. Unscented transform can alleviate this problem because unscented transform can be regarded as a Monte Carlo simulation with an infinite number of samples. This idea means that unscented transform considers the uncertainty evaluation with respect to the ideal operator. Thus, unscented transform can evaluate the measurement uncertainty the same as the uncertainty that the GUM provides.

Cost-effectiveness of insulin analogs from the perspective of the Brazilian public health system

Directory of Open Access Journals (Sweden)

Maurílio de Souza Cazarim

2017-11-01

Full Text Available ABSTRACT Human insulin is provided by the Brazilian Public Health System (BPHS for the treatment of diabetes, however, legal proceedings to acquire insulin analogs have burdened the BPHS health system. The aim of this study was to perform a cost-effectiveness analysis to compare insulin analogs and human insulins. This is a pharmacoeconomic study of cost-effectiveness. The direct medical cost related to insulin extracted from the Ministry of Health drug price list was considered. The clinical results, i.e. reduction in glycated hemoglobin (HbA1c, were extracted by meta-analysis. Different scenarios were structured to measure the uncertainties regarding the costs and reduction in HbA1c. Decision tree was developed for sensitivity of Incremental Cost Effectiveness Ratio (ICER. A total of fifteen scenarios were structured. Given the best-case scenario for the insulin analogs, the insulins aspart, lispro, glargine and detemir showed an ICER of R$ 1,768.59; R$ 3,308.54; R$ 11,718.75 and R$ 2,685.22, respectively. In all scenarios in which the minimum effectiveness was proposed, lispro, glargine and detemir were dominant strategies. Sensitivity analysis showed that the aspart had R$ 3,066.98 [95 % CI: 2339.22; 4418.53] and detemir had R$ 6,163.97 [95% CI: 3919.29; 11401.57] for incremental costs. We concluded there was evidence that the insulin aspart is the most cost-effective.

Cost-Effectiveness of Surgery, Stereotactic Body Radiation Therapy, and Systemic Therapy for Pulmonary Oligometastases

Energy Technology Data Exchange (ETDEWEB)

Lester-Coll, Nataniel H., E-mail: nataniel.lester-coll@yale.edu [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States); Rutter, Charles E.; Bledsoe, Trevor J. [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States); Goldberg, Sarah B. [Department of Medicine (Medical Oncology), Yale University School of Medicine, New Haven, Connecticut (United States); Decker, Roy H.; Yu, James B. [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States)

2016-06-01

Introduction: Pulmonary oligometastases have conventionally been managed with surgery and/or systemic therapy. However, given concerns about the high cost of systemic therapy and improvements in local treatment of metastatic cancer, the optimal cost-effective management of these patients is unclear. Therefore, we sought to assess the cost-effectiveness of initial management strategies for pulmonary oligometastases. Methods and Materials: A cost-effectiveness analysis using a Markov modeling approach was used to compare average cumulative costs, quality adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) among 3 initial disease management strategies: video-assisted thoracic surgery (VATS) wedge resection, stereotactic body radiation therapy (SBRT), and systemic therapy among 5 different cohorts of patient disease: (1) melanoma; (2) non-small cell lung cancer adenocarcinoma without an EGFR mutation (NSCLC AC); (3) NSCLC with an EGFR mutation (NSCLC EGFRm AC); (4) NSCLC squamous cell carcinoma (NSCLC SCC); and (5) colon cancer. One-way sensitivity analyses and probabilistic sensitivity analyses were performed to analyze uncertainty with regard to model parameters. Results: In the base case, SBRT was cost effective for melanoma, with costs/net QALYs of $467,787/0.85. In patients with NSCLC, the most cost-effective strategies were SBRT for AC ($156,725/0.80), paclitaxel/carboplatin for SCC ($123,799/0.48), and erlotinib for EGFRm AC ($147,091/1.90). Stereotactic body radiation therapy was marginally cost-effective for EGFRm AC compared to erlotinib with an incremental cost-effectiveness ratio of $126,303/QALY. For colon cancer, VATS wedge resection ($147,730/2.14) was the most cost-effective strategy. Variables with the greatest influence in the model were erlotinib-associated progression-free survival (EGFRm AC), toxicity (EGFRm AC), cost of SBRT (NSCLC SCC), and patient utilities (all histologies). Conclusions: Video-assisted thoracic

Joint cost of energy under an optimal economic policy of hybrid power systems subject to uncertainty

International Nuclear Information System (INIS)

Díaz, Guzmán; Planas, Estefanía; Andreu, Jon; Kortabarria, Iñigo

2015-01-01

Economical optimization of hybrid systems is usually performed by means of LCoE (levelized cost of energy) calculation. Previous works deal with the LCoE calculation of the whole hybrid system disregarding an important issue: the stochastic component of the system units must be jointly considered. This paper deals with this issue and proposes a new fast optimal policy that properly calculates the LCoE of a hybrid system and finds the lowest LCoE. This proposed policy also considers the implied competition among power sources when variability of gas and electricity prices are taken into account. Additionally, it presents a comparative between the LCoE of the hybrid system and its individual technologies of generation by means of a fast and robust algorithm based on vector logical computation. Numerical case analyses based on realistic data are presented that valuate the contribution of technologies in a hybrid power system to the joint LCoE. - Highlights: • We perform the LCoE calculation with the stochastic component jointly considered. • We propose a fast an optimal policy that minimizes the LCoE. • We compare the obtained LCoEs by means of a fast and robust algorithm. • We take into account the competition among gas prices and electricity prices

Cost-constrained optimal sampling for system identification in pharmacokinetics applications with population priors and nuisance parameters.

Science.gov (United States)

Sorzano, Carlos Oscars S; Pérez-De-La-Cruz Moreno, Maria Angeles; Burguet-Castell, Jordi; Montejo, Consuelo; Ros, Antonio Aguilar

2015-06-01

Pharmacokinetics (PK) applications can be seen as a special case of nonlinear, causal systems with memory. There are cases in which prior knowledge exists about the distribution of the system parameters in a population. However, for a specific patient in a clinical setting, we need to determine her system parameters so that the therapy can be personalized. This system identification is performed many times by measuring drug concentrations in plasma. The objective of this work is to provide an irregular sampling strategy that minimizes the uncertainty about the system parameters with a fixed amount of samples (cost constrained). We use Monte Carlo simulations to estimate the average Fisher's information matrix associated to the PK problem, and then estimate the sampling points that minimize the maximum uncertainty associated to system parameters (a minimax criterion). The minimization is performed employing a genetic algorithm. We show that such a sampling scheme can be designed in a way that is adapted to a particular patient and that it can accommodate any dosing regimen as well as it allows flexible therapeutic strategies. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Robust control of nonlinear MAGLEV suspension system with mismatched uncertainties via DOBC approach.

Science.gov (United States)

Yang, Jun; Zolotas, Argyrios; Chen, Wen-Hua; Michail, Konstantinos; Li, Shihua

2011-07-01

Robust control of a class of uncertain systems that have disturbances and uncertainties not satisfying "matching" condition is investigated in this paper via a disturbance observer based control (DOBC) approach. In the context of this paper, "matched" disturbances/uncertainties stand for the disturbances/uncertainties entering the system through the same channels as control inputs. By properly designing a disturbance compensation gain, a novel composite controller is proposed to counteract the "mismatched" lumped disturbances from the output channels. The proposed method significantly extends the applicability of the DOBC methods. Rigorous stability analysis of the closed-loop system with the proposed method is established under mild assumptions. The proposed method is applied to a nonlinear MAGnetic LEViation (MAGLEV) suspension system. Simulation shows that compared to the widely used integral control method, the proposed method provides significantly improved disturbance rejection and robustness against load variation. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Economic analysis of energy system considering the uncertainties of crude oil, natural gas and nuclear utilization employing stochastic dynamic programming

International Nuclear Information System (INIS)

Hasegawa, Keita; Komiyama, Ryoichi; Fujii, Yasumasa

2016-01-01

The paper presents an economic rationality analysis of power generation mix by stochastic dynamic programming considering fuel price uncertainties and supply disruption risks such as import disruption and nuclear power plant shutdown risk. The situation revolving around Japan's energy security adopted the past statistics, it cannot be applied to a quantitative analysis of future uncertainties. Further objective and quantitative evaluation methods are required in order to analyze Japan's energy system and make it more resilient in sight of long time scale. In this paper, the authors firstly develop the cost minimization model considering oil and natural gas price respectively by stochastic dynamic programming. Then, the authors show several premises of model and an example of result with related to crude oil stockpile, liquefied natural gas stockpile and nuclear power plant capacity. (author)

Multilevel Production Systems with Dependent Demand with Uncertainty of Lead Times

Directory of Open Access Journals (Sweden)

Haibatolah Sadeghi

2016-01-01

Full Text Available This study considers a multilevel assembly system with several components in each sublevel. It is assumed that actual lead time for all components is probabilistic; and periodic order quantity (POQ policy for ordering is utilized. If at a certain level a job is not received at the expected time, a delay is incurred at the delivery of production at this level and this may result in backorders of the finished product. It is assumed in this case that a fixed percentage of the shortage is backlogged and other sales are lost. In the real situation, some but not all customers will wait for backlogged components during a period of shortage, such as for fashionable commodities or high-tech products with the short product life cycle. The objective of this study is to find the planned lead time and periodicity for the total components in order to minimize the expected fixed ordering, holding, and partial backlogging costs for the finished product. In this study, it is assumed that a percentage of components at each level are scrap. A general mathematical model is suggested and the method developed can be used for optimization planned lead time and periodicity for such an MRP system under lead time uncertainties.

Managing Uncertainty in Water Infrastructure Design Using Info-gap Robustness

Science.gov (United States)

Irias, X.; Cicala, D.

2013-12-01

Info-gap theory, a tool for managing deep uncertainty, can be of tremendous value for design of water systems in areas of high seismic risk. Maintaining reliable water service in those areas is subject to significant uncertainties including uncertainty of seismic loading, unknown seismic performance of infrastructure, uncertain costs of innovative seismic-resistant construction, unknown costs to repair seismic damage, unknown societal impacts from downtime, and more. Practically every major earthquake that strikes a population center reveals additional knowledge gaps. In situations of such deep uncertainty, info-gap can offer advantages over traditional approaches, whether deterministic approaches that use empirical safety factors to address the uncertainties involved, or probabilistic methods that attempt to characterize various stochastic properties and target a compromise between cost and reliability. The reason is that in situations of deep uncertainty, it may not be clear what safety factor would be reasonable, or even if any safety factor is sufficient to address the uncertainties, and we may lack data to characterize the situation probabilistically. Info-gap is a tool that recognizes up front that our best projection of the future may be wrong. Thus, rather than seeking a solution that is optimal for that projection, info-gap seeks a solution that works reasonably well for all plausible conditions. In other words, info-gap seeks solutions that are robust in the face of uncertainty. Info-gap has been used successfully across a wide range of disciplines including climate change science, project management, and structural design. EBMUD is currently using info-gap to help it gain insight into possible solutions for providing reliable water service to an island community within its service area. The island, containing about 75,000 customers, is particularly vulnerable to water supply disruption from earthquakes, since it has negligible water storage and is

Risk aversion and uncertainty in cost-effectiveness analysis: the expected-utility, moment-generating function approach.

Science.gov (United States)

Elbasha, Elamin H

2005-05-01

The availability of patient-level data from clinical trials has spurred a lot of interest in developing methods for quantifying and presenting uncertainty in cost-effectiveness analysis (CEA). Although the majority has focused on developing methods for using sample data to estimate a confidence interval for an incremental cost-effectiveness ratio (ICER), a small strand of the literature has emphasized the importance of incorporating risk preferences and the trade-off between the mean and the variance of returns to investment in health and medicine (mean-variance analysis). This paper shows how the exponential utility-moment-generating function approach is a natural extension to this branch of the literature for modelling choices from healthcare interventions with uncertain costs and effects. The paper assumes an exponential utility function, which implies constant absolute risk aversion, and is based on the fact that the expected value of this function results in a convenient expression that depends only on the moment-generating function of the random variables. The mean-variance approach is shown to be a special case of this more general framework. The paper characterizes the solution to the resource allocation problem using standard optimization techniques and derives the summary measure researchers need to estimate for each programme, when the assumption of risk neutrality does not hold, and compares it to the standard incremental cost-effectiveness ratio. The importance of choosing the correct distribution of costs and effects and the issues related to estimation of the parameters of the distribution are also discussed. An empirical example to illustrate the methods and concepts is provided. Copyright 2004 John Wiley & Sons, Ltd

Uncertainty as Knowledge: Constraints on Policy Choices Provided by Analysis of Uncertainty

Science.gov (United States)

Lewandowsky, S.; Risbey, J.; Smithson, M.; Newell, B. R.

2012-12-01

Uncertainty forms an integral part of climate science, and it is often cited in connection with arguments against mitigative action. We argue that an analysis of uncertainty must consider existing knowledge as well as uncertainty, and the two must be evaluated with respect to the outcomes and risks associated with possible policy options. Although risk judgments are inherently subjective, an analysis of the role of uncertainty within the climate system yields two constraints that are robust to a broad range of assumptions. Those constraints are that (a) greater uncertainty about the climate system is necessarily associated with greater expected damages from warming, and (b) greater uncertainty translates into a greater risk of the failure of mitigation efforts. These ordinal constraints are unaffected by subjective or cultural risk-perception factors, they are independent of the discount rate, and they are independent of the magnitude of the estimate for climate sensitivity. The constraints mean that any appeal to uncertainty must imply a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.

A Conceptual Methodology for Assessing Acquisition Requirements Robustness against Technology Uncertainties

Science.gov (United States)

Chou, Shuo-Ju

2011-12-01

In recent years the United States has shifted from a threat-based acquisition policy that developed systems for countering specific threats to a capabilities-based strategy that emphasizes the acquisition of systems that provide critical national defense capabilities. This shift in policy, in theory, allows for the creation of an "optimal force" that is robust against current and future threats regardless of the tactics and scenario involved. In broad terms, robustness can be defined as the insensitivity of an outcome to "noise" or non-controlled variables. Within this context, the outcome is the successful achievement of defense strategies and the noise variables are tactics and scenarios that will be associated with current and future enemies. Unfortunately, a lack of system capability, budget, and schedule robustness against technology performance and development uncertainties has led to major setbacks in recent acquisition programs. This lack of robustness stems from the fact that immature technologies have uncertainties in their expected performance, development cost, and schedule that cause to variations in system effectiveness and program development budget and schedule requirements. Unfortunately, the Technology Readiness Assessment process currently used by acquisition program managers and decision-makers to measure technology uncertainty during critical program decision junctions does not adequately capture the impact of technology performance and development uncertainty on program capability and development metrics. The Technology Readiness Level metric employed by the TRA to describe program technology elements uncertainties can only provide a qualitative and non-descript estimation of the technology uncertainties. In order to assess program robustness, specifically requirements robustness, against technology performance and development uncertainties, a new process is needed. This process should provide acquisition program managers and decision

Uncertainty propagation and sensitivity analysis in system reliability assessment via unscented transformation

International Nuclear Information System (INIS)

Rocco Sanseverino, Claudio M.; Ramirez-Marquez, José Emmanuel

2014-01-01

The reliability of a system, notwithstanding it intended function, can be significantly affected by the uncertainty in the reliability estimate of the components that define the system. This paper implements the Unscented Transformation to quantify the effects of the uncertainty of component reliability through two approaches. The first approach is based on the concept of uncertainty propagation, which is the assessment of the effect that the variability of the component reliabilities produces on the variance of the system reliability. This assessment based on UT has been previously considered in the literature but only for system represented through series/parallel configuration. In this paper the assessment is extended to systems whose reliability cannot be represented through analytical expressions and require, for example, Monte Carlo Simulation. The second approach consists on the evaluation of the importance of components, i.e., the evaluation of the components that most contribute to the variance of the system reliability. An extension of the UT is proposed to evaluate the so called “main effects” of each component, as well to assess high order component interaction. Several examples with excellent results illustrate the proposed approach. - Highlights: • Simulation based approach for computing reliability estimates. • Computation of reliability variance via 2n+1 points. • Immediate computation of component importance. • Application to network systems

Robust framework for PET image reconstruction incorporating system and measurement uncertainties.

Directory of Open Access Journals (Sweden)

Huafeng Liu

Full Text Available In Positron Emission Tomography (PET, an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts.

Is the system really the solution? Operating costs in hospital systems.

Science.gov (United States)

Burns, Lawton Robert; McCullough, Jeffrey S; Wholey, Douglas R; Kruse, Gregory; Kralovec, Peter; Muller, Ralph

2015-06-01

Hospital system formation has recently accelerated. Executives emphasize scale economies that lower operating costs, a claim unsupported in academic research. Do systems achieve lower costs than freestanding facilities, and, if so, which system types? We test hypotheses about the relationship of cost with membership in systems, larger systems, and centralized and local hub-and-spoke systems. We also test whether these relationships have changed over time. Examining 4,000 U.S. hospitals during 1998 to 2010, we find no evidence that system members exhibit lower costs. However, members of smaller systems are lower cost than larger systems, and hospitals in centralized systems are lower cost than everyone else. There is no evidence that the system's spatial configuration is associated with cost, although national system hospitals exhibit higher costs. Finally, these results hold over time. We conclude that while systems in general may not be the solution to lower costs, some types of systems are. © The Author(s) 2015.

Calculation of the uncertainty of HP (10) evaluation for a thermoluminescent dosimetry system

International Nuclear Information System (INIS)

Ferreira, M.S.; Silva, E.R.; Mauricio, C.L.P.

2016-01-01

Full interpretation of dose assessment only can be performed when the uncertainty of the measurement is known. The aim of this study is to calculate the uncertainty of the TL dosimetry system of the LDF/IRD for evaluation of H P (10) for photons. It has been done by experimental measurements, extraction of information from documents and calculation of uncertainties based on ISO GUM. Energy and angular dependence is the most important source to the combined u c (y) and expanded (U) uncertainty. For 10 mSv, it was obtained u c (y) = 1,99 mSv and U = 3,98 mSv for 95% of coverage interval. (author)

Assessing Power System Stability Following Load Changes and Considering Uncertainty

Directory of Open Access Journals (Sweden)

D. V. Ngo

2018-04-01

Full Text Available An increase in load capacity during the operation of a power system usually causes voltage drop and leads to system instability, so it is necessary to monitor the effect of load changes. This article presents a method of assessing the power system stability according to the load node capacity considering uncertainty factors in the system. The proposed approach can be applied to large-scale power systems for voltage stability assessment in real-time.

Evaluation of cost estimates and calculation methods used by SKB

International Nuclear Information System (INIS)

1994-01-01

The Swedish Nuclear Fuel Management Co. (SKB) has estimated the costs for decommissioning the swedish nuclear power plants and managing the nuclear wastes in a 'traditional' manner i.e. by handling uncertainties through percentage additions. A 'normal' addition is used for uncertainties in specified technical systems. 'Extra' additions are used for systems uncertainties. An alternative method is suggested, using top-down principles for uncertainties, which should be applied successively, giving higher precision as the knowledge accumulates. This type of calculation can help project managers to identify and deal with areas common to different partial projects. A first step in this direction would be to perform sensitivity analyses for the most important calculation parameters. 21 refs

Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

Energy Technology Data Exchange (ETDEWEB)

Neto, Joao C. do L, E-mail: jcaldas@ufam.edu.br [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Costa Junior, Carlos T. da [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil); Bitar, Sandro D.B. [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Junior, Walter B. [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil)

2011-09-15

Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the {alpha}-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs. - Highlights: > A decision support system has been developed using SARIMA with fuzzy systems and optimizations tools. > It assists the decision-making process for planning the expansion in isolated thermoelectric systems. > The {alpha}-cut concept facilitated the evaluation of risks for the cost of electricity production. > Provides decisions using various forecasted interval for this cost with different membership values.

Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

International Nuclear Information System (INIS)

Neto, Joao C. do L; Costa Junior, Carlos T. da; Bitar, Sandro D.B.; Junior, Walter B.

2011-01-01

Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the α-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs. - Highlights: → A decision support system has been developed using SARIMA with fuzzy systems and optimizations tools. → It assists the decision-making process for planning the expansion in isolated thermoelectric systems. → The α-cut concept facilitated the evaluation of risks for the cost of electricity production. → Provides decisions using various forecasted interval for this cost with different membership values.

Facts and feelings : Framing effects in responses to uncertainties about high-voltage power lines

NARCIS (Netherlands)

de Vries, G.; de Bruijn, J.A.

2017-01-01

To ensure power supply security, electricity transmission system operators (TSOs) have to upscale high-voltage overhead power lines. However, upscaling frequently meets opposition. Opposition can be caused by uncertainties about risks and benefits and might lead to costly delays (Linder, 1995;

Cost system design and cost management in the Spanish public sector

OpenAIRE

Boned, Josep Lluís; Bagur, Llorenç; Tayles, Mike

2006-01-01

Cost systems have been shown to have developed considerably in recent years and activity-based costing (ABC) has been shown to be a contribution to cost management, particularly in service businesses. The public sector is composed to a very great extent of service functions, yet considerably less has been reported of the use of ABC to support cost management in this sector. In Spain, cost systems are essential for city councils as they are obliged to calculate the cost of the services subject...

Laser tracker TSPI uncertainty quantification via centrifuge trajectory

Science.gov (United States)

Romero, Edward; Paez, Thomas; Brown, Timothy; Miller, Timothy

2009-08-01

Sandia National Laboratories currently utilizes two laser tracking systems to provide time-space-position-information (TSPI) and high speed digital imaging of test units under flight. These laser trackers have been in operation for decades under the premise of theoretical accuracies based on system design and operator estimates. Advances in optical imaging and atmospheric tracking technology have enabled opportunities to provide more precise six degree of freedom measurements from these trackers. Applying these technologies to the laser trackers requires quantified understanding of their current errors and uncertainty. It was well understood that an assortment of variables contributed to laser tracker uncertainty but the magnitude of these contributions was not quantified and documented. A series of experiments was performed at Sandia National Laboratories large centrifuge complex to quantify TSPI uncertainties of Sandia National Laboratories laser tracker III. The centrifuge was used to provide repeatable and economical test unit trajectories of a test-unit to use for TSPI comparison and uncertainty analysis. On a centrifuge, testunits undergo a known trajectory continuously with a known angular velocity. Each revolution may represent an independent test, which may be repeated many times over for magnitudes of data practical for statistical analysis. Previously these tests were performed at Sandia's rocket sled track facility but were found to be costly with challenges in the measurement ground truth TSPI. The centrifuge along with on-board measurement equipment was used to provide known ground truth position of test units. This paper discusses the experimental design and techniques used to arrive at measures of laser tracker error and uncertainty.

Visualizing uncertainties in a storm surge ensemble data assimilation and forecasting system

KAUST Repository

Hollt, Thomas

2015-01-15

We present a novel integrated visualization system that enables the interactive visual analysis of ensemble simulations and estimates of the sea surface height and other model variables that are used for storm surge prediction. Coastal inundation, caused by hurricanes and tropical storms, poses large risks for today\\'s societies. High-fidelity numerical models of water levels driven by hurricane-force winds are required to predict these events, posing a challenging computational problem, and even though computational models continue to improve, uncertainties in storm surge forecasts are inevitable. Today, this uncertainty is often exposed to the user by running the simulation many times with different parameters or inputs following a Monte-Carlo framework in which uncertainties are represented as stochastic quantities. This results in multidimensional, multivariate and multivalued data, so-called ensemble data. While the resulting datasets are very comprehensive, they are also huge in size and thus hard to visualize and interpret. In this paper, we tackle this problem by means of an interactive and integrated visual analysis system. By harnessing the power of modern graphics processing units for visualization as well as computation, our system allows the user to browse through the simulation ensembles in real time, view specific parameter settings or simulation models and move between different spatial and temporal regions without delay. In addition, our system provides advanced visualizations to highlight the uncertainty or show the complete distribution of the simulations at user-defined positions over the complete time series of the prediction. We highlight the benefits of our system by presenting its application in a real-world scenario using a simulation of Hurricane Ike.

Estimation of the Influence of Power System Mathematical Model Parameter Uncertainty on PSS2A System Stabilizers

Directory of Open Access Journals (Sweden)

Adrian Nocoń

2015-09-01

Full Text Available This paper presents an analysis of the influence of uncertainty of power system mathematical model parameters on optimised parameters of PSS2A system stabilizers. Optimisation of power system stabilizer parameters was based on polyoptimisation (multi-criteria optimisation. Optimisation criteria were determined for disturbances occurring in a multi-machine power system, when taking into account transient waveforms associated with electromechanical swings (instantaneous power, angular speed and terminal voltage waveforms of generators. A genetic algorithm with floating-point encoding, tournament selection, mean crossover and perturbative mutations, modified for the needs of investigations, was used for optimisation. The impact of uncertainties on the quality of operation of power system stabilizers with optimised parameters has been evaluated using various deformation factors.

Electricity supply industry modelling for multiple objectives under demand growth uncertainty

International Nuclear Information System (INIS)

Heinrich, G.; Basson, L.; Howells, M.; Petrie, J.

2007-01-01

Appropriate energy-environment-economic (E3) modelling provides key information for policy makers in the electricity supply industry (ESI) faced with navigating a sustainable development path. Key challenges include engaging with stakeholder values and preferences, and exploring trade-offs between competing objectives in the face of underlying uncertainty. As a case study we represent the South African ESI using a partial equilibrium E3 modelling approach, and extend the approach to include multiple objectives under selected future uncertainties. This extension is achieved by assigning cost penalties to non-cost attributes to force the model's least-cost objective function to better satisfy non-cost criteria. This paper incorporates aspects of flexibility to demand growth uncertainty into each future expansion alternative by introducing stochastic programming with recourse into the model. Technology lead times are taken into account by the inclusion of a decision node along the time horizon where aspects of real options theory are considered within the planning process. Hedging in the recourse programming is automatically translated from being purely financial, to include the other attributes that the cost penalties represent. From a retrospective analysis of the cost penalties, the correct market signals, can be derived to meet policy goal, with due regard to demand uncertainty. (author)

Delay-Dependent Guaranteed Cost Control of an Interval System with Interval Time-Varying Delay

Directory of Open Access Journals (Sweden)

Xiao Min

2009-01-01

Full Text Available This paper concerns the problem of the delay-dependent robust stability and guaranteed cost control for an interval system with time-varying delay. The interval system with matrix factorization is provided and leads to less conservative conclusions than solving a square root. The time-varying delay is assumed to belong to an interval and the derivative of the interval time-varying delay is not a restriction, which allows a fast time-varying delay; also its applicability is broad. Based on the Lyapunov-Ktasovskii approach, a delay-dependent criterion for the existence of a state feedback controller, which guarantees the closed-loop system stability, the upper bound of cost function, and disturbance attenuation lever for all admissible uncertainties as well as out perturbation, is proposed in terms of linear matrix inequalities (LMIs. The criterion is derived by free weighting matrices that can reduce the conservatism. The effectiveness has been verified in a number example and the compute results are presented to validate the proposed design method.

Climate system properties determining the social cost of carbon

International Nuclear Information System (INIS)

Otto, Alexander; Allen, Myles R; Todd, Benjamin J; Bowerman, Niel; Frame, David J

2013-01-01

The choice of an appropriate scientific target to guide global mitigation efforts is complicated by uncertainties in the temperature response to greenhouse gas emissions. Much climate policy discourse has been based on the equilibrium global mean temperature increase following a concentration stabilization scenario. This is determined by the equilibrium climate sensitivity (ECS) which, in many studies, shows persistent, fat-tailed uncertainty. However, for many purposes, the equilibrium response is less relevant than the transient response. Here, we show that one prominent policy variable, the social cost of carbon (SCC), is generally better constrained by the transient climate response (TCR) than by the ECS. Simple analytic expressions show the SCC to be directly proportional to the TCR under idealized assumptions when the rate at which we discount future damage equals 2.8%. Using ensemble simulations of a simple climate model we find that knowing the true value of the TCR can reduce the relative uncertainty in the SCC substantially more, up to a factor of 3, than knowing the ECS under typical discounting assumptions. We conclude that the TCR, which is better constrained by observations, less subject to fat-tailed uncertainty and more directly related to the SCC, is generally preferable to the ECS as a single proxy for the climate response in SCC calculations. (letter)

Climate system properties determining the social cost of carbon

Science.gov (United States)

Otto, Alexander; Todd, Benjamin J.; Bowerman, Niel; Frame, David J.; Allen, Myles R.

2013-06-01

The choice of an appropriate scientific target to guide global mitigation efforts is complicated by uncertainties in the temperature response to greenhouse gas emissions. Much climate policy discourse has been based on the equilibrium global mean temperature increase following a concentration stabilization scenario. This is determined by the equilibrium climate sensitivity (ECS) which, in many studies, shows persistent, fat-tailed uncertainty. However, for many purposes, the equilibrium response is less relevant than the transient response. Here, we show that one prominent policy variable, the social cost of carbon (SCC), is generally better constrained by the transient climate response (TCR) than by the ECS. Simple analytic expressions show the SCC to be directly proportional to the TCR under idealized assumptions when the rate at which we discount future damage equals 2.8%. Using ensemble simulations of a simple climate model we find that knowing the true value of the TCR can reduce the relative uncertainty in the SCC substantially more, up to a factor of 3, than knowing the ECS under typical discounting assumptions. We conclude that the TCR, which is better constrained by observations, less subject to fat-tailed uncertainty and more directly related to the SCC, is generally preferable to the ECS as a single proxy for the climate response in SCC calculations.

Filmless versus film-based systems in radiographic examination costs: an activity-based costing method

Directory of Open Access Journals (Sweden)

Sase Yuji

2011-09-01

Full Text Available Abstract Background Since the shift from a radiographic film-based system to that of a filmless system, the change in radiographic examination costs and costs structure have been undetermined. The activity-based costing (ABC method measures the cost and performance of activities, resources, and cost objects. The purpose of this study is to identify the cost structure of a radiographic examination comparing a filmless system to that of a film-based system using the ABC method. Methods We calculated the costs of radiographic examinations for both a filmless and a film-based system, and assessed the costs or cost components by simulating radiographic examinations in a health clinic. The cost objects of the radiographic examinations included lumbar (six views, knee (three views, wrist (two views, and other. Indirect costs were allocated to cost objects using the ABC method. Results The costs of a radiographic examination using a filmless system are as follows: lumbar 2,085 yen; knee 1,599 yen; wrist 1,165 yen; and other 1,641 yen. The costs for a film-based system are: lumbar 3,407 yen; knee 2,257 yen; wrist 1,602 yen; and other 2,521 yen. The primary activities were "calling patient," "explanation of scan," "take photographs," and "aftercare" for both filmless and film-based systems. The cost of these activities cost represented 36.0% of the total cost for a filmless system and 23.6% of a film-based system. Conclusions The costs of radiographic examinations using a filmless system and a film-based system were calculated using the ABC method. Our results provide clear evidence that the filmless system is more effective than the film-based system in providing greater value services directly to patients.

Filmless versus film-based systems in radiographic examination costs: an activity-based costing method.

Science.gov (United States)

Muto, Hiroshi; Tani, Yuji; Suzuki, Shigemasa; Yokooka, Yuki; Abe, Tamotsu; Sase, Yuji; Terashita, Takayoshi; Ogasawara, Katsuhiko

2011-09-30

Since the shift from a radiographic film-based system to that of a filmless system, the change in radiographic examination costs and costs structure have been undetermined. The activity-based costing (ABC) method measures the cost and performance of activities, resources, and cost objects. The purpose of this study is to identify the cost structure of a radiographic examination comparing a filmless system to that of a film-based system using the ABC method. We calculated the costs of radiographic examinations for both a filmless and a film-based system, and assessed the costs or cost components by simulating radiographic examinations in a health clinic. The cost objects of the radiographic examinations included lumbar (six views), knee (three views), wrist (two views), and other. Indirect costs were allocated to cost objects using the ABC method. The costs of a radiographic examination using a filmless system are as follows: lumbar 2,085 yen; knee 1,599 yen; wrist 1,165 yen; and other 1,641 yen. The costs for a film-based system are: lumbar 3,407 yen; knee 2,257 yen; wrist 1,602 yen; and other 2,521 yen. The primary activities were "calling patient," "explanation of scan," "take photographs," and "aftercare" for both filmless and film-based systems. The cost of these activities cost represented 36.0% of the total cost for a filmless system and 23.6% of a film-based system. The costs of radiographic examinations using a filmless system and a film-based system were calculated using the ABC method. Our results provide clear evidence that the filmless system is more effective than the film-based system in providing greater value services directly to patients.

H∞ Loop Shaping Control of Input Saturated Systems with Norm-Bounded Parametric Uncertainty

Directory of Open Access Journals (Sweden)

Renan Lima Pereira

2015-01-01

Full Text Available This paper proposes a gain-scheduling control design strategy for a class of linear systems with the presence of both input saturation constraints and norm-bounded parametric uncertainty. LMI conditions are derived in order to obtain a gain-scheduled controller that ensures the robust stability and performance of the closed loop system. The main steps to obtain such a controller are given. Differently from other gain-scheduled approaches in the literature, this one focuses on the problem of H∞ loop shaping control design with input saturation nonlinearity and norm-bounded uncertainty to reduce the effect of the disturbance input on the controlled outputs. Here, the design problem has been formulated in the four-block H∞ synthesis framework, in which it is possible to describe the parametric uncertainty and the input saturation nonlinearity as perturbations to normalized coprime factors of the shaped plant. As a result, the shaped plant is represented as a linear parameter-varying (LPV system while the norm-bounded uncertainty and input saturation are incorporated. This procedure yields a linear parameter-varying structure for the controller that ensures the stability of the polytopic LPV shaped plant from the vertex property. Finally, the effectiveness of the method is illustrated through application to a physical system: a VTOL “vertical taking-off landing” helicopter.

Development of electrical efficiency measurement techniques for 10 kW-class SOFC system: Part II. Uncertainty estimation

International Nuclear Information System (INIS)

Tanaka, Yohei; Momma, Akihiko; Kato, Ken; Negishi, Akira; Takano, Kiyonami; Nozaki, Ken; Kato, Tohru

2009-01-01

Uncertainty of electrical efficiency measurement was investigated for a 10 kW-class SOFC system using town gas. Uncertainty of heating value measured by the gas chromatography method on a mole base was estimated as ±0.12% at 95% level of confidence. Micro-gas chromatography with/without CH 4 quantification may be able to reduce uncertainty of measurement. Calibration and uncertainty estimation methods are proposed for flow-rate measurement of town gas with thermal mass-flow meters or controllers. By adequate calibrations for flowmeters, flow rate of town gas or natural gas at 35 standard litters per minute can be measured within relative uncertainty ±1.0% at 95 % level of confidence. Uncertainty of power measurement can be as low as ±0.14% when a precise wattmeter is used and calibrated properly. It is clarified that electrical efficiency for non-pressurized 10 kW-class SOFC systems can be measured within ±1.0% relative uncertainty at 95% level of confidence with the developed techniques when the SOFC systems are operated relatively stably

FORMATION OF THE ENTERPRISE COSTS MANAGEMENT SYSTEM

Directory of Open Access Journals (Sweden)

Borysiuk Iryna

2018-01-01

Full Text Available Introduction. The paper deals with the actual issues of formation of the enterprise management system costs, because in the conditions of an unstable market environment the financial performance depends on the efficiency of the cost management system, competitiveness, financial sustainability and investment attractiveness of any subject of economic activity. Purpose of the article is consolidation of approaches to cost management, theoretical substantiation and development of recommendations regarding the formation of the enterprise cost management system. Results. Development of an enterprise cost management system based on research on the essence and cost management approaches. The goals, tasks, principles, methods, tools, functions and main elements of the cost management system were determined, factors of the external and internal environment of the enterprise, that affect the system of its costs management. Conclusions. Formation of integrated cost management system ensures the successful company operation on the market, production of competitive products based on costs and prices optimization and making a profit, increase of the reasonableness of making managerial decisions.

Uncertainty, joint uncertainty, and the quantum uncertainty principle

International Nuclear Information System (INIS)

Narasimhachar, Varun; Poostindouz, Alireza; Gour, Gilad

2016-01-01

Historically, the element of uncertainty in quantum mechanics has been expressed through mathematical identities called uncertainty relations, a great many of which continue to be discovered. These relations use diverse measures to quantify uncertainty (and joint uncertainty). In this paper we use operational information-theoretic principles to identify the common essence of all such measures, thereby defining measure-independent notions of uncertainty and joint uncertainty. We find that most existing entropic uncertainty relations use measures of joint uncertainty that yield themselves to a small class of operational interpretations. Our notion relaxes this restriction, revealing previously unexplored joint uncertainty measures. To illustrate the utility of our formalism, we derive an uncertainty relation based on one such new measure. We also use our formalism to gain insight into the conditions under which measure-independent uncertainty relations can be found. (paper)

Robust uncertainty evaluation for system identification on distributed wireless platforms

Science.gov (United States)

Crinière, Antoine; Döhler, Michael; Le Cam, Vincent; Mevel, Laurent

2016-04-01

Health monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on

Optimized production planning model for a multi-plant cultivation system under uncertainty

Science.gov (United States)

Ke, Shunkui; Guo, Doudou; Niu, Qingliang; Huang, Danfeng

2015-02-01

An inexact multi-constraint programming model under uncertainty was developed by incorporating a production plan algorithm into the crop production optimization framework under the multi-plant collaborative cultivation system. In the production plan, orders from the customers are assigned to a suitable plant under the constraints of plant capabilities and uncertainty parameters to maximize profit and achieve customer satisfaction. The developed model and solution method were applied to a case study of a multi-plant collaborative cultivation system to verify its applicability. As determined in the case analysis involving different orders from customers, the period of plant production planning and the interval between orders can significantly affect system benefits. Through the analysis of uncertain parameters, reliable and practical decisions can be generated using the suggested model of a multi-plant collaborative cultivation system.

Risk Assessment and Decision-Making under Uncertainty in Tunnel and Underground Engineering

Directory of Open Access Journals (Sweden)

Yuanpu Xia

2017-10-01

Full Text Available The impact of uncertainty on risk assessment and decision-making is increasingly being prioritized, especially for large geotechnical projects such as tunnels, where uncertainty is often the main source of risk. Epistemic uncertainty, which can be reduced, is the focus of attention. In this study, the existing entropy-risk decision model is first discussed and analyzed, and its deficiencies are improved upon and overcome. Then, this study addresses the fact that existing studies only consider parameter uncertainty and ignore the influence of the model uncertainty. Here, focus is on the issue of model uncertainty and differences in risk consciousness with different decision-makers. The utility theory is introduced in the model. Finally, a risk decision model is proposed based on the sensitivity analysis and the tolerance cost, which can improve decision-making efficiency. This research can provide guidance or reference for the evaluation and decision-making of complex systems engineering problems, and indicate a direction for further research of risk assessment and decision-making issues.

Statistical uncertainty of response characteristic of building-appendage system for spectrum-compatible artificial earthquake motion

International Nuclear Information System (INIS)

Kurosaki, A.; Kozeki, M.

1981-01-01

Spectrum-compatible artificial time histories of ground motions are frequently used for the seismic design of nuclear power plant structures and components. However, statistical uncertainty of the responses of building structures and mechanical components mounted on the building (building-appendage systems) are anticipated, since an artificial time history is no more than one sample from a population of such time histories that match a specified design response spectrum. This uncertainty may spoil the reliability of the seismic design and therefore the extent of the uncertainty of the response characteristic is a matter of great concern. In this paper, above-mentioned uncertainty of the dynamic response characteristics of the building-appendage system to the spectrum-compatible artificial earthquake is investigated. (orig./RW)

Maintenance cost models in deregulated power systems under opportunity costs

International Nuclear Information System (INIS)

Al-Arfaj, K.; Dahal, K.; Azaiez, M.N.

2007-01-01

In a centralized power system, the operator is responsible for scheduling maintenance. There are different types of maintenance, including corrective maintenance; predictive maintenance; preventive maintenance; and reliability-centred maintenance. The main cause of power failures is poor maintenance. As such, maintenance costs play a significant role in deregulated power systems. They include direct costs associated with material and labor costs as well as indirect costs associated with spare parts inventory, shipment, test equipment, indirect labor, opportunity costs and cost of failure. In maintenance scheduling and planning, the cost function is the only component of the objective function. This paper presented the results of a study in which different components of maintenance costs were modeled. The maintenance models were formulated as an optimization problem with single and multiple objectives and a set of constraints. The maintenance costs models could be used to schedule the maintenance activities of power generators more accurately and to identify the best maintenance strategies over a period of time as they consider failure and opportunity costs in a deregulated environment. 32 refs., 4 tabs., 4 figs

Optimum design of forging process parameters and preform shape under uncertainties

International Nuclear Information System (INIS)

Repalle, Jalaja; Grandhi, Ramana V.

2004-01-01

Forging is a highly complex non-linear process that is vulnerable to various uncertainties, such as variations in billet geometry, die temperature, material properties, workpiece and forging equipment positional errors and process parameters. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion and production risk. Identifying the sources of uncertainties, quantifying and controlling them will reduce risk in the manufacturing environment, which will minimize the overall cost of production. In this paper, various uncertainties that affect forging tool life and preform design are identified, and their cumulative effect on the forging process is evaluated. Since the forging process simulation is computationally intensive, the response surface approach is used to reduce time by establishing a relationship between the system performance and the critical process design parameters. Variability in system performance due to randomness in the parameters is computed by applying Monte Carlo Simulations (MCS) on generated Response Surface Models (RSM). Finally, a Robust Methodology is developed to optimize forging process parameters and preform shape. The developed method is demonstrated by applying it to an axisymmetric H-cross section disk forging to improve the product quality and robustness

[Method for optimal sensor placement in water distribution systems with nodal demand uncertainties].

Science.gov (United States)

Liu, Shu-Ming; Wu, Xue; Ouyang, Le-Yan

2013-08-01

The notion of identification fitness was proposed for optimizing sensor placement in water distribution systems. Nondominated Sorting Genetic Algorithm II was used to find the Pareto front between minimum overlap of possible detection times of two events and the best probability of detection, taking nodal demand uncertainties into account. This methodology was applied to an example network. The solutions show that the probability of detection and the number of possible locations are not remarkably affected by nodal demand uncertainties, but the sources identification accuracy declines with nodal demand uncertainties.

A new costing model in hospital management: time-driven activity-based costing system.

Science.gov (United States)

Öker, Figen; Özyapıcı, Hasan

2013-01-01

Traditional cost systems cause cost distortions because they cannot meet the requirements of today's businesses. Therefore, a new and more effective cost system is needed. Consequently, time-driven activity-based costing system has emerged. The unit cost of supplying capacity and the time needed to perform an activity are the only 2 factors considered by the system. Furthermore, this system determines unused capacity by considering practical capacity. The purpose of this article is to emphasize the efficiency of the time-driven activity-based costing system and to display how it can be applied in a health care institution. A case study was conducted in a private hospital in Cyprus. Interviews and direct observations were used to collect the data. The case study revealed that the cost of unused capacity is allocated to both open and laparoscopic (closed) surgeries. Thus, by using the time-driven activity-based costing system, managers should eliminate the cost of unused capacity so as to obtain better results. Based on the results of the study, hospital management is better able to understand the costs of different surgeries. In addition, managers can easily notice the cost of unused capacity and decide how many employees to be dismissed or directed to other productive areas.

Evaluation of the uncertainty in an EBT3 film dosimetry system utilizing net optical density.

Science.gov (United States)

Marroquin, Elsa Y León; Herrera González, José A; Camacho López, Miguel A; Barajas, José E Villarreal; García-Garduño, Olivia A

2016-09-08

Radiochromic film has become an important tool to verify dose distributions for intensity-modulated radiotherapy (IMRT) and quality assurance (QA) procedures. A new radiochromic film model, EBT3, has recently become available, whose composition and thickness of the sensitive layer are the same as those of previous EBT2 films. However, a matte polyester layer was added to EBT3 to prevent the formation of Newton's rings. Furthermore, the symmetrical design of EBT3 allows the user to eliminate side-orientation dependence. This film and the flatbed scanner, Epson Perfection V750, form a dosimetry system whose intrinsic characteristics were studied in this work. In addition, uncertainties associated with these intrinsic characteristics and the total uncertainty of the dosimetry system were determined. The analysis of the response of the radiochromic film (net optical density) and the fitting of the experimental data to a potential function yielded an uncertainty of 2.6%, 4.3%, and 4.1% for the red, green, and blue channels, respectively. In this work, the dosimetry system presents an uncertainty in resolving the dose of 1.8% for doses greater than 0.8 Gy and less than 6 Gy for red channel. The films irradiated between 0 and 120 Gy show differences in the response when scanned in portrait or landscape mode; less uncertainty was found when using the portrait mode. The response of the film depended on the position on the bed of the scanner, contributing an uncertainty of 2% for the red, 3% for the green, and 4.5% for the blue when placing the film around the center of the bed of scanner. Furthermore, the uniformity and reproducibility radiochromic film and reproducibility of the response of the scanner contribute less than 1% to the overall uncertainty in dose. Finally, the total dose uncertainty was 3.2%, 4.9%, and 5.2% for red, green, and blue channels, respectively. The above uncertainty values were obtained by mini-mizing the contribution to the total dose uncertainty

A comparative analysis of methods to represent uncertainty in estimating the cost of constructing wastewater treatment plants.

Science.gov (United States)

Chen, Ho-Wen; Chang, Ni-Bin

2002-08-01

Prediction of construction cost of wastewater treatment facilities could be influential for the economic feasibility of various levels of water pollution control programs. However, construction cost estimation is difficult to precisely evaluate in an uncertain environment and measured quantities are always burdened with different types of cost structures. Therefore, an understanding of the previous development of wastewater treatment plants and of the related construction cost structures of those facilities becomes essential for dealing with an effective regional water pollution control program. But deviations between the observed values and the estimated values are supposed to be due to measurement errors only in the conventional regression models. The inherent uncertainties of the underlying cost structure, where the human estimation is influential, are rarely explored. This paper is designed to recast a well-known problem of construction cost estimation for both domestic and industrial wastewater treatment plants via a comparative framework. Comparisons were made for three technologies of regression analyses, including the conventional least squares regression method, the fuzzy linear regression method, and the newly derived fuzzy goal regression method. The case study, incorporating a complete database with 48 domestic wastewater treatment plants and 29 industrial wastewater treatment plants being collected in Taiwan, implements such a cost estimation procedure in an uncertain environment. Given that the fuzzy structure in regression estimation may account for the inherent human complexity in cost estimation, the fuzzy goal regression method does exhibit more robust results in terms of some criteria. Moderate economy of scale exists in constructing both the domestic and industrial wastewater treatment plants. Findings indicate that the optimal size of a domestic wastewater treatment plant is approximately equivalent to 15,000 m3/day (CMD) and higher in Taiwan

SWEPP PAN assay system uncertainty analysis: Passive mode measurements of graphite waste

International Nuclear Information System (INIS)

Blackwood, L.G.; Harker, Y.D.; Meachum, T.R.; Yoon, Woo Y.

1997-07-01

The Idaho National Engineering and Environmental Laboratory is being used as a temporary storage facility for transuranic waste generated by the U.S. Nuclear Weapons program at the Rocky Flats Plant (RFP) in Golden, Colorado. Currently, there is a large effort in progress to prepare to ship this waste to the Waste Isolation Pilot Plant (WIPP) in Carlsbad, New Mexico. In order to meet the TRU Waste Characterization Quality Assurance Program Plan nondestructive assay compliance requirements and quality assurance objectives, it is necessary to determine the total uncertainty of the radioassay results produced by the Stored Waste Examination Pilot Plant (SWEPP) Passive Active Neutron (PAN) radioassay system. To this end a modified statistical sampling and verification approach has been developed to determine the total uncertainty of a PAN measurement. In this approach the total performance of the PAN nondestructive assay system is simulated using computer models of the assay system and the resultant output is compared with the known input to assess the total uncertainty. This paper is one of a series of reports quantifying the results of the uncertainty analysis of the PAN system measurements for specific waste types and measurement modes. In particular this report covers passive mode measurements of weapons grade plutonium-contaminated graphite molds contained in 208 liter drums (waste code 300). The validity of the simulation approach is verified by comparing simulated output against results from measurements using known plutonium sources and a surrogate graphite waste form drum. For actual graphite waste form conditions, a set of 50 cases covering a statistical sampling of the conditions exhibited in graphite wastes was compiled using a Latin hypercube statistical sampling approach

Uncertainty evaluation of reliability of shutdown system of a medium size fast breeder reactor

Energy Technology Data Exchange (ETDEWEB)

Zeliang, Chireuding; Singh, Om Pal, E-mail: singhop@iitk.ac.in; Munshi, Prabhat

2016-11-15

Highlights: • Uncertainty analysis of reliability of Shutdown System is carried out. • Monte Carlo method of sampling is used. • The effect of various reliability improvement measures of SDS are accounted. - Abstract: In this paper, results are presented on the uncertainty evaluation of the reliability of Shutdown System (SDS) of a Medium Size Fast Breeder Reactor (MSFBR). The reliability analysis results are of Kumar et al. (2005). The failure rate of the components of SDS are taken from International literature and it is assumed that these follow log-normal distribution. Fault tree method is employed to propagate the uncertainty in failure rate from components level to shutdown system level. The beta factor model is used to account different extent of diversity. The Monte Carlo sampling technique is used for the analysis. The results of uncertainty analysis are presented in terms of the probability density function, cumulative distribution function, mean, variance, percentile values, confidence intervals, etc. It is observed that the spread in the probability distribution of SDS failure rate is less than SDS components failure rate and ninety percent values of the failure rate of SDS falls below the target value. As generic values of failure rates are used, sensitivity analysis is performed with respect to failure rate of control and safety rods and beta factor. It is discovered that a large increase in failure rate of SDS rods is not carried to SDS system failure proportionately. The failure rate of SDS is very sensitive to the beta factor of common cause failure between the two systems of SDS. The results of the study provide insight in the propagation of uncertainty in the failure rate of SDS components to failure rate of shutdown system.

Distribution system costs associated with the deployment of photovoltaic systems

Energy Technology Data Exchange (ETDEWEB)

Horowitz, Kelsey A. W.; Palmintier, Bryan; Mather, Barry; Denholm, Paul

2018-07-01

The broadening of our energy system to include increasing amounts of wind and solar has led to significant debate about the total costs and benefits associated with different types of generators - with potentially far-reaching policy implications. This has included debate about the cost associated with integrating these generators onto the electric grid. For photovoltaics (PV), this encompasses costs incurred on both the bulk power and distribution systems, as well as the value provided to them. These costs and benefits, in particular those associated with integrating PV onto the distribution system, are not well understood. We seek to advance the state of understanding of 'grid integration costs' for the distribution system by reviewing prior literature and outlining a transparent, bottom-up approach that can be used to calculate these costs. We provide a clear delineation of costs to integrate PV in to the distribution system within the larger context of total costs and benefits associated with PV generators. We emphasize that these costs are situationally dependent, and that a single 'cost of integration' cannot be obtained. We additionally emphasize that benefits must be considered when evaluating the competitiveness of the technology in a given situation.

Cost-effectiveness of nitrogen mitigation by alternative household wastewater management technologies.

Science.gov (United States)

Wood, Alison; Blackhurst, Michael; Hawkins, Troy; Xue, Xiaobo; Ashbolt, Nicholas; Garland, Jay

2015-03-01

Household wastewater, especially from conventional septic systems, is a major contributor to nitrogen pollution. Alternative household wastewater management technologies provide similar sewerage management services but their life cycle costs and nitrogen flow implications remain uncertain. This paper addresses two key questions: (1) what are the total costs, nitrogen mitigation potential, and cost-effectiveness of a range of conventional and alternative municipal wastewater treatment technologies, and (2) what uncertainties influence these outcomes and how can we improve our understanding of these technologies? We estimate a household nitrogen mass balance for various household wastewater treatment systems and combine this mass balance with life cycle cost assessment to calculate the cost-effectiveness of nitrogen mitigation, which we define as nitrogen removed from the local watershed. We apply our methods to Falmouth, MA, where failing septic systems have caused heightened eutrophication in local receiving water bodies. We find that flushing and dry (composting) urine-diversion toilets paired with conventional septic systems for greywater management demonstrate the lowest life cycle cost and highest cost-effectiveness (dollars per kilogram of nitrogen removed from the watershed). Composting toilets are also attractive options in some cases, particularly best-case nitrogen mitigation. Innovative/advanced septic systems designed for high-level nitrogen removal are cost-competitive options for newly constructed homes, except at their most expensive. A centralized wastewater treatment plant is the most expensive and least cost-effective option in all cases. Using a greywater recycling system with any treatment technology increases the cost without adding any nitrogen removal benefits. Sensitivity analysis shows that these results are robust considering a range of cases and uncertainties. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Certainty Equivalence M-MRAC for Systems with Unmatched Uncertainties

Science.gov (United States)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

The paper presents a certainty equivalence state feedback indirect adaptive control design method for the systems of any relative degree with unmatched uncertainties. The approach is based on the parameter identification (estimation) model, which is completely separated from the control design and is capable of producing parameter estimates as fast as the computing power allows without generating high frequency oscillations. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters.

Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

Directory of Open Access Journals (Sweden)

Shaopeng Zhong

2013-01-01

Full Text Available Focusing on the first-best marginal cost pricing (MCP in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1 the effect of stochastic perception error (SPE on the perceived travel time distribution and the components of road toll; (2 the effect of road toll on the actual travel time distribution and its reliability measures; (3 the effect of road toll on the total network travel time distribution and its statistics; and (4 the effect of travel demand level and the value of reliability (VoR level on the components of road toll.

Evaluation of Uniform Cost Accounting System to Fully Capture Depot Level Repair Costs.

Science.gov (United States)

1985-12-01

RD-RI65 522 EVALUATION OF UNIFORM COST ACCOUNTING SYSTEM TO FULLY i/I CAPTURE DEPOT LEVEL REPAIR COSTS (U) NAVAL POSTGRADUATE SCHOOL MONTEREY CA D R...8217.LECTE B ,- THESIS EVALUATION OF UNIFORM COST ACCOUNTING SYSTEM 0TO FULLY CAPTURE DEPOT LEVEL REPAIR COSTS Jby __jDavid Richmond O’Brien lj,,, December...Include Security Classification) EVALUATION OF UNIFORM COST ACCOUNTING SYSTEM TO FULLY CAPTURE DEPOT LEVEL REPAIR COSTS 12 PERSONAL AUTHOR(S) O’Brien- David

Integrated waste management system costs in a MPC system

International Nuclear Information System (INIS)

Supko, E.M.

1995-01-01

The impact on system costs of including a centralized interim storage facility as part of an integrated waste management system based on multi-purpose canister (MPC) technology was assessed in analyses by Energy Resources International, Inc. A system cost savings of $1 to $2 billion occurs if the Department of Energy begins spent fuel acceptance in 1998 at a centralized interim storage facility. That is, the savings associated with decreased utility spent fuel management costs will be greater than the cost of constructing and operating a centralized interim storage facility

Uncertainties in key low carbon power generation technologies - Implication for UK decarbonisation targets

International Nuclear Information System (INIS)

Kannan, R.

2009-01-01

The UK government's economy-wide 60% carbon dioxide reduction target by 2050 requires a paradigm shift in the whole energy system. Numerous analytical studies have concluded that the power sector is a critical contributor to a low carbon energy system, and electricity generation has dominated the policy discussion on UK decarbonisation scenarios. However, range of technical, social and market challenges, combined with alternate market investment strategies mean that large scale deployment of key classes of low carbon electricity technologies is fraught with uncertainty. The UK MARKAL energy systems model has been used to investigate these long-term uncertainties in key electricity generation options. A range of power sector specific parametric sensitivities have been performed under a 'what-if' framework to provide a systematic exploration of least-cost energy system configurations under a broad, integrated set of input assumptions. In this paper results of six sensitivities, via restricted investments in key low carbon technologies to reflect their technical and political uncertainties, and an alternate investment strategies from perceived risk and other barriers, have been presented. (author)

Uncertainty in air quality observations using low-cost sensors

Science.gov (United States)

Castell, Nuria; Dauge, Franck R.; Dongol, Rozina; Vogt, Matthias; Schneider, Philipp

2016-04-01

Air pollution poses a threat to human health, and the WHO has classified air pollution as the world's largest single environmental health risk. In Europe, the majority of the population lives in areas where air quality levels frequently exceed WHO's ambient air quality guidelines. The emergence of low-cost, user-friendly and very compact air pollution platforms allowing observations at high spatial resolution in near real-time, provides us with new opportunities to simultaneously enhance existing monitoring systems as well as enable citizens to engage in more active environmental monitoring (citizen science). However the data sets generated by low-cost sensors show often questionable data quality. For many sensors, neither their error characteristics nor how their measurement capability holds up over time or through a range of environmental conditions, have been evaluated. We have conducted an exhaustive evaluation of the commercial low-cost platform AQMesh (measuring NO, NO2, CO, O3, PM10 and PM2.5) in laboratory and in real-world conditions in the city of Oslo (Norway). Co-locations in field of 24 platforms were conducted over a 6 month period (April to September 2015) allowing to characterize the temporal variability in the performance. Additionally, the field performance included the characterization on different monitoring urban monitoring sites characteristic of both traffic and background conditions. All the evaluations have been conducted against CEN reference method analyzers maintained according to the Norwegian National Reference Laboratory quality system. The results show clearly that a good performance in laboratory does not imply similar performance in real-world outdoor conditions. Moreover, laboratory calibration is not suitable for subsequent measurements in urban environments. In order to reduce the errors, sensors require on-site field calibration. Even after such field calibration, the platforms show a significant variability in the performance

Robust Economic MPC for a Power Management Scenario with Uncertainties

DEFF Research Database (Denmark)

Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp

2011-01-01

This paper presents a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with Economic Model Predictive Control (MPC). Hereby the performance of the controller is robustyfied in the presence of both model and forecast uncertainties. Economic MPC is a receding...... horizon controller that minimizes an economic objective function and we have previously demonstrated its usage to include a refrigeration system as a controllable power consumer with a portfolio of power generators such that total cost is minimized. The main focus for our work is the power management...... an optimal solution to an infeasible or very expensive solution. The main contribution of this paper is the Finite Impulse Response (FIR) formulation of the system models allowing us to describe and handle model uncertainties in the framework of probabilistic constraints. Our new solution using this setup...

Incorporating outcome uncertainty and prior outcome beliefs in stated preferences

DEFF Research Database (Denmark)

Lundhede, Thomas; Jacobsen, Jette Bredahl; Hanley, Nick

2015-01-01

Stated preference studies tell respondents that policies create environmental changes with varying levels of uncertainty. However, respondents may include their own a priori assessments of uncertainty when making choices among policy options. Using a choice experiment eliciting respondents......’ preferences for conservation policies under climate change, we find that higher outcome uncertainty reduces utility. When accounting for endogeneity, we find that prior beliefs play a significant role in this cost of uncertainty. Thus, merely stating “objective” levels of outcome uncertainty...

Establishment of design and performance requirements using cost and systems analysis

International Nuclear Information System (INIS)

Waganer, L.M.; Carosella, L.A.; Defreece, D.A.

1977-01-01

The current uncertainty in design approach and performance requirements for a commercial fusion power plant poses a problem for the designer in configuring the plant and for the utilities in analyzing the attractiveness of a future fusion power plant. To provide direction and insight in this area, a systems analysis model was constructed at McDonnell Douglas, utilizing fusion subsystem algorithms with subsystem cost estimating relationships into a self-consistent computerized model for several fusion reactor concepts. Cost estimating data has been compiled by utilizing McDonnell Douglas' experience in fabricating large, complex metal assemblies and soliciting the accumulated store of knowledge in existing power plants and new emerging technologies such as the Clinch River Breeder Reactor. Using the computer model, sensitivities to plasma, reactor and plant parameters are a few of the options that have been evaluated to yield recommended concepts/techniques/solutions. This is a very beneficial tool in assessing the impact of the fusion reactor on the electrical power community and charting the optimum developmental approach

Air Force Reusable Booster System: A Quick-look, Design Focused Modeling and Cost Analysis Study

Science.gov (United States)

Zapata, Edgar

2011-01-01

. Design and technology features bear special relevance to early program research and development directions. Given the uncertainties involved in both their actual performance promise and their relation to costs of operational systems, this later relationship is also given special attention.

Reusable launch vehicle model uncertainties impact analysis

Science.gov (United States)

Chen, Jiaye; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

Reusable launch vehicle(RLV) has the typical characteristics of complex aerodynamic shape and propulsion system coupling, and the flight environment is highly complicated and intensely changeable. So its model has large uncertainty, which makes the nominal system quite different from the real system. Therefore, studying the influences caused by the uncertainties on the stability of the control system is of great significance for the controller design. In order to improve the performance of RLV, this paper proposes the approach of analyzing the influence of the model uncertainties. According to the typical RLV, the coupling dynamic and kinematics models are built. Then different factors that cause uncertainties during building the model are analyzed and summed up. After that, the model uncertainties are expressed according to the additive uncertainty model. Choosing the uncertainties matrix's maximum singular values as the boundary model, and selecting the uncertainties matrix's norm to show t how much the uncertainty factors influence is on the stability of the control system . The simulation results illustrate that the inertial factors have the largest influence on the stability of the system, and it is necessary and important to take the model uncertainties into consideration before the designing the controller of this kind of aircraft( like RLV, etc).

Delay-Dependent Guaranteed Cost H∞ Control of an Interval System with Interval Time-Varying Delay

Directory of Open Access Journals (Sweden)

Zhongke Shi

2009-01-01

Full Text Available This paper concerns the problem of the delay-dependent robust stability and guaranteed cost H∞ control for an interval system with time-varying delay. The interval system with matrix factorization is provided and leads to less conservative conclusions than solving a square root. The time-varying delay is assumed to belong to an interval and the derivative of the interval time-varying delay is not a restriction, which allows a fast time-varying delay; also its applicability is broad. Based on the Lyapunov-Ktasovskii approach, a delay-dependent criterion for the existence of a state feedback controller, which guarantees the closed-loop system stability, the upper bound of cost function, and disturbance attenuation lever for all admissible uncertainties as well as out perturbation, is proposed in terms of linear matrix inequalities (LMIs. The criterion is derived by free weighting matrices that can reduce the conservatism. The effectiveness has been verified in a number example and the compute results are presented to validate the proposed design method.

Linking environment-productivity trade-offs and correlated uncertainties: Greenhouse gas emissions and crop productivity in paddy rice production systems

International Nuclear Information System (INIS)

Hayashi, Kiyotada; Nagumo, Yoshifumi; Domoto, Akiko

2016-01-01

In comparative life cycle assessments of agricultural production systems, analyses of both the trade-offs between environmental impacts and crop productivity and of the uncertainties specific to agriculture such as fluctuations in greenhouse gas (GHG) emissions and crop yields are crucial. However, these two issues are usually analyzed separately. In this paper, we present a framework to link trade-off and uncertainty analyses; correlated uncertainties are integrated into environment-productivity trade-off analyses. We compared three rice production systems in Japan: a system using a pelletized, nitrogen-concentrated organic fertilizer made from poultry manure using closed-air composting techniques (high-N system), a system using a conventional organic fertilizer made from poultry manure using open-air composting techniques (low-N system), and a system using a chemical compound fertilizer (conventional system). We focused on two important sources of uncertainties in paddy rice cultivation—methane emissions from paddy fields and crop yields. We found trade-offs between the conventional and high-N systems and the low-N system and the existence of positively correlated uncertainties in the conventional and high-N systems. We concluded that our framework is effective in recommending the high-N system compared with the low-N system, although the performance of the former is almost the same as the conventional system. - Highlights: • Correlated uncertainties were integrated into environment-productivity trade-offs. • Life cycle GHG emissions and crop yields were analyzed using field and survey data. • Three rice production systems using chemical or organic fertilizers were compared. • There were portfolio (insurance) effects in matured technologies. • Analysis of trade-offs and correlated uncertainties will be useful for decisions.

Linking environment-productivity trade-offs and correlated uncertainties: Greenhouse gas emissions and crop productivity in paddy rice production systems

Energy Technology Data Exchange (ETDEWEB)

Hayashi, Kiyotada, E-mail: hayashi@affrc.go.jp [Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604 (Japan); Nagumo, Yoshifumi [Crop Research Center, Niigata Agricultural Research Institute, 857 Nagakura-machi, Nagaoka, Niigata 940-0826 (Japan); Domoto, Akiko [Mie Prefecture Agricultural Research Institute, 530 Kawakita-cho, Ureshino, Matsusaka, Mie 515-2316 (Japan)

2016-11-15

In comparative life cycle assessments of agricultural production systems, analyses of both the trade-offs between environmental impacts and crop productivity and of the uncertainties specific to agriculture such as fluctuations in greenhouse gas (GHG) emissions and crop yields are crucial. However, these two issues are usually analyzed separately. In this paper, we present a framework to link trade-off and uncertainty analyses; correlated uncertainties are integrated into environment-productivity trade-off analyses. We compared three rice production systems in Japan: a system using a pelletized, nitrogen-concentrated organic fertilizer made from poultry manure using closed-air composting techniques (high-N system), a system using a conventional organic fertilizer made from poultry manure using open-air composting techniques (low-N system), and a system using a chemical compound fertilizer (conventional system). We focused on two important sources of uncertainties in paddy rice cultivation—methane emissions from paddy fields and crop yields. We found trade-offs between the conventional and high-N systems and the low-N system and the existence of positively correlated uncertainties in the conventional and high-N systems. We concluded that our framework is effective in recommending the high-N system compared with the low-N system, although the performance of the former is almost the same as the conventional system. - Highlights: • Correlated uncertainties were integrated into environment-productivity trade-offs. • Life cycle GHG emissions and crop yields were analyzed using field and survey data. • Three rice production systems using chemical or organic fertilizers were compared. • There were portfolio (insurance) effects in matured technologies. • Analysis of trade-offs and correlated uncertainties will be useful for decisions.

Uncertainty analysis guide

International Nuclear Information System (INIS)

Andres, T.H.

2002-05-01

This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)

Uncertainty analysis guide

Energy Technology Data Exchange (ETDEWEB)

Andres, T.H

2002-05-01

This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)

Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Energy Technology Data Exchange (ETDEWEB)

Gillingham, Kenneth; Nordhaus, William; Anthoff, David; Blanford, Geoffrey J.; Bosetti, Valentina; Christensen, Peter; McJeon, Haewon C.; Reilly, J. M.; Sztorc, Paul

2015-10-01

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity and estimates the pdfs of key output variables, including CO₂ concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insight on tail events.

Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield

Science.gov (United States)

Baurle, R. A.; Axdahl, E. L.

2017-01-01

Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.

Cost reduction through system integration

International Nuclear Information System (INIS)

Helsing, P.

1994-01-01

In resent years cost reduction has been a key issue in the petroleum industry. Several findings are not economically attractive at the current cost level, and for this and other reasons some of the major oil companies require the suppliers to have implemented a cost reduction programme to prequalify for projects. The present paper addresses cost reduction through system design and integration in both product development and working methods. This is to be obtained by the combination of contracts by reducing unnecessary coordination and allow re-use of proven interface designs, improve subsystem integration by ''top down'' system design, and improve communication and exchange of experience. 3 figs

A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response

International Nuclear Information System (INIS)

Ju, Liwei; Tan, Zhongfu; Yuan, Jinyun; Tan, Qingkun; Li, Huanhuan; Dong, Fugui

2016-01-01

Highlights: • Our research focuses on Virtual Power Plant (VPP). • Virtual Power Plant consists of WPP, PV, CGT, ESSs and DRPs. • Robust optimization theory is introduced to analyze uncertainties. • A bi-level stochastic scheduling optimization model is proposed for VPP. • Models are built to measure the impacts of ESSs and DERPs on VPP operation. - Abstract: To reduce the uncertain influence of wind power and solar photovoltaic power on virtual power plant (VPP) operation, robust optimization theory (ROT) is introduced to build a stochastic scheduling model for VPP considering the uncertainty, price-based demand response (PBDR) and incentive-based demand response (IBDR). First, the VPP components are described including the wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs) and demand resource providers (DRPs). Then, a scenario generation and reduction frame is proposed for analyzing and simulating output stochastics based on the interval method and the Kantorovich distance. Second, a bi-level robust scheduling model is proposed with a double robust coefficient for WPP and PV. In the upper layer model, the maximum VPP operation income is taken as the optimization objective for building the scheduling model with the day-ahead prediction output of WPP and PV. In the lower layer model, the day-ahead scheduling scheme is revised with the actual output of the WPP and PV under the objectives of the minimum system net load and the minimum system operation cost. Finally, the independent micro-grid in a coastal island in eastern China is used for the simulation analysis. The results illustrate that the model can overcome the influence of uncertainty on VPP operations and reduce the system power shortage cost by connecting the day-ahead scheduling with the real-time scheduling. ROT could provide a flexible decision tool for decision makers, effectively addressing system uncertainties. ESSs could

Energy levels of one-dimensional systems satisfying the minimal length uncertainty relation

Energy Technology Data Exchange (ETDEWEB)

Bernardo, Reginald Christian S., E-mail: rcbernardo@nip.upd.edu.ph; Esguerra, Jose Perico H., E-mail: jesguerra@nip.upd.edu.ph

2016-10-15

The standard approach to calculating the energy levels for quantum systems satisfying the minimal length uncertainty relation is to solve an eigenvalue problem involving a fourth- or higher-order differential equation in quasiposition space. It is shown that the problem can be reformulated so that the energy levels of these systems can be obtained by solving only a second-order quasiposition eigenvalue equation. Through this formulation the energy levels are calculated for the following potentials: particle in a box, harmonic oscillator, Pöschl–Teller well, Gaussian well, and double-Gaussian well. For the particle in a box, the second-order quasiposition eigenvalue equation is a second-order differential equation with constant coefficients. For the harmonic oscillator, Pöschl–Teller well, Gaussian well, and double-Gaussian well, a method that involves using Wronskians has been used to solve the second-order quasiposition eigenvalue equation. It is observed for all of these quantum systems that the introduction of a nonzero minimal length uncertainty induces a positive shift in the energy levels. It is shown that the calculation of energy levels in systems satisfying the minimal length uncertainty relation is not limited to a small number of problems like particle in a box and the harmonic oscillator but can be extended to a wider class of problems involving potentials such as the Pöschl–Teller and Gaussian wells.

Uncertainties Related to Extreme Event Statistics of Sewer System Surcharge and Overflow

DEFF Research Database (Denmark)

Schaarup-Jensen, Kjeld; Johansen, C.; Thorndahl, Søren Liedtke

2005-01-01

Today it is common practice - in the major part of Europe - to base design of sewer systems in urban areas on recommended minimum values of flooding frequencies related to either pipe top level, basement level in buildings or level of road surfaces. Thus storm water runoff in sewer systems is only...... proceeding in an acceptable manner, if flooding of these levels is having an average return period bigger than a predefined value. This practice is also often used in functional analysis of existing sewer systems. If a sewer system can fulfil recommended flooding frequencies or not, can only be verified...... by performing long term simulations - using a sewer flow simulation model - and draw up extreme event statistics from the model simulations. In this context it is important to realize that uncertainties related to the input parameters of rainfall runoff models will give rise to uncertainties related...

Evaluation of the uncertainty in an EBT3 film dosimetry system utilizing net optical density

Science.gov (United States)

Marroquin, Elsa Y. León; Herrera González, José A.; Camacho López, Miguel A.; Barajas, José E. Villarreal

2016-01-01

Radiochromic film has become an important tool to verify dose distributions for intensity‐modulated radiotherapy (IMRT) and quality assurance (QA) procedures. A new radiochromic film model, EBT3, has recently become available, whose composition and thickness of the sensitive layer are the same as those of previous EBT2 films. However, a matte polyester layer was added to EBT3 to prevent the formation of Newton's rings. Furthermore, the symmetrical design of EBT3 allows the user to eliminate side‐orientation dependence. This film and the flatbed scanner, Epson Perfection V750, form a dosimetry system whose intrinsic characteristics were studied in this work. In addition, uncertainties associated with these intrinsic characteristics and the total uncertainty of the dosimetry system were determined. The analysis of the response of the radiochromic film (net optical density) and the fitting of the experimental data to a potential function yielded an uncertainty of 2.6%, 4.3%, and 4.1% for the red, green, and blue channels, respectively. In this work, the dosimetry system presents an uncertainty in resolving the dose of 1.8% for doses greater than 0.8 Gy and less than 6 Gy for red channel. The films irradiated between 0 and 120 Gy show differences in the response when scanned in portrait or landscape mode; less uncertainty was found when using the portrait mode. The response of the film depended on the position on the bed of the scanner, contributing an uncertainty of 2% for the red, 3% for the green, and 4.5% for the blue when placing the film around the center of the bed of scanner. Furthermore, the uniformity and reproducibility radiochromic film and reproducibility of the response of the scanner contribute less than 1% to the overall uncertainty in dose. Finally, the total dose uncertainty was 3.2%, 4.9%, and 5.2% for red, green, and blue channels, respectively. The above uncertainty values were obtained by minimizing the contribution to the total dose

Uncertainty analysis of nuclear waste package corrosion

International Nuclear Information System (INIS)

Kurth, R.E.; Nicolosi, S.L.

1986-01-01

This paper describes the results of an evaluation of three uncertainty analysis methods for assessing the possible variability in calculating the corrosion process in a nuclear waste package. The purpose of the study is the determination of how each of three uncertainty analysis methods, Monte Carlo, Latin hypercube sampling (LHS) and a modified discrete probability distribution method, perform in such calculations. The purpose is not to examine the absolute magnitude of the numbers but rather to rank the performance of each of the uncertainty methods in assessing the model variability. In this context it was found that the Monte Carlo method provided the most accurate assessment but at a prohibitively high cost. The modified discrete probability method provided accuracy close to that of the Monte Carlo for a fraction of the cost. The LHS method was found to be too inaccurate for this calculation although it would be appropriate for use in a model which requires substantially more computer time than the one studied in this paper

Development of Advanced Life Cycle Costing Methods for Technology Benefit/Cost/Risk Assessment

Science.gov (United States)

Yackovetsky, Robert (Technical Monitor)

2002-01-01

The overall objective of this three-year grant is to provide NASA Langley's System Analysis Branch with improved affordability tools and methods based on probabilistic cost assessment techniques. In order to accomplish this objective, the Aerospace Systems Design Laboratory (ASDL) needs to pursue more detailed affordability, technology impact, and risk prediction methods and to demonstrate them on variety of advanced commercial transports. The affordability assessment, which is a cornerstone of ASDL methods, relies on the Aircraft Life Cycle Cost Analysis (ALCCA) program originally developed by NASA Ames Research Center and enhanced by ASDL. This grant proposed to improve ALCCA in support of the project objective by updating the research, design, test, and evaluation cost module, as well as the engine development cost module. Investigations into enhancements to ALCCA include improved engine development cost, process based costing, supportability cost, and system reliability with airline loss of revenue for system downtime. A probabilistic, stand-alone version of ALCCA/FLOPS will also be developed under this grant in order to capture the uncertainty involved in technology assessments. FLOPS (FLight Optimization System program) is an aircraft synthesis and sizing code developed by NASA Langley Research Center. This probabilistic version of the coupled program will be used within a Technology Impact Forecasting (TIF) method to determine what types of technologies would have to be infused in a system in order to meet customer requirements. A probabilistic analysis of the CER's (cost estimating relationships) within ALCCA will also be carried out under this contract in order to gain some insight as to the most influential costs and the impact that code fidelity could have on future RDS (Robust Design Simulation) studies.

Uncertainty management in knowledge based systems for nondestructive testing-an example from ultrasonic testing

International Nuclear Information System (INIS)

Rajagopalan, C.; Kalyanasundaram, P.; Baldev Raj

1996-01-01

The use of fuzzy logic, as a framework for uncertainty management, in a knowledge-based system (KBS) for ultrasonic testing of austenitic stainless steels is described. Parameters that may contain uncertain values are identified. Methodologies to handle uncertainty in these parameters using fuzzy logic are detailed. The overall improvement in the performance of the knowledge-based system after incorporating fuzzy logic is discussed. The methodology developed being universal, its extension to other KBS for nondestructive testing and evaluation is highlighted. (author)

Essays in energy policy and planning modeling under uncertainty: Value of information, optimistic biases, and simulation of capacity markets

Science.gov (United States)

Hu, Ming-Che

Optimization and simulation are popular operations research and systems analysis tools for energy policy modeling. This dissertation addresses three important questions concerning the use of these tools for energy market (and electricity market) modeling and planning under uncertainty. (1) What is the value of information and cost of disregarding different sources of uncertainty for the U.S. energy economy? (2) Could model-based calculations of the performance (social welfare) of competitive and oligopolistic market equilibria be optimistically biased due to uncertainties in objective function coefficients? (3) How do alternative sloped demand curves perform in the PJM capacity market under economic and weather uncertainty? How does curve adjustment and cost dynamics affect the capacity market outcomes? To address the first question, two-stage stochastic optimization is utilized in the U.S. national MARKAL energy model; then the value of information and cost of ignoring uncertainty are estimated for three uncertainties: carbon cap policy, load growth and natural gas prices. When an uncertainty is important, then explicitly considering those risks when making investments will result in better performance in expectation (positive expected cost of ignoring uncertainty). Furthermore, eliminating the uncertainty would improve strategies even further, meaning that improved forecasts of future conditions are valuable ( i.e., a positive expected value of information). Also, the value of policy coordination shows the difference between a strategy developed under the incorrect assumption of no carbon cap and a strategy correctly anticipating imposition of such a cap. For the second question, game theory models are formulated and the existence of optimistic (positive) biases in market equilibria (both competitive and oligopoly markets) are proved, in that calculated social welfare and producer profits will, in expectation, exceed the values that will actually be received

Using the sampling method to propagate uncertainties of physical parameters in systems with fissile material

International Nuclear Information System (INIS)

Campolina, Daniel de Almeida Magalhães

2015-01-01

There is an uncertainty for all the components that comprise the model of a nuclear system. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a realistic calculation that has been replacing conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using a Monte Carlo code by sampling the input parameters is recent because of the huge computational effort required. By analyzing the propagated uncertainty to the effective neutron multiplication factor (k eff ), the effects of the sample size, computational uncertainty and efficiency of a random number generator to represent the distributions that characterize physical uncertainty in a light water reactor was investigated. A program entitled GB s ample was implemented to enable the application of the random sampling method, which requires an automated process and robust statistical tools. The program was based on the black box model and the MCNPX code was used in and parallel processing for the calculation of particle transport. The uncertainties considered were taken from a benchmark experiment in which the effects in k eff due to physical uncertainties is done through a conservative method. In this work a script called GB s ample was implemented to automate the sampling based method, use multiprocessing and assure the necessary robustness. It has been found the possibility of improving the efficiency of the random sampling method by selecting distributions obtained from a random number generator in order to obtain a better representation of uncertainty figures. After the convergence of the method is achieved, in order to reduce the variance of the uncertainty propagated without increase in computational time, it was found the best number o components to be sampled. It was also observed that if the sampling method is used to calculate the effect on k eff due to physical uncertainties reported by

Offshore wind farms for hydrogen production subject to uncertainties

Energy Technology Data Exchange (ETDEWEB)

Kassem, Nabil [Royal Inst. of Tech., Stockholm (Sweden). Dept. of Energy Processes

2002-07-01

Wind power is a source of clean, nonpolluting electricity, which is fully competitive, if installed at favorable wind sites, with fossil fuel and nuclear power generation. Major technical growth has been in Europe, where government policies and high conventional energy costs favor the use of wind power. As part of its strategy, the EU-Commission has launched a target to increase the installed capacity of Wind power from 7 GWe, in 1998 to 40 GWe by year 2012. Wind power is an intermittent electricity generator, thus it does not provide electric power on an 'as needed' basis. Off-peak power generated from offshore wind farms can be utilized for hydrogen production using water electrolysis. Like electricity, hydrogen is a second energy carrier, which will pave the way for future sustainable energy systems. It is environmentally friendly, versatile, with great potentials in stationary and mobile power applications. Water electrolysis is a well-established technology, which depends on the availability of cheap electrical power. Offshore wind farms have longer lifetime due to lower mechanical fatigue loads, yet to be economic, they have to be of sizes greater than 150 MW using large turbines (> 1.5 MW). The major challenge in wind energy assessment is how accurately the wind speed and hence the error in wind energy can be predicted. Therefore, wind power is subject to a great deal of uncertainties, which should be accounted for in order to provide meaningful and reliable estimates of performance and economic figures-of-merit. Failure to account for uncertainties would result in deterministic estimates that tend to overstate performance and underestimate costs. This study uses methods of risk analysis to evaluate the simultaneous effect of multiple input uncertainties, and provide Life Cycle Assessment (LCA) of the-economic viability of offshore wind systems for hydrogen production subject to technical and economical uncertainties (Published in summary form only)

Robust Optimization for Time-Cost Tradeoff Problem in Construction Projects

OpenAIRE

Li, Ming; Wu, Guangdong

2014-01-01

Construction projects are generally subject to uncertainty, which influences the realization of time-cost tradeoff in project management. This paper addresses a time-cost tradeoff problem under uncertainty, in which activities in projects can be executed in different construction modes corresponding to specified time and cost with interval uncertainty. Based on multiobjective robust optimization method, a robust optimization model for time-cost tradeoff problem is developed. In order to illus...

Accounting for Epistemic and Aleatory Uncertainty in Early System Design, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — This project extends Probability Bounds Analysis to model epistemic and aleatory uncertainty during early design of engineered systems in an Integrated Concurrent...

Accounting for Epistemic and Aleatory Uncertainty in Early System Design, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The proposed work extends Probability Bounds Analysis to model epistemic and aleatory uncertainty during early design of engineered systems in an Integrated...

A game-theoretic approach for calibration of low-cost magnetometers under noise uncertainty

Science.gov (United States)

Siddharth, S.; Ali, A. S.; El-Sheimy, N.; Goodall, C. L.; Syed, Z. F.

2012-02-01

Pedestrian heading estimation is a fundamental challenge in Global Navigation Satellite System (GNSS)-denied environments. Additionally, the heading observability considerably degrades in low-speed mode of operation (e.g. walking), making this problem even more challenging. The goal of this work is to improve the heading solution when hand-held personal/portable devices, such as cell phones, are used for positioning and to improve the heading estimation in GNSS-denied signal environments. Most smart phones are now equipped with self-contained, low cost, small size and power-efficient sensors, such as magnetometers, gyroscopes and accelerometers. A magnetometer needs calibration before it can be properly employed for navigation purposes. Magnetometers play an important role in absolute heading estimation and are embedded in many smart phones. Before the users navigate with the phone, a calibration is invoked to ensure an improved signal quality. This signal is used later in the heading estimation. In most of the magnetometer-calibration approaches, the motion modes are seldom described to achieve a robust calibration. Also, suitable calibration approaches fail to discuss the stopping criteria for calibration. In this paper, the following three topics are discussed in detail that are important to achieve proper magnetometer-calibration results and in turn the most robust heading solution for the user while taking care of the device misalignment with respect to the user: (a) game-theoretic concepts to attain better filter parameter tuning and robustness in noise uncertainty, (b) best maneuvers with focus on 3D and 2D motion modes and related challenges and (c) investigation of the calibration termination criteria leveraging the calibration robustness and efficiency.

A game-theoretic approach for calibration of low-cost magnetometers under noise uncertainty

International Nuclear Information System (INIS)

Siddharth, S; Ali, A S; El-Sheimy, N; Goodall, C L; Syed, Z F

2012-01-01

Pedestrian heading estimation is a fundamental challenge in Global Navigation Satellite System (GNSS)-denied environments. Additionally, the heading observability considerably degrades in low-speed mode of operation (e.g. walking), making this problem even more challenging. The goal of this work is to improve the heading solution when hand-held personal/portable devices, such as cell phones, are used for positioning and to improve the heading estimation in GNSS-denied signal environments. Most smart phones are now equipped with self-contained, low cost, small size and power-efficient sensors, such as magnetometers, gyroscopes and accelerometers. A magnetometer needs calibration before it can be properly employed for navigation purposes. Magnetometers play an important role in absolute heading estimation and are embedded in many smart phones. Before the users navigate with the phone, a calibration is invoked to ensure an improved signal quality. This signal is used later in the heading estimation. In most of the magnetometer-calibration approaches, the motion modes are seldom described to achieve a robust calibration. Also, suitable calibration approaches fail to discuss the stopping criteria for calibration. In this paper, the following three topics are discussed in detail that are important to achieve proper magnetometer-calibration results and in turn the most robust heading solution for the user while taking care of the device misalignment with respect to the user: (a) game-theoretic concepts to attain better filter parameter tuning and robustness in noise uncertainty, (b) best maneuvers with focus on 3D and 2D motion modes and related challenges and (c) investigation of the calibration termination criteria leveraging the calibration robustness and efficiency. (paper)

Knowledge management system for risk mitigation in supply chain uncertainty: case from automotive battery supply chain

Science.gov (United States)

Marie, I. A.; Sugiarto, D.; Surjasa, D.; Witonohadi, A.

2018-01-01

Automotive battery supply chain include battery manufacturer, sulphuric acid suppliers, polypropylene suppliers, lead suppliers, transportation service providers, warehouses, retailers and even customers. Due to the increasingly dynamic condition of the environment, supply chain actors were required to improve their ability to overcome various uncertainty issues in the environment. This paper aims to describe the process of designing a knowledge management system for risk mitigation in supply chain uncertainty. The design methodology began with the identification of the knowledge needed to solve the problems associated with uncertainty and analysis of system requirements. The design of the knowledge management system was described in the form of a data flow diagram. The results of the study indicated that key knowledge area that needs to be managed were the knowledge to maintain the stability of process in sulphuric acid process and knowledge to overcome the wastes in battery manufacturing process. The system was expected to be a media acquisition, dissemination and storage of knowledge associated with the uncertainty in the battery supply chain and increase the supply chain performance.

The Launch Systems Operations Cost Model

Science.gov (United States)

Prince, Frank A.; Hamaker, Joseph W. (Technical Monitor)

2001-01-01

One of NASA's primary missions is to reduce the cost of access to space while simultaneously increasing safety. A key component, and one of the least understood, is the recurring operations and support cost for reusable launch systems. In order to predict these costs, NASA, under the leadership of the Independent Program Assessment Office (IPAO), has commissioned the development of a Launch Systems Operations Cost Model (LSOCM). LSOCM is a tool to predict the operations & support (O&S) cost of new and modified reusable (and partially reusable) launch systems. The requirements are to predict the non-recurring cost for the ground infrastructure and the recurring cost of maintaining that infrastructure, performing vehicle logistics, and performing the O&S actions to return the vehicle to flight. In addition, the model must estimate the time required to cycle the vehicle through all of the ground processing activities. The current version of LSOCM is an amalgamation of existing tools, leveraging our understanding of shuttle operations cost with a means of predicting how the maintenance burden will change as the vehicle becomes more aircraft like. The use of the Conceptual Operations Manpower Estimating Tool/Operations Cost Model (COMET/OCM) provides a solid point of departure based on shuttle and expendable launch vehicle (ELV) experience. The incorporation of the Reliability and Maintainability Analysis Tool (RMAT) as expressed by a set of response surface model equations gives a method for estimating how changing launch system characteristics affects cost and cycle time as compared to today's shuttle system. Plans are being made to improve the model. The development team will be spending the next few months devising a structured methodology that will enable verified and validated algorithms to give accurate cost estimates. To assist in this endeavor the LSOCM team is part of an Agency wide effort to combine resources with other cost and operations professionals to

Autonomous Flight Rules Concept: User Implementation Costs and Strategies

Science.gov (United States)

Cotton, William B.; Hilb, Robert

2014-01-01

The costs to implement Autonomous Flight Rules (AFR) were examined for estimates in acquisition, installation, training and operations. The user categories were airlines, fractional operators, general aviation and unmanned aircraft systems. Transition strategies to minimize costs while maximizing operational benefits were also analyzed. The primary cost category was found to be the avionics acquisition. Cost ranges for AFR equipment were given to reflect the uncertainty of the certification level for the equipment and the extent of existing compatible avionics in the aircraft to be modified.

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

Science.gov (United States)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design

Trapped between two tails: trading off scientific uncertainties via climate targets

International Nuclear Information System (INIS)

Lemoine, Derek; McJeon, Haewon C

2013-01-01

Climate change policies must trade off uncertainties about future warming, about the social and ecological impacts of warming, and about the cost of reducing greenhouse gas emissions. We show that laxer carbon targets produce broader distributions for climate damages, skewed towards severe outcomes. However, if potential low-carbon technologies fill overlapping niches, then more stringent carbon targets produce broader distributions for the cost of reducing emissions, skewed towards high-cost outcomes. We use the technology-rich GCAM integrated assessment model to assess the robustness of 450 and 500 ppm carbon targets to each uncertain factor. The 500 ppm target provides net benefits across a broad range of futures. The 450 ppm target provides net benefits only when impacts are greater than conventionally assumed, when multiple technological breakthroughs lower the cost of abatement, or when evaluated with a low discount rate. Policy evaluations are more sensitive to uncertainty about abatement technology and impacts than to uncertainty about warming. (letter)

Trapped between two tails: trading off scientific uncertainties via climate targets

Science.gov (United States)

Lemoine, Derek; McJeon, Haewon C.

2013-09-01

Climate change policies must trade off uncertainties about future warming, about the social and ecological impacts of warming, and about the cost of reducing greenhouse gas emissions. We show that laxer carbon targets produce broader distributions for climate damages, skewed towards severe outcomes. However, if potential low-carbon technologies fill overlapping niches, then more stringent carbon targets produce broader distributions for the cost of reducing emissions, skewed towards high-cost outcomes. We use the technology-rich GCAM integrated assessment model to assess the robustness of 450 and 500 ppm carbon targets to each uncertain factor. The 500 ppm target provides net benefits across a broad range of futures. The 450 ppm target provides net benefits only when impacts are greater than conventionally assumed, when multiple technological breakthroughs lower the cost of abatement, or when evaluated with a low discount rate. Policy evaluations are more sensitive to uncertainty about abatement technology and impacts than to uncertainty about warming.

Uncertainty quantification of CO2 emission reduction for maritime shipping

International Nuclear Information System (INIS)

Yuan, Jun; Ng, Szu Hui; Sou, Weng Sut

2016-01-01

The International Maritime Organization (IMO) has recently proposed several operational and technical measures to improve shipping efficiency and reduce the greenhouse gases (GHG) emissions. The abatement potentials estimated for these measures have been further used by many organizations to project future GHG emission reductions and plot Marginal Abatement Cost Curves (MACC). However, the abatement potentials estimated for many of these measures can be highly uncertain as many of these measures are new, with limited sea trial information. Furthermore, the abatements obtained are highly dependent on ocean conditions, trading routes and sailing patterns. When the estimated abatement potentials are used for projections, these ‘input’ uncertainties are often not clearly displayed or accounted for, which can lead to overly optimistic or pessimistic outlooks. In this paper, we propose a methodology to systematically quantify and account for these input uncertainties on the overall abatement potential forecasts. We further propose improvements to MACCs to better reflect the uncertainties in marginal abatement costs and total emissions. This approach provides a fuller and more accurate picture of abatement forecasts and potential reductions achievable, and will be useful to policy makers and decision makers in the shipping industry to better assess the cost effective measures for CO 2 emission reduction. - Highlights: • We propose a systematic method to quantify uncertainty in emission reduction. • Marginal abatement cost curves are improved to better reflect the uncertainties. • Percentage reduction probability is given to determine emission reduction target. • The methodology is applied to a case study on maritime shipping.

Estimating the costs of nuclear power: benchmarks and uncertainties

International Nuclear Information System (INIS)

Leveque, Francois

2013-05-01

The debate on this topic is fairly confusing. Some present electricity production using nuclear power as an affordable solution, others maintain it is too expensive. These widely divergent views prompt fears among consumers and voters that they are being manipulated: each side is just defending its own interests and the true cost of nuclear power is being concealed. Companies and non-government organizations certainly adopt whatever position suits them best. But at the same time, the notion of just one 'true' cost is misleading. As we shall see in this paper there is no such thing as the cost of nuclear power: we must reason in terms of costs and draw a distinction between a private cost and a social cost. The private cost is what an operator examines before deciding whether it is opportune to build a new nuclear power station. This cost varies between different investors, particularly as a function of their attitude to risks. On the other hand the social cost weighs on society, which may take into account the risk of proliferation, or the benefits of avoiding carbon-dioxide emissions, among others. The cost of actually building new plant differs from one country to the next. So deciding whether nuclear power is profitable or not, a benefit for society or not, does not involve determining the real cost, but rather compiling data, developing methods and formulating hypotheses. It is not as easy as inundating the general public with contradictory figures, but it is a more effective way of casting light on economic decisions by industry and government. Without evaluating the costs it is impossible to establish the cost price, required to compare electricity production using nuclear power and rival technologies. Would it be preferable to build a gas-powered plant, a nuclear reactor or a wind farm? Which technology yields the lowest cost per KWh? Under what conditions - financial terms, regulatory framework, carbon pricing - will private investors see an adequate return

Sensing risk, fearing uncertainty: Systems science approach to change.

Directory of Open Access Journals (Sweden)

Ivo P Janecka

2014-03-01

Full Text Available BackgroundMedicine devotes its primary focus to understanding change, from cells to network relationships; observations of non-linearity are inescapable. Recent events provide extraordinary examples of major non-linear surprises within the societal system: human genome-from anticipated 100,000+ genes to only 20,000+; junk DNA-initially ignored but now proven to control genetic processes; economic reversals-bursting of bubbles in technology, housing, finance; foreign wars; relentless rise in obesity, neurodegenerative diseases.There are two attributes of systems science that are especially relevant to this research: One- it offers a method for creating a structural context with a guiding path to pragmatic knowledge; and, two- it gives pre-eminence to sensory input capable to register, evaluate, and react to change. Material / MethodPublic domain records of change, during the last fifty years, have been studied in the context of systems science, the dynamic systems model, and various cycles. Results / Conclusions Change is dynamic, ever-present, never isolated, and of variable impact; it reflects innumerable relationships among contextual systems; change can be perceived as risk or uncertainty depending upon how the assessment is made; risk is quantifiable by sensory input and generates a degree of rational optimism; uncertainty is not quantifiable and evokes fear; trust is key to sharing risk; the measurable financial credit can be a proxy for societal trust; expanding credit dilutes trust; when a credit bubble bursts, so will trust; absence of trust paralyzes systems’ relationships leading to disorganized complexity which prevents value creation and heightens the probability of random events; disappearance of value, accompanied by chaos, threatens all systems.From personal health to economic sustainability and collective rationality, most examined components of the societal system were found not to be optimized and trust was not in evidence.

Cost and Systems Analysis of Innovative Fuel Resources Concepts

Energy Technology Data Exchange (ETDEWEB)

Schneider, Erich [Univ. of Texas, Austin, TX (United States). Nuclear and Radiation Engineering Program; Byers, M. [Univ. of Texas, Austin, TX (United States). Nuclear and Radiation Engineering Program

2017-05-04

Economically recovered uranium from seawater can have a transformative effect on the way policy makers view the long-term viability of uranium based fuel cycles. Seawater uranium, even when estimated to cost more than terrestrially mined uranium, is integral in establishing an economic backstop, thus reducing uncertainty in future nuclear power costs. While a passive recovery scheme relying on a field of polymer adsorbents prepared via radiation induced grafting has long been considered the leading technology for full scale deployment, non-trivial cost and logistical barriers persist. Consequently, university partners of the nation-wide consortium for seawater uranium recovery have developed variants of this technology, each aiming to address a substantial weakness. The focus of this NEUP project is the economic impacts of the proposed variant technologies. The team at University of Alabama has pursued an adsorbent synthesis method that replaces the synthetic fiber backbone with a natural waste product. Chitin fibers suitable for ligand grafting have been prepared from shrimp shell waste. These environmental benefits could be realized at a comparable cost to the reference fiber so long as the uptake can be increased or the chemical consumption cost decreased.

A Belief Rule Based Expert System to Assess Mental Disorder under Uncertainty

DEFF Research Database (Denmark)

Hossain, Mohammad Shahadat; Afif Monrat, Ahmed; Hasan, Mamun

2016-01-01