Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor—A Solution for Smoothing the Output Power of PV Power Plants

Science.gov (United States)

Sukič, Primož; Štumberger, Gorazd

2017-01-01

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly. PMID:28505078

A survey on wind power ramp forecasting.

Energy Technology Data Exchange (ETDEWEB)

Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-02-23

The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

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

Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

Energy Technology Data Exchange (ETDEWEB)

Shin, Jung Ho; Roh, Myung Sub [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

2013-10-15

The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3{sup rd} through 5{sup th} BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6{sup th} BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3{sup th}, 4{sup th}, 5{sup th} entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then

Reactor power control device in BWR power plant

International Nuclear Information System (INIS)

Kurosawa, Tsuneo.

1997-01-01

The present invention provides a device for controlling reactor power based on a start-up/shut down program in a BWR type reactor, as well as for detecting deviation, if occurs, of the power from the start-up/shut down program, to control a recycling flow rate control system or control rod drive mechanisms. Namely, a power instruction section successively executes the start-up/shut down program and controls the coolant recycling system and the control rod driving mechanisms to control the power. A current state monitoring and calculation section receives a process amount, calculates parameters showing the plant state, compares/monitors them with predetermined values, detecting the deviation, if occurs, of the plant state from the start-up/shut down program, and prevents output of a power increase control signal which leads to power increase. A forecasting and monitoring/calculation section forecasts and calculates the plant state when not yet executed steps of the start-up/shut down program are performed, stops the execution of the start-up/shut down program in the next step in a case of forecasting that the results of the calculation will deviate from the start-up/shut down program. (I.S.)

The effects of forecast errors on the merchandising of wind power

International Nuclear Information System (INIS)

Roon, Serafin von

2012-01-01

A permanent balance between consumption and generation is essential for a stable supply of electricity. In order to ensure this balance, all relevant load data have to be announced for the following day. Consequently, a day-ahead forecast of the wind power generation is required, which also forms the basis for the sale of the wind power at the wholesale market. The main subject of the study is the short-term power supply, which compensates errors in wind power forecasting for balancing the wind power forecast errors at short notice. These forecast errors effects the revenues and the expenses by selling and buying power in the day-ahead, intraday and balance energy market. These price effects resulting from the forecast errors are derived from an empirical analysis. In a scenario for the year 2020 the potential of conventional power plants to supply power at short notice is evaluated from a technical and economic point of view by a time series analysis and a unit commitment simulation.

Scheduled Operation of PV Power Station Considering Solar Radiation Forecast Error

Science.gov (United States)

Takayama, Satoshi; Hara, Ryoichi; Kita, Hiroyuki; Ito, Takamitsu; Ueda, Yoshinobu; Saito, Yutaka; Takitani, Katsuyuki; Yamaguchi, Koji

Massive penetration of photovoltaic generation (PV) power stations may cause some serious impacts on a power system operation due to their volatile and unpredictable output. Growth of uncertainty may require larger operating reserve capacity and regulating capacity. Therefore, in order to utilize a PV power station as an alternative for an existing power plant, improvement in controllability and adjustability of station output become very important factor. Purpose of this paper is to develop the scheduled operation technique using a battery system (NAS battery) and the meteorological forecast. The performance of scheduled operation strongly depends on the accuracy of solar radiation forecast. However, the solar radiation forecast contains error. This paper proposes scheduling method and rescheduling method considering the trend of forecast error. More specifically, the forecast error scenario is modeled by means of the clustering analysis of the past actual forecast error. Validity and effectiveness of the proposed method is ascertained through computational simulations using the actual PV generation data monitored at the Wakkanai PV power station and solar radiation forecast data provided by the Japan Weather Association.

Numerical forecast test on local wind fields at Qinshan Nuclear Power Plant

International Nuclear Information System (INIS)

Chen Xiaoqiu

2005-01-01

Non-hydrostatic, full compressible atmospheric dynamics model is applied to perform numerical forecast test on local wind fields at Qinshan nuclear power plant, and prognostic data are compared with observed data for wind fields. The results show that the prognostic of wind speeds is better than that of wind directions as compared with observed results. As the whole, the results of prognostic wind field are consistent with meteorological observation data, 54% of wind speeds are within a factor of 1.5, about 61% of the deviation of wind direction within the 1.5 azimuth (≤33.75 degrees) in the first six hours. (authors)

Space power needs and forecasted technologies for the 1990s and beyond

International Nuclear Information System (INIS)

Buden, D.; Albert, T.

1987-01-01

A new generation of reactors for electric power will be available for space missions to satisfy military and civilian needs in the 1990s and beyond. To ensure a useful product, nuclear power plant development must be cognizant of other space power technologies. Major advances in solar and chemical technologies need to be considered in establishing the goals of future nuclear power plants. In addition, the mission needs are evolving into new regimes. Civilian and military power needs are forecasted to exceed anything used in space to date. Technology trend forecasts have been mapped as a function of time for solar, nuclear, chemical, and storage systems to illustrate areas where each technology provides minimum mass. Other system characteristics may dominate the usefulness of a technology on a given mission. This paper will discuss some of these factors, as well as forecast future military and civilian power needs and the status of technologies for the 1990s and 2000s. 6 references

Wind power forecast

Energy Technology Data Exchange (ETDEWEB)

Pestana, Rui [Rede Electrica Nacional (REN), S.A., Lisboa (Portugal). Dept. Systems and Development System Operator; Trancoso, Ana Rosa; Delgado Domingos, Jose [Univ. Tecnica de Lisboa (Portugal). Seccao de Ambiente e Energia

2012-07-01

Accurate wind power forecast are needed to reduce integration costs in the electric grid caused by wind inherent variability. Currently, Portugal has a significant wind power penetration level and consequently the need to have reliable wind power forecasts at different temporal scales, including localized events such as ramps. This paper provides an overview of the methodologies used by REN to forecast wind power at national level, based on statistical and probabilistic combinations of NWP and measured data with the aim of improving accuracy of pure NWP. Results show that significant improvement can be achieved with statistical combination with persistence in the short-term and with probabilistic combination in the medium-term. NWP are also able to detect ramp events with 3 day notice to the operational planning. (orig.)

Short-term wind power combined forecasting based on error forecast correction

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

An analyser for power plant operations

International Nuclear Information System (INIS)

Rogers, A.E.; Wulff, W.

1990-01-01

Safe and reliable operation of power plants is essential. Power plant operators need a forecast of what the plant will do when its current state is disturbed. The in-line plant analyser provides precisely this information at relatively low cost. The plant analyser scheme uses a mathematical model of the dynamic behaviour of the plant to establish a numerical simulation. Over a period of time, the simulation is calibrated with measurements from the particular plant in which it is used. The analyser then provides a reference against which to evaluate the plant's current behaviour. It can be used to alert the operator to any atypical excursions or combinations of readings that indicate malfunction or off-normal conditions that, as the Three Mile Island event suggests, are not easily recognised by operators. In a look-ahead mode, it can forecast the behaviour resulting from an intended change in settings or operating conditions. Then, when such changes are made, the plant's behaviour can be tracked against the forecast in order to assure that the plant is behaving as expected. It can be used to investigate malfunctions that have occurred and test possible adjustments in operating procedures. Finally, it can be used to consider how far from the limits of performance the elements of the plant are operating. Then by adjusting settings, the required power can be generated with as little stress as possible on the equipment. (6 figures) (Author)

Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

International Nuclear Information System (INIS)

Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

2015-01-01

Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach

Structured, Physically Inspired (Gray Box) Models Versus Black Box Modeling for Forecasting the Output Power of Photovoltaic Plants

Czech Academy of Sciences Publication Activity Database

Paulescu, M.; Brabec, Marek; Boata, R.; Badescu, V.

2017-01-01

Roč. 121, 15 February (2017), s. 792-802 ISSN 0360-5442 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : photovoltaic plant * output power * forecasting * fuzzy model * generalized additive model Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 4.520, year: 2016

Method for assigning sites to projected generic nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Holter, G.M.; Purcell, W.L.; Shutz, M.E.; Young, J.R.

1986-07-01

Pacific Northwest Laboratory developed a method for forecasting potential locations and startup sequences of nuclear power plants that will be required in the future but have not yet been specifically identified by electric utilities. Use of the method results in numerical ratings for potential nuclear power plant sites located in each of the 10 federal energy regions. The rating for each potential site is obtained from numerical factors assigned to each of 5 primary siting characteristics: (1) cooling water availability, (2) site land area, (3) power transmission land area, (4) proximity to metropolitan areas, and (5) utility plans for the site. The sequence of plant startups in each federal energy region is obtained by use of the numerical ratings and the forecasts of generic nuclear power plant startups obtained from the EIA Middle Case electricity forecast. Sites are assigned to generic plants in chronological order according to startup date.

Method for assigning sites to projected generic nuclear power plants

International Nuclear Information System (INIS)

Holter, G.M.; Purcell, W.L.; Shutz, M.E.; Young, J.R.

1986-07-01

Pacific Northwest Laboratory developed a method for forecasting potential locations and startup sequences of nuclear power plants that will be required in the future but have not yet been specifically identified by electric utilities. Use of the method results in numerical ratings for potential nuclear power plant sites located in each of the 10 federal energy regions. The rating for each potential site is obtained from numerical factors assigned to each of 5 primary siting characteristics: (1) cooling water availability, (2) site land area, (3) power transmission land area, (4) proximity to metropolitan areas, and (5) utility plans for the site. The sequence of plant startups in each federal energy region is obtained by use of the numerical ratings and the forecasts of generic nuclear power plant startups obtained from the EIA Middle Case electricity forecast. Sites are assigned to generic plants in chronological order according to startup date

Online Short-term Solar Power Forecasting

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

2011-01-01

This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours.......This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours....

Method of forecasting power distribution

International Nuclear Information System (INIS)

Kaneto, Kunikazu.

1981-01-01

Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

Directory of Open Access Journals (Sweden)

Yuan-Kang Wu

2014-01-01

Full Text Available The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

Forecasting parameters of a the monuclear power plant with a torsatron reactor

International Nuclear Information System (INIS)

Artyugina, I.M.; Semenov, A.A.; Smirnov, A.N.

1982-01-01

A number of problems related to forecasting technical economical factors of thermonuclear electric plant (TNPP) based on the torsatron reactor is considered. Possible methodic approaches to the estimation of TNPP nonstandard equipment construction-mounting works and the results of forecasting the investment structure in TNPP are analysed. The influence of TP basic systems on the total investment value depending on accepted price level is shown. Quantitative estimations of specific investments and electric energy production cost permit to estimate rather optimistically the considered TNPP type and to draw a conclusion on advisability of the further study

Ensemble forecasting using sequential aggregation for photovoltaic power applications

International Nuclear Information System (INIS)

Thorey, Jean

2017-01-01

Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts. (author) [fr

Online short-term solar power forecasting

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

2009-01-01

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen......-minute observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques....... Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to two hours...

Elecnuc. Nuclear power plants in the world

International Nuclear Information System (INIS)

1998-01-01

This small booklet summarizes in tables all the numerical data relative to the nuclear power plants worldwide. These data come from the French CEA/DSE/SEE Elecnuc database. The following aspects are reviewed: 1997 highlights; main characteristics of the reactor types in operation, under construction or on order; map of the French nuclear power plants; worldwide status of nuclear power plants at the end of 1997; nuclear power plants in operation, under construction and on order; capacity of nuclear power plants in operation; net and gross capacity of nuclear power plants on the grid and in commercial operation; forecasts; first power generation of nuclear origin per country, achieved or expected; performance indicator of PWR units in France; worldwide trend of the power generation indicator; nuclear power plants in operation, under construction, on order, planned, cancelled, shutdown, and exported; planning of steam generators replacement; MOX fuel program for plutonium recycling. (J.S.)

Power distribution forecasting device for reactors

International Nuclear Information System (INIS)

Tsukii, Makoto

1981-01-01

Purpose: To save expensive calculations on the forecasting of reactor power distribution. Constitution: Core status (CSD) such as entire coolant flow rate, pressures in the reactor, temperatures at the outlet and inlet and positions for control rods are inputted into a power distribution calculation device to calculate the power distribution based on physical models intermittently. Further, present power distribution is calculated based on in-core neutron flux measured values and CSD in a process control computer. Further, the ratio of the calculation results of the latter to those of the former is calculated, stored and inputted into a correction device to correct the forecast power distribution obtained by the power distribution calculation device. This enables to forecast the power distribution with excellent responsivity in the reactor site. (Furukawa, Y.)

Methods of improvement of forecasting of development of mineral deposits' power supply

Directory of Open Access Journals (Sweden)

Alexander V. Putilov

2015-03-01

Full Text Available Mineral deposits (among which non-ferrous metals take a leading place are situated on the territory of our planet rather unevenly, and often in out-of-the-way places. Nuclear power (particularly, transportable nuclear power plants provides the new possibilities of power supply, which is very important for deposits' development. This article shares the economic aspects of forecasting in the field of power development (in particular, nuclear power on the basis of transportable nuclear power plants. Economic barriers of development of innovative nuclear technologies are considered on the example of transportable nuclear power plants. At the same time, there are given the ways of elimination of such barrier to development of this technology as methodical absence of investigation of a question of distribution of added cost between producers of innovative equipment and final product. Addition of new analytical tool (“business diagonal” is offered for a method of definition of economically efficient distribution of added cost (received as a result of introduction of innovative technologies between participants of production and consumption of atomic energy within the “economic cross” model. There is offered the order of use of method of cash flows discounting at calculations between nuclear market participants. Economic methods, offered in this article, may be used in forecasting of development of other energy technologies and introduction of prospective energy equipment.

Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

Energy Technology Data Exchange (ETDEWEB)

Optis, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Scott, George N. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Draxl, Caroline [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2018-02-02

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present. Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.

Project 'WINDBANK mittleres Aaretal' - Analysis, Diagnosis and Forecast of Wind Fields around the Nuclear Power Plant Goesgen

International Nuclear Information System (INIS)

Graber, W. K.; Tinguely, M.

2002-07-01

An emergency decision support system for accidental releases of radioactivity into the atmosphere providing regional wind field information is presented. This system is based on intensive meteorological field campaigns each lasting 3-4 months in the regions around the Swiss nuclear power plants. The wind data from temporary and permanent stations are analysed to evaluate the typical wind field patterns occurring in these regions. A cluster analysis for these data-sets lead to 12 different wind field classes with a high separation quality. In the present report, it is demonstrated that an on-line acquisition of meteorological data from existing permanent stations is enough to diagnose the recent wind field class in a region with a radius of 25 km around the nuclear power station of Goesgen with a probability of 95% to hit the correct class. Furthermore, a method is presented to use a high resolution weather prediction model to forecast the future wind field classes. An average probability of 76% to hit the correct class for a forecast time of 24 hours is evaluated. Finally, a method for parameterization of turbulence providing input for dispersion models from standard meteorological online data is presented. (author)

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, J.; Hodge, B. M.

2014-04-01

This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

DEFF Research Database (Denmark)

Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre

2015-01-01

the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview...

Elecnuc. Nuclear power plants in the world

International Nuclear Information System (INIS)

2000-01-01

This small booklet summarizes in tables all the numerical data relative to the nuclear power plants worldwide. These data come from the French CEA/DSE/SEE Elecnuc database. The following aspects are reviewed: 1999 highlights; main characteristics of the reactor types in operation, under construction or on order; map of the French nuclear power plants; worldwide status of nuclear power plants at the end of 1999; nuclear power plants in operation, under construction and on order; capacity of nuclear power plants in operation; net and gross capacity of nuclear power plants on the grid and in commercial operation; grid connection forecasts; world electric power market; electronuclear owners and share holders in EU, capacity and load factor; first power generation of nuclear origin per country, achieved or expected; performance indicator of PWR units in France; worldwide trend of the power generation indicator; 1999 gross load factor by operator; nuclear power plants in operation, under construction, on order, planned, cancelled, shutdown, and exported; planning of steam generators replacement; MOX fuel program for plutonium recycling. (J.S.)

On probabilistic forecasting of wind power time-series

DEFF Research Database (Denmark)

Pinson, Pierre

power dynamics. In both cases, the model parameters are adaptively and recursively estimated, time-adaptativity being the result of exponential forgetting of past observations. The probabilistic forecasting methodology is applied at the Horns Rev wind farm in Denmark, for 10-minute ahead probabilistic...... forecasting of wind power generation. Probabilistic forecasts generated from the proposed methodology clearly have higher skill than those obtained from a classical Gaussian assumption about wind power predictive densities. Corresponding point forecasts also exhibit significantly lower error criteria....

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

Energy Technology Data Exchange (ETDEWEB)

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

Wind Power Forecasting Error Distributions: An International Comparison

DEFF Research Database (Denmark)

Hodge, Bri-Mathias; Lew, Debra; Milligan, Michael

2012-01-01

Wind power forecasting is essential for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that may occur is a critical factor for system operation functions, such as the setting of operating reserve...... levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations....

Investigation of ecological constraints influence on competitiveness of nuclear power plants

OpenAIRE

Marchenko, O.V.; Solomin, S.V.

2015-01-01

The purpose of the present study is to compare economic efficiency of nuclear power plants and plants on fossil fuel for short-term and long-term (until 2050) perspective and further forecasts specification of nuclear power generation development in Russia and in the world on the background of world energy as a whole. Technical and economic indicators of power plants of different types are systematized taking into account their uncertainty margins. Competitiveness of power plants of differ...

Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

Directory of Open Access Journals (Sweden)

Maher G. M. Abdolrasol

2017-06-01

Full Text Available This paper discuss virtual power plant (VPP and Microgrid controller for energy management system (EMS based on optimization techniques by using two optimization techniques namely Backtracking search algorithm (BSA and particle swarm optimization algorithm (PSO. The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.

The effects of forecast errors on the merchandising of wind power; Auswirkungen von Prognosefehlern auf die Vermarktung von Windstrom

Energy Technology Data Exchange (ETDEWEB)

Roon, Serafin von

2012-02-28

A permanent balance between consumption and generation is essential for a stable supply of electricity. In order to ensure this balance, all relevant load data have to be announced for the following day. Consequently, a day-ahead forecast of the wind power generation is required, which also forms the basis for the sale of the wind power at the wholesale market. The main subject of the study is the short-term power supply, which compensates errors in wind power forecasting for balancing the wind power forecast errors at short notice. These forecast errors effects the revenues and the expenses by selling and buying power in the day-ahead, intraday and balance energy market. These price effects resulting from the forecast errors are derived from an empirical analysis. In a scenario for the year 2020 the potential of conventional power plants to supply power at short notice is evaluated from a technical and economic point of view by a time series analysis and a unit commitment simulation.

Elecnuc. Nuclear power plants in the world. 1997

International Nuclear Information System (INIS)

Maubacq, F.; Tailland, C.

1997-04-01

This small booklet provides information about all type of nuclear power plants worldwide. It is based on the data taken from the CEA/DSE/SEE Elecnuc database. The content comprises: the 1996 highlights, the main characteristics of the different type of reactors in operation or under construction, the map of the French nuclear power plant sites, the worldwide status of nuclear power plants at the end of 1996, the nuclear power plants in operation, under construction or on order (by groups of reactor-types), the power capacity evolution of power plants in operation, the net and gross capacity of the power plants on the grid, the commercial operation and grid connection forecasts, the first achieved or expected power generation supplied by a nuclear reactor for each country and the power generation from nuclear reactors, the performance indicator of the PWR units in France, the trends of the power generation indicator worldwide, the nuclear power plants in operation, under construction, on order, planned, cancelled, decommissioned and exported worldwide, the schedule of steam generator replacements, and the MOX fuel plutonium recycling programme. (J.S.)

Use of wind power forecasting in operational decisions.

Energy Technology Data Exchange (ETDEWEB)

Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V. (Decision and Information Sciences); (INESC Porto)

2011-11-29

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help

SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

Directory of Open Access Journals (Sweden)

B. Jency Paulin

2016-01-01

Full Text Available Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP and training procedure for neural network is done by error Back Propagation (BP. In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted.

Short-term load forecasting of power system

Science.gov (United States)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Investigations on forecast-based operating strategies for solar thermal power plants with integrated storage capacity; Untersuchungen zu vorhersagebasierten Betriebsstrategien fuer solarthermische Kraftwerke mit integrierter Speicherkapazitaet

Energy Technology Data Exchange (ETDEWEB)

Wittmann, Michael Karl

2012-07-01

This publication describes a method for scheduling the operation of a power plant storage. The purpose of operation scheduling is to determine the economically optimum yield achievable in the course of daily power plant operation. The optimum operation schedule for the storage is determined based on Dynamic Programming Algorithms. Besides its focus on operation scheduling the publication investigates the effects of imperfect weather and price forecasts on electricity production and thus on the operator's economic results. It assesses the current Spanish legislation as well as other incentive scenarios in terms of their impact on operators' feed-in behaviour.

Electricity demand forecasting techniques

International Nuclear Information System (INIS)

Gnanalingam, K.

1994-01-01

Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

KAUST Repository

Xie, Le

2014-01-01

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

Directory of Open Access Journals (Sweden)

Simone Sperati

2015-09-01

Full Text Available A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE” with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

Large-scale integration of wind power into power systems as well as on transmission networks for offshore wind power plants. Proceedings

Energy Technology Data Exchange (ETDEWEB)

Betancourt, Uta; Ackermann, Thomas (eds.)

2013-11-01

This proceedings contains contributions to the followings main topics: Grid integration experiences; Flexibility and economics of integration; Voltage control issues; Offshore wind power plants; Forecasting; Grid code issues; HVDC connection issues; Frequency control issues; National grid's perspective; Power system balancing; Power system issues; New grid and generators issues; Flexibility with storage and demand side management; AC connected offshore wind power plants; Economic and market issues; Modelling issues; Offshore grid issues.

Probabilistic forecasting of wind power generation using extreme learning machine

DEFF Research Database (Denmark)

Wan, Can; Xu, Zhao; Pinson, Pierre

2014-01-01

an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified......Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes...... with the best performance. Consequently, a new method for prediction intervals formulation based on theELMand the pairs bootstrap is developed.Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results...

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

Energy Technology Data Exchange (ETDEWEB)

Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [University of Texas at Dallas; Feng, Cong [University of Texas at Dallas; Wang, Zhenke [University of Texas at Dallas; Zhang, Jie [University of Texas at Dallas

2018-02-01

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

Forecasting Canadian nuclear power station construction costs

International Nuclear Information System (INIS)

Keng, C.W.K.

1985-01-01

Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequences of the decision as possible. This understanding must be provided by the evaluation of future situations. A key consideration in an evaluation is the financial component. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A brief review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A database is built and indexes are prepared. Based on these indexes, an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of 40 years are generated and applications (such as alternative scenario forecasts and range forecasts) to uncertainty assessment and/or decision-making are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the first for Canadian NGS constructions. (author)

Using Bayes Model Averaging for Wind Power Forecasts

Science.gov (United States)

Preede Revheim, Pål; Beyer, Hans Georg

2014-05-01

For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data

Forecasting nuclear power supply with Bayesian autoregression

International Nuclear Information System (INIS)

Beck, R.; Solow, J.L.

1994-01-01

We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)

Analysis and forecast of maintenance in power plants; Instandhaltungsanalyse und -prognose fuer Grosskraftwerke

Energy Technology Data Exchange (ETDEWEB)

Kittan, Thomas [Vattenfall Europe Generation AG, Spremberg (Germany). Standort Technischer Service; Herold, Matthias [TUEV SUED Industrie Service GmbH, Chemnitz (Germany); Baumann, Carsten [TUEV SUED Industrie Service GmbH, Dresden (Germany)

2011-07-01

A comprehensive assessment of the 'Schwarze Pumpe' power station has given insights into the effectiveness of maintenance measures and facilitated the budgeting of future maintenance for four additional power station units. Vattenfall commissioned experts from TUeV SUeD Industrie Service to carry out third-party analysis of its maintenance measures and forecast future maintenance budgets. The aim was to obtain a valid data set enabling the detailed assessment of past and future activities and pointing out potentials for improvement. (orig.)

INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

KAUST Repository

Elkantassi, Soumaya

2017-10-03

Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

KAUST Repository

Elkantassi, Soumaya; Kalligiannaki, Evangelia; Tempone, Raul

2017-01-01

Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

Development and testing of improved statistical wind power forecasting methods.

Energy Technology Data Exchange (ETDEWEB)

Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-12-06

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

Simulation of regional day-ahead PV power forecast scenarios

DEFF Research Database (Denmark)

Nuno, Edgar; Koivisto, Matti Juhani; Cutululis, Nicolaos Antonio

2017-01-01

Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often...... at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional...... PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records....

Estimation of the uncertainty in wind power forecasting

International Nuclear Information System (INIS)

Pinson, P.

2006-03-01

WIND POWER experiences a tremendous development of its installed capacities in Europe. Though, the intermittence of wind generation causes difficulties in the management of power systems. Also, in the context of the deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3-day ahead eases the integration of wind generation. Wind power forecasts are traditionally provided in the form of point predictions, which correspond to the most-likely power production for a given horizon. That sole information is not sufficient for developing optimal management or trading strategies. Therefore, we investigate on possible ways for estimating the uncertainty of wind power forecasts. The characteristics of the prediction uncertainty are described by a thorough study of the performance of some of the state-of-the-art approaches, and by underlining the influence of some variables e.g. level of predicted power on distributions of prediction errors. Then, a generic method for the estimation of prediction intervals is introduced. This statistical method is non-parametric and utilizes fuzzy logic concepts for integrating expertise on the prediction uncertainty characteristics. By estimating several prediction intervals at once, one obtains predictive distributions of wind power output. The proposed method is evaluated in terms of its reliability, sharpness and resolution. In parallel, we explore the potential use of ensemble predictions for skill forecasting. Wind power ensemble forecasts are obtained either by converting meteorological ensembles (from ECMWF and NCEP) to power or by applying a poor man's temporal approach. A proposal for the definition of prediction risk indices is given, reflecting the disagreement between ensemble members over a set of successive look-ahead times. Such prediction risk indices may comprise a more comprehensive signal on the expected level

The new IEA Wind Task 36 on Wind Power Forecasting

DEFF Research Database (Denmark)

Giebel, Gregor; Cline, Joel; Frank, Helmut

Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind E...... forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions....

Analysis and planning of the utilization of nuclear power plants

International Nuclear Information System (INIS)

Skvarka, P.

1985-01-01

The utilization coefficient as one of the characteristics of availability of nuclear power plants and the operation results (like maximum power, block number, and electric energy generation) are investigated by different statistic methods for several nuclear power plants with PWR type reactors and compared with those of WWER 440-type reactors. By means of linear many-parameter regression analysis the utilization coefficient is studied in dependence on block power and time after reactor commissioning. Forecastings of mean utilization coefficients are presented for the power of WWER 1000-type reactors

Short-Term Solar Collector Power Forecasting

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Perers, Bengt

2011-01-01

This paper describes a new approach to online forecasting of power output from solar thermal collectors. The method is suited for online forecasting in many applications and in this paper it is applied to predict hourly values of power from a standard single glazed large area flat plate collector...... enabling tracking of changes in the system and in the surrounding conditions, such as decreasing performance due to wear and dirt, and seasonal changes such as leaves on trees. This furthermore facilitates remote monitoring and check of the system....

Introducing distributed learning approaches in wind power forecasting

DEFF Research Database (Denmark)

Pinson, Pierre

2016-01-01

Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks...

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

Energy Technology Data Exchange (ETDEWEB)

Finley, Cathy [WindLogics, St. Paul, MN (United States)

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

Energy Technology Data Exchange (ETDEWEB)

Hodge, B.

2013-12-01

Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

Sharing wind power forecasts in electricity markets: A numerical analysis

DEFF Research Database (Denmark)

Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

2016-01-01

In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day......-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding...... flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making...

Advances in electric power and energy systems load and price forecasting

CERN Document Server

2017-01-01

A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

Sharing wind power forecasts in electricity markets: A numerical analysis

International Nuclear Information System (INIS)

Exizidis, Lazaros; Kazempour, S. Jalal; Pinson, Pierre; Greve, Zacharie de; Vallée, François

2016-01-01

Highlights: • Information sharing among different agents can be beneficial for electricity markets. • System cost decreases by sharing wind power forecasts between different agents. • Market power of wind producer may increase by sharing forecasts with market operator. • Extensive out-of-sample analysis is employed to draw reliable conclusions. - Abstract: In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

Energy Technology Data Exchange (ETDEWEB)

Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [Univ. of Texas-Dallas, Richardson, TX (United States); Feng, Cong [Univ. of Texas-Dallas, Richardson, TX (United States); Wang, Zhenke [Univ. of Texas-Dallas, Richardson, TX (United States); Zhang, Jie [Univ. of Texas-Dallas, Richardson, TX (United States)

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

Future wind power forecast errors, need for regulating power, and costs in the Swedish system

Energy Technology Data Exchange (ETDEWEB)

Carlsson, Fredrik [Vattenfall Research and Development AB, Stockholm (Sweden). Power Technology

2011-07-01

Wind power is one of the renewable energy sources in the electricity system that grows most rapid in Sweden. There are however two market challenges that need to be addressed with a higher proportion of wind power - that is variability and predictability. Predictability is important since the spot market Nord Pool Spot requires forecasts of production 12 - 36 hours ahead. The forecast errors must be regulated with regulating power, which is expensive for the actors causing the forecast errors. This paper has investigated a number of scenarios with 10 - 55 TWh of wind power installed in the Swedish system. The focus has been on a base scenario with 10 TWh new wind power consisting of 3,5 GW new wind power and 1,5 GW already installed power, which gives 5 GW. The results show that the costs for the forecast errors will increase as more intermittent production is installed. However, the increase can be limited by for instance trading on intraday market or increase quality of forecasts. (orig.)

Skill forecasting from ensemble predictions of wind power

DEFF Research Database (Denmark)

Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

2009-01-01

Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

Comparison of two new short-term wind-power forecasting systems

Energy Technology Data Exchange (ETDEWEB)

Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

2009-07-15

This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

Device for forecasting reactor power-up routes

International Nuclear Information System (INIS)

Fukuzaki, Takaharu.

1980-01-01

Purpose: To improve the reliability and forecasting accuracy for a device forecasting the change of the state on line in BWR type reactors. Constitution: The present state in a nuclear reactor is estimated in a present state judging section based on measuring signals for thermal power, core flow rate, control rod density and the like from the nuclear reactor, and the estimated results are accumulated in an operation result collecting section. While on the other hand, a forecasting section forecasts the future state in the reactor based on the signals from the forecasting condition setting section. The actual result values from the collecting section and the forecasting results are compared to each other. If they are not equal, new setting signals are outputted from the setting section to perform the forecasting again. These procedures are repeated till the difference between the forecast results and the actual result values is minimized, by which accurate forecasting for the state of the reactor is made possible. (Furukawa, Y.)

Short time ahead wind power production forecast

International Nuclear Information System (INIS)

Sapronova, Alla; Meissner, Catherine; Mana, Matteo

2016-01-01

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast. (paper)

Short time ahead wind power production forecast

Science.gov (United States)

Sapronova, Alla; Meissner, Catherine; Mana, Matteo

2016-09-01

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

The economic value of accurate wind power forecasting to utilities

Energy Technology Data Exchange (ETDEWEB)

Watson, S J [Rutherford Appleton Lab., Oxfordshire (United Kingdom); Giebel, G; Joensen, A [Risoe National Lab., Dept. of Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

With increasing penetrations of wind power, the need for accurate forecasting is becoming ever more important. Wind power is by its very nature intermittent. For utility schedulers this presents its own problems particularly when the penetration of wind power capacity in a grid reaches a significant level (>20%). However, using accurate forecasts of wind power at wind farm sites, schedulers are able to plan the operation of conventional power capacity to accommodate the fluctuating demands of consumers and wind farm output. The results of a study to assess the value of forecasting at several potential wind farm sites in the UK and in the US state of Iowa using the Reading University/Rutherford Appleton Laboratory National Grid Model (NGM) are presented. The results are assessed for different types of wind power forecasting, namely: persistence, optimised numerical weather prediction or perfect forecasting. In particular, it will shown how the NGM has been used to assess the value of numerical weather prediction forecasts from the Danish Meteorological Institute model, HIRLAM, and the US Nested Grid Model, which have been `site tailored` by the use of the linearized flow model WA{sup s}P and by various Model output Statistics (MOS) and autoregressive techniques. (au)

Short-term wind power forecasting: probabilistic and space-time aspects

DEFF Research Database (Denmark)

Tastu, Julija

work deals with the proposal and evaluation of new mathematical models and forecasting methods for short-term wind power forecasting, accounting for space-time dynamics based on geographically distributed information. Different forms of power predictions are considered, starting from traditional point...... into the corresponding models are analysed. As a final step, emphasis is placed on generating space-time trajectories: this calls for the prediction of joint multivariate predictive densities describing wind power generation at a number of distributed locations and for a number of successive lead times. In addition......Optimal integration of wind energy into power systems calls for high quality wind power predictions. State-of-the-art forecasting systems typically provide forecasts for every location individually, without taking into account information coming from the neighbouring territories. It is however...

Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

DEFF Research Database (Denmark)

Wan, Can; Song, Yonghua; Xu, Zhao

2016-01-01

probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

Directory of Open Access Journals (Sweden)

Yajing Gao

2016-01-01

Full Text Available With the growing influence of fog and haze (F-H weather and the rapid development of distributed energy resources (DERs and smart grids, the concept of the virtual power plant (VPP employed in this study would help to solve the dispatch problem caused by multiple DERs connected to the power grid. The effects of F-H weather on photovoltaic output forecast, load forecast and power system dispatch are discussed according to real case data. The wavelet neural network (WNN model was employed to predict photovoltaic output and load, considering F-H weather, based on the idea of “similar days of F-H”. The multi-objective optimal dispatch model of a power system adopted in this paper contains several VPPs and conventional power plants, under F-H weather, and the mixed integer linear programming (MILP and the Yalmip toolbox of MATLAB were adopted to solve the dispatch model. The analysis of the results from a case study proves the validity and feasibility of the model and the algorithms.

Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

Directory of Open Access Journals (Sweden)

Huiru Zhao

2015-03-01

Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

Directory of Open Access Journals (Sweden)

E. Faghihnia

2014-01-01

Full Text Available Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF approach, trained by the polynomial model tree (POLYMOT learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: application to ETEX tracer experiment

International Nuclear Information System (INIS)

Zhang, X.L.; Li, Q.B.; Su, G.F.; Yuan, M.Q.

2015-01-01

The accidental release of radioactive materials from nuclear power plant leads to radioactive pollution. We apply an augmented ensemble Kalman filter (EnKF) with a chemical transport model to jointly estimate the emissions of Perfluoromethylcyclohexane (PMCH), a tracer substitute for radionuclides, from a point source during the European Tracer Experiment, and to improve the forecast of its dispersion downwind. We perturb wind fields to account for meteorological uncertainties. We expand the state vector of PMCH concentrations through continuously adding an a priori emission rate for each succeeding assimilation cycle. We adopt a time-correlated red noise to simulate the temporal emission fluctuation. The improved EnKF system rapidly updates (and reduces) the excessively large initial first-guess emissions, thereby significantly improves subsequent forecasts (r = 0.83, p < 0.001). It retrieves 94% of the total PMCH released and substantially reduces transport error (>80% average reduction of the normalized mean square error). - Highlights: • EnKF is augmented for estimating emission and improving dispersion forecast. • The improved system retrieves 94% of the actual total tracer release in ETEX. • The system substantially improves the 3-h forecast of the tracer dispersion. • The method is robust and insensitive to the first-guess emissions. • The meteorological uncertainties exert strong influence on the performance

Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Directory of Open Access Journals (Sweden)

Haixiang Zang

2016-01-01

Full Text Available Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD, runs test (RT, and relevance vector machine (RVM. First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF components and residual (RES component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

The costs of completing unfinished US nuclear power plants

International Nuclear Information System (INIS)

Feldman, S.L.; Bernstein, M.A.; Noland, R.B.

1988-01-01

A cost benefit analysis is performed to assess the costs of completing unfinished nuclear power plants in four regions of the United States of America, (north-east, south-east, mid-west and west). The analysis is in five main sections: the projection of the cost to complete nuclear plants under construction, the forecast of future operations and maintenance costs, the forecast of price of fuels, the evaluation of future electricity demand and capacity growth, and calculation of the financial cost-benefit ratio based on the preceding figures. It was found that in the north-east, mid-west and west, because the demand for the power will not be made before the year 2000, finishing the units is not the least-cost supply option. Therefore, most of the units should not be finished unless over 90% completed already, in which case it may be cost-effective to finish them. (author)

Increase of the Integration Degree of Wind Power Plants into the Energy System Using Wind Forecasting and Power Consumption Predictor Models by Transmission System Operator

Directory of Open Access Journals (Sweden)

Manusov V.Z.

2017-12-01

Full Text Available Wind power plants’ (WPPs high penetration into the power system leads to various inconveniences in the work of system operators. This fact is associated with the unpredictable nature of wind speed and generated power, respectively. Due to these factors, such source of electricity must be connected to the power system to avoid detrimental effects on the stability and quality of electricity. The power generated by the WPPs is not regulated by the system operator. Accurate forecasting of wind speed and power, as well as power load can solve this problem, thereby making a significant contribution to improving the power supply systems reliability. The article presents a mathematical model for the wind speed prediction, which is based on autoregression and fuzzy logic derivation of Takagi-Sugeno. The new model of wavelet transform has been developed, which makes it possible to include unnecessary noise from the model, as well as to reveal the cycling of the processes and their trend. It has been proved, that the proposed combination of methods can be used simultaneously to predict the power consumption and the wind power plant potential power at any time interval, depending on the planning horizon. The proposed models support a new scientific concept for the predictive control system of wind power stations and increase their degree integration into the electric power system.

Aggregated wind power generation probabilistic forecasting based on particle filter

International Nuclear Information System (INIS)

Li, Pai; Guan, Xiaohong; Wu, Jiang

2015-01-01

Highlights: • A new method for probabilistic forecasting of aggregated wind power generation. • A dynamic system is established based on a numerical weather prediction model. • The new method handles the non-Gaussian and time-varying wind power uncertainties. • Particle filter is applied to forecast predictive densities of wind generation. - Abstract: Probability distribution of aggregated wind power generation in a region is one of important issues for power system daily operation. This paper presents a novel method to forecast the predictive densities of the aggregated wind power generation from several geographically distributed wind farms, considering the non-Gaussian and non-stationary characteristics in wind power uncertainties. Based on a mesoscale numerical weather prediction model, a dynamic system is established to formulate the relationship between the atmospheric and near-surface wind fields of geographically distributed wind farms. A recursively backtracking framework based on the particle filter is applied to estimate the atmospheric state with the near-surface wind power generation measurements, and to forecast the possible samples of the aggregated wind power generation. The predictive densities of the aggregated wind power generation are then estimated based on these predicted samples by a kernel density estimator. In case studies, the new method presented is tested on a 9 wind farms system in Midwestern United States. The testing results that the new method can provide competitive interval forecasts for the aggregated wind power generation with conventional statistical based models, which validates the effectiveness of the new method

Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

Ying-Yi Hong

2013-11-01

Full Text Available Operation of wind power generation in a large farm is quite challenging in a smart grid owing to uncertain weather conditions. Consequently, operators must accurately forecast wind speed/power in the dispatch center to carry out unit commitment, real power scheduling and economic dispatch. This work presents a novel method based on the integration of empirical mode decomposition (EMD with artificial neural networks (ANN to forecast the short-term (1 h ahead wind speed/power. First, significant parameters for training the ANN are identified using the correlation coefficients. These significant parameters serve as inputs of the ANN. Owing to the volatile and intermittent wind speed/power, the historical time series of wind speed/power is decomposed into several intrinsic mode functions (IMFs and a residual function through EMD. Each IMF becomes less volatile and therefore increases the accuracy of the neural network. The final forecasting results are achieved by aggregating all individual forecasting results from all IMFs and their corresponding residual functions. Real data related to the wind speed and wind power measured at a wind-turbine generator in Taiwan are used for simulation. The wind speed forecasting and wind power forecasting for the four seasons are studied. Comparative studies between the proposed method and traditional methods (i.e., artificial neural network without EMD, autoregressive integrated moving average (ARIMA, and persistence method are also introduced.

On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

DEFF Research Database (Denmark)

Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.

2017-01-01

This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...... of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes...

Using ensemble forecasting for wind power

Energy Technology Data Exchange (ETDEWEB)

Giebel, G.; Landberg, L.; Badger, J. [Risoe National Lab., Roskilde (Denmark); Sattler, K.

2003-07-01

Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and

A methodology for Electric Power Load Forecasting

Directory of Open Access Journals (Sweden)

Eisa Almeshaiei

2011-06-01

Full Text Available Electricity demand forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is almost very complex due to the deregulation of energy markets. Therefore, finding an appropriate forecasting model for a specific electricity network is not an easy task. Although many forecasting methods were developed, none can be generalized for all demand patterns. Therefore, this paper presents a pragmatic methodology that can be used as a guide to construct Electric Power Load Forecasting models. This methodology is mainly based on decomposition and segmentation of the load time series. Several statistical analyses are involved to study the load features and forecasting precision such as moving average and probability plots of load noise. Real daily load data from Kuwaiti electric network are used as a case study. Some results are reported to guide forecasting future needs of this network.

Space-time wind speed forecasting for improved power system dispatch

KAUST Repository

Zhu, Xinxin

2014-02-27

To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

Directory of Open Access Journals (Sweden)

Radziukynas V.

2016-04-01

Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

Science.gov (United States)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Electricity production by hydro power plants: possibilities of forecasting

International Nuclear Information System (INIS)

Barkans, J.; Zicmane, I.

2004-01-01

Hydro energy accounts for 17% of global electricity production and is the most important source of renewable energies actively used today, being at the same time the least influential ecologically. Its only disadvantages is that this kind of energy is difficult to forecast, which hinders not only the planning of tariffs, year budgets and investments, but also contractual negotiations in particular month. The paper shows that the forecasting of hydro energy production can be linked to certain natural processes, namely, to the cyclic behaviour observed for water flows of the world's rivers. The authors propose a method according to which the forecasting procedure is performed using the data of observations as signals applied to special digital filters transforming the water flow process into integral and differential forms, which after appropriate treatment are expected again in usual water flow units. For this purpose the water flow integral function is to be divided, by means of spectral analysis, into 'low-frequency' (with a semi-period of 44 years) and 'high-frequency' (4-6 year semi-periods) components, which are of different origin. Each of them should be forecasted separately, with the following summation of the results. In the research it is shown that the cyclic fluctuations of world rivers' water flows are directly associated with variations in the Solar activity. (authors)

Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption

OpenAIRE

Eynard , Julien; Grieu , Stéphane; Polit , Monique

2011-01-01

15 pages; International audience; As part of the OptiEnR research project, the present paper deals with outdoor temperature and thermal power consumption forecasting. This project focuses on optimizing the functioning of a multi-energy district boiler (La Rochelle, west coast of France), adding to the plant a thermal storage unit and implementing a model-based predictive controller. The proposed short-term forecast method is based on the concept of time series and uses both a wavelet-based mu...

Forecasting power plant effects on the coastal zone. EG and G final report number B-4441

International Nuclear Information System (INIS)

1976-06-01

Field methods, data analyses, and calculation are presented exemplifying procedures for oceanic dispersion prediction as a tool for forecasting power plant effects on the coastal zone. Measurements were made of dye, drogues and temperatures near Pilgrim Station's discharge (Plymouth, Massachusetts), and of currents and other variables across Massachusetts Bay. Analysis of current data illustrates separation of tidal, wind-driven and inertial constituents and their significance for dispersion. Dye and temperature dispersion are compared with the currents study, and diffusion coefficients estimated. Current data from coastal sites (New Jersey and Massachusetts) are analyzed to determine field requirements for dispersion estimates. Methods to calculate expected precision of estimates based on brief current records are developed. Model calculations predicting dispersion based on observed ocean currents are described. Formulae are derived to estimate the spatial distribution of impact from a discharge. A numerical model to calculate discharge dispersion in more detail is discussed and used to study time variations of discharge effects. Model predictions are compared with field observations

An overview of wind power forecast types and their use in large-scale integration of wind power

Energy Technology Data Exchange (ETDEWEB)

Nielsen, Henrik Aalborg; Nielsen, Torben Skov [ENFOR A/S, Horslholm (Denmark); Madsen, Henrik [Technical Univ. of Denmark, Lyngby (Denmark). Informatics and Mathematical Modelling

2011-07-01

Wind power forecast characteristics are described and it is shown how analyses of actual decision problems can be used to derive the forecast characteristics important in a given situation. Generally, characteristics related to resolution in space and time, together with the required maximal forecast horizon are easily identified. However, identification of forecast characteristics required for optimal decision support requires a more thorough investigation, which is illustrated by examples. Generally, quantile forecasts of the future wind power production are required, but the transformation of a quantile forecast into an actual decisions is highly dependent on the precise formulation of the decision problem. Furthermore, when consequences of neighbouring time steps interact, quantile forecasts are not sufficient. It is argued that a general solution in such cases is to base the decision on reliable scenarios of the future wind power production. (orig.)

The marketing concept of nuclear power plant constructors

International Nuclear Information System (INIS)

Czakainski, M.

1980-01-01

The paper examines the largely non-investigated area of marketing theory and energy sciences. The author considers the structure of the nuclear power industry and of marketing, analyses the nuclear power station market and its factors of influence, and gives a market forecast. The marketing concept requires especially a typologization of the investment good nuclear power plant. Project-dependent and project-independent marketing activities are coordinated in a marketing programme, and are integrated into mixed marketing efforts. Problems result from insecurity related to the further development of political, social and economic factors of influence. Constructors of nuclear power plants in the Federal Republic of Germany have to adapt to this insecurity and to face risks presented by entrepreneurial activities and the environment by means of flexible planning. (HSCH) [de

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

Directory of Open Access Journals (Sweden)

Wen-Yeau Chang

2013-09-01

Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Electric power systems advanced forecasting techniques and optimal generation scheduling

CERN Document Server

Catalão, João P S

2012-01-01

Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

Applying of forecasting at decision making in power systems

International Nuclear Information System (INIS)

Sapundjiev, G.

2007-01-01

The problems concerning forecast and decision making are analyzed. The typical tasks arising in the forecasting process of the power systems with hierarchical structure formulated and brought to formal description

An analog ensemble for short-term probabilistic solar power forecast

International Nuclear Information System (INIS)

Alessandrini, S.; Delle Monache, L.; Sperati, S.; Cervone, G.

2015-01-01

Highlights: • A novel method for solar power probabilistic forecasting is proposed. • The forecast accuracy does not depend on the nominal power. • The impact of climatology on forecast accuracy is evaluated. - Abstract: The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the solar elevation and the solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the solar radiation within the troposphere is the main meteorological factor influencing the solar power production, as a fraction of short wave solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on solar photovoltaic power prediction. This lack of predictability makes the solar energy integration into the grid challenging. A cost-effective utilization of solar energy over a grid strongly depends on the accuracy and reliability of the power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring solar power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic solar power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model

The role of inmformation systems in virtual power plants; Die Rolle der Informationssysteme in virtuellen Kraftwerken

Energy Technology Data Exchange (ETDEWEB)

Schloegl, F.; Rohrig, K. [Inst. fuer Solare Energieversorgungstechnik e.V. (ISET), Kassel (Germany); Biermann, K. [Deutscher Wetterdienst, Hamburg (Germany); Frank, T. [Envidatec GmbH, Hamburg (Germany); Rudion, K. [Otto-von-Guericke-Univ. Magdeburg (Germany)

2006-07-01

Increasing penetration of electricity supply systems with information and communication technologies in combination with a growing share of distributed generation creates an abundance of information. The new challenge to information systems is to provide the user with relevant information. A virtual power plant is a cluster of distributed generation installations which are collectively run by a central control entity. A most essential part of a virtual power plant is the information system. For an efficient development of information systems it is necessary to replace the existing diversity of data formats with some few standards. Forecasts are an important component of the information system. In order to be able to control future virtual power plants it is necessary to improve the accuracy of forecasts constantly. (orig.)

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

Energy Technology Data Exchange (ETDEWEB)

Hou, Zhangshuan; Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.

2014-10-27

In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing “quasi-deterministic” components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power

NARCIS (Netherlands)

Tascikaraoglu, A.; Sanandaji, B.M.; Chicco, G.; Cocina, V.; Spertino, F.; Erdinç, O.; Paterakis, N.G.; Catalaõ, J.P.S.

2016-01-01

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind

Multi-site solar power forecasting using gradient boosted regression trees

DEFF Research Database (Denmark)

Persson, Caroline Stougård; Bacher, Peder; Shiga, Takahiro

2017-01-01

The challenges to optimally utilize weather dependent renewable energy sources call for powerful tools for forecasting. This paper presents a non-parametric machine learning approach used for multi-site prediction of solar power generation on a forecast horizon of one to six hours. Historical pow...

Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

KAUST Repository

Elkantassi, Soumaya

2017-04-01

Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

An application of ensemble/multi model approach for wind power production forecasting

Science.gov (United States)

Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

2011-02-01

The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

Forecasting wind power production from a wind farm using the RAMS model

DEFF Research Database (Denmark)

Tiriolo, L.; Torcasio, R. C.; Montesanti, S.

2015-01-01

of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available......The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex...... and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution...

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

Directory of Open Access Journals (Sweden)

Emanuele Ogliari

2018-06-01

Full Text Available An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively.

Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

International Nuclear Information System (INIS)

Zhang, Yao; Wang, Jianxue; Luo, Xu

2015-01-01

Highlights: • Quantitative information on the uncertainty of wind power generation. • Kernel density estimator provides non-Gaussian predictive distributions. • Logarithmic transformation reduces the skewness of wind power density. • Boundary kernel method eliminates the density leakage near the boundary. - Abstracts: Probabilistic wind power forecasting not only produces the expectation of wind power output, but also gives quantitative information on the associated uncertainty, which is essential for making better decisions about power system and market operations with the increasing penetration of wind power generation. This paper presents a novel kernel density estimator for probabilistic wind power forecasting, addressing two characteristics of wind power which have adverse impacts on the forecast accuracy, namely, the heavily skewed and double-bounded nature of wind power density. Logarithmic transformation is used to reduce the skewness of wind power density, which improves the effectiveness of the kernel density estimator in a transformed scale. Transformations partially relieve the boundary effect problem of the kernel density estimator caused by the double-bounded nature of wind power density. However, the case study shows that there are still some serious problems of density leakage after the transformation. In order to solve this problem in the transformed scale, a boundary kernel method is employed to eliminate the density leak at the bounds of wind power distribution. The improvement of the proposed method over the standard kernel density estimator is demonstrated by short-term probabilistic forecasting results based on the data from an actual wind farm. Then, a detailed comparison is carried out of the proposed method and some existing probabilistic forecasting methods

Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

KAUST Repository

Elkantassi, Soumaya

2017-01-01

Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized

Hydrosphere monitoring at nuclear power plant sites

International Nuclear Information System (INIS)

Belousova, A.P.; Zakharova, T.V.; Shvets, V.M.

1993-01-01

The paper deals with problems related to protection of the environment in areas occupied by nuclear power plants (NPP). NPP construction and operation result in destruction of ecological, geochemical and geological equilibria in and around NPP sites. This process requires monitoring. Recommendations of the International Agency for Atomic Energy (IAAE) suggest monitoring to commence 2-3 years prior to the start of NPP construction. The paper describes the extent of hydrosphere monitoring and guidelines along which monitoring is to be organized. The authors recommend a certain approach toward the planning observation networks and provide description of forecasting subsystem that consist of a data bank, a continuously operating model (COM) and a forecast unit

An application and verification of ensemble forecasting on wind power to assess operational risk indicators in power grids

Energy Technology Data Exchange (ETDEWEB)

Alessandrini, S.; Ciapessoni, E.; Cirio, D.; Pitto, A.; Sperati, S. [Ricerca sul Sistema Energetico RSE S.p.A., Milan (Italy). Power System Development Dept. and Environment and Sustainable Development Dept.; Pinson, P. [Technical University of Denmark, Lyngby (Denmark). DTU Informatics

2012-07-01

Wind energy is part of the so-called not schedulable renewable sources, i.e. it must be exploited when it is available, otherwise it is lost. In European regulation it has priority of dispatch over conventional generation, to maximize green energy production. However, being variable and uncertain, wind (and solar) generation raises several issues for the security of the power grids operation. In particular, Transmission System Operators (TSOs) need as accurate as possible forecasts. Nowadays a deterministic approach in wind power forecasting (WPF) could easily be considered insufficient to face the uncertainty associated to wind energy. In order to obtain information about the accuracy of a forecast and a reliable estimation of its uncertainty, probabilistic forecasting is becoming increasingly widespread. In this paper we investigate the performances of the COnsortium for Small-scale MOdelling Limited area Ensemble Prediction System (COSMO-LEPS). First the ensemble application is followed by assessment of its properties (i.e. consistency, reliability) using different verification indices and diagrams calculated on wind power. Then we provide examples of how EPS based wind power forecast can be used in power system security analyses. Quantifying the forecast uncertainty allows to determine more accurately the regulation reserve requirements, hence improving security of operation and reducing system costs. In particular, the paper also presents a probabilistic power flow (PPF) technique developed at RSE and aimed to evaluate the impact of wind power forecast accuracy on the probability of security violations in power systems. (orig.)

Forecast of nuclear energetics

Energy Technology Data Exchange (ETDEWEB)

Sikora, W

1976-01-01

The forecast concerning the development of nuclear energetics is presented. Some information on economics of nuclear power plants is given. The nuclear fuel reserves are estimated on the background of power resources of the world. The safety and environment protection problems are mentioned.

Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

DEFF Research Database (Denmark)

Iversen, Jan Emil Banning; Morales González, Juan Miguel; Møller, Jan Kloppenborg

2017-01-01

Short-term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per-horizon-basis, meaning that they lack information about the development of the u...

Assessment of storm forecast

DEFF Research Database (Denmark)

Cutululis, Nicolaos Antonio; Hahmann, Andrea N.; Huus Bjerge, Martin

When wind speed exceeds a certain value, wind turbines shut-down in order to protect their structure. This leads to sudden wind plants shut down and to new challenges concerning the secure operation of the pan-European electric system with future large scale offshore wind power. This task aims...... stopped, completely or partially, producing due to extreme wind speeds. Wind speed and power measurements from those events are presented and compared to the forecast available at Energinet.dk. The analysis looked at wind speed and wind power forecast. The main conclusion of the analysis is that the wind...... to consider it an EWP) and that the available wind speed forecasts are given as a mean wind speed over a rather large area. At wind power level, the analysis shows that prediction of accurate production levels from a wind farm experiencing EWP is rather poor. This is partially because the power curve...

An application of ensemble/multi model approach for wind power production forecast.

Science.gov (United States)

Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

2010-09-01

The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic

The issue of the fourth nuclear power plant and its impact on 3-E problems in Taiwan - empirical evidence from the energy forecasting (EnFore) system

International Nuclear Information System (INIS)

Bor, Y.J.; Chou, F.-Y.

2003-01-01

Taiwan has placed considerable emphasis on economic, energy and environmental (3-E) problems in recent decades. Following President Chen's inauguration, one particular issue of concern has been the current dispute over the fourth nuclear power plant (FNPP) in northern Taiwan. This dispute has had a serious impact on Taiwan's economy, including its energy structure and general policy towards CO 2 emission controls. It is estimated, for example, that the loss to Taiwan's capital market as a result of the FNPP dispute, reached NT$7 trillion (about US$219 billion) by the end of 2000. If Taiwan Power Company (Taipower) were to replace the nuclear power plant capacity with liquified natural gas generators, average utility prices would go up by around 4.6% over the next 10 years. The alternative would be for low-cost coal-fired power plants to assume the major position in future power generation; however, this could cause significant damage to Taiwan's CO 2 emission control policy. This paper uses an integrated computerized system model of energy forecasting to simulate the complex interrelationship between the various issues. Empirical results reveal that there are no perfect solutions available; thus, this is an important learning process for the government in terms of administration, as well as for other academic studies

Short-Term Wind Speed Forecasting for Power System Operations

KAUST Repository

Zhu, Xinxin

2012-04-01

The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.

System for forecasting a reactor power distribution

International Nuclear Information System (INIS)

Motoda, Hiroshi; Nishizawa, Yasuo.

1976-01-01

Purpose: To dispense with frequent running of detector in a BWR type reactor and permit calculation of the prevailing value and forecast value of power distribution in a specified region in an on-line basis. Constitution: The prevailing power distribution P sub(OZ) (where Z indicates a position in the axial direction) at a given position is estimated by prevailing power distribution estimating means, and the average prevailing power distribution Q sub(OZ) in the core is estimated while making correction of a primary neutron distribution model by core average characteristic measuring means. Then, the estimated core average power distribution Q sub(Z) after alteration of the core flow rate or alteration of Xe concentration is estimated by core average power distribution estimating means. At this time, a forecast power distribution P sub(Z) in a specified region after alteration of the flow rate or alteration of the Xe concentration is calculated on the basis of a relation P sub(Z) = (Q sub(Z)/Q sub(OZ)) by using P sub(OZ), Q sub(OZ) and Q sub(Z). The above calculations are carried out in a short period of time by using a process computer. (Ikeda, J.)

Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

Science.gov (United States)

Keane, R. J.; Plant, R. S.; Tennant, W. J.

2015-12-01

The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

DEFF Research Database (Denmark)

Dahl, Christian M.; Croonenbroeck, Carsten

2014-01-01

We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium......-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more...... accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology....

Cash flow forecasting model for nuclear power projects

International Nuclear Information System (INIS)

Liu Wei; Guo Jilin

2002-01-01

Cash flow forecasting is very important for owners and contractors of nuclear power projects to arrange the capital and to decrease the capital cost. The factors related to contractor cash flow forecasting are analyzed and a cash flow forecasting model is presented which is suitable for both contractors and owners. The model is efficiently solved using a cost-schedule data integration scheme described. A program is developed based on the model and verified with real project data. The result indicates that the model is efficient and effective

A new framework to increase the efficiency of large-scale solar power plants.

Science.gov (United States)

Alimohammadi, Shahrouz; Kleissl, Jan P.

2015-11-01

A new framework to estimate the spatio-temporal behavior of solar power is introduced, which predicts the statistical behavior of power output at utility scale Photo-Voltaic (PV) power plants. The framework is based on spatio-temporal Gaussian Processes Regression (Kriging) models, which incorporates satellite data with the UCSD version of the Weather and Research Forecasting model. This framework is designed to improve the efficiency of the large-scale solar power plants. The results are also validated from measurements of the local pyranometer sensors, and some improvements in different scenarios are observed. Solar energy.

Skill forecasting from different wind power ensemble prediction methods

International Nuclear Information System (INIS)

Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

2007-01-01

This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

Directory of nuclear power plants in the world, 1985

International Nuclear Information System (INIS)

Fujii, Haruo

1985-01-01

This book presents technical information and estimates trends of load factors and construction costs of nuclear power plants. Particularly road maps indicating plants are drawn in, which would be practical in visiting them. The data used here are directly confirmed by operators in every part of the world. Therefore, they reflect up-to-date nuclear power developments and its future. This allows wide and exact understanding of world's nuclear power. Chapter 1 presents nuclear power growth around the world and estimates forecasts based on information from electric power companies: nuclear power growths and the growths in the number of reactors around the world, in WOCA (World outside the Centrally Planned Economies Area), in CPEA (Centrally Planned Economies Area) are analyzed in detail. Chapter 2 presents nuclear power plants on maps by country. The maps show exact locations of nuclear power plants with local cities around them, rivers and lakes. For convenience, symbols are given to aid in identifying the types of reactors. Chapter 3 presents general information of nuclear power plants. Also the addresses of operators, all segments of nuclear power supply industries and nuclear organizations are included. For convenience, the index of nuclear power plants is added. Chapter 4 presents technical information, road maps in large scales and photographs of nuclear power plants in the world. The road maps show exact locations of plants. Chapter 5 presents operating experiences, load factors, refuelling and maintenance outages. The trends of data are analyzed both regionally (WOCA, CPEA) and world-widely. Chapter 6 presents trends of construction costs, component costs as percent of total construction costs and direct costs, and construction durations. (J.P.N.)

Life extension for German nuclear power plants

International Nuclear Information System (INIS)

Heller, W.

2005-01-01

The Federation of German Industries (BDI) commissioned a study of the ''Economic Effects of Alternative Lifetimes of Nuclear Power Plants in Germany.'' The expert organizations invited as authors were the Power Economy Institute of the University of Cologne (EWI) and Energy Environment Forecast Analysis GmbH (EEFA), Berlin. The reasons for commissioning the Study include the changed framework conditions (deregulation, CO 2 emission certificate trading, worldwide competition for resources), which have altered the energy supply situation in Europe. The findings of the Study were presented to the public by the BDI on October 26, 2005. The study deals with two scenarios of extended lifetimes for German nuclear power plants of 40 and 60 years as against the existing regulations with plant lifetimes limited to approx. 32 years. The longer service lives of plants are reflected in reduced electricity generation costs and thus may have a positive influence on electricity prices. Moreover, there would be additional growth of production together with additional jobs, all of which would add up to nearly 42,000 persons for all sectors of the economy as compared to the basic scenario. Also, CO 2 emissions could be curbed by up to 50 million tons of carbon dioxide. The Study offers ample and valid reasons in favor of extending the lifetimes of nuclear power plants. In the interest of general welfare, politics would be well advised to relax the restrictions on plant life in the course of this legislative term. (orig.)

Port Menier thermal power plant: Pre-project report

International Nuclear Information System (INIS)

1992-02-01

Port Menier, the town on Anticosti Island in the St Lawrence River estuary, is supplied with electricity from a diesel power plant having a firm capacity of 1,080 kW. Since 1987, power demand has increased at an average annual rate of 5.7%, raising the winter peak demand from 670 kW to 987 kW. The power plant is located in the center of town and is obsolete, presenting a number of architectural, environmental, and operational deficiencies. It is proposed to construct a new power plant having an initial firm capacity of 1,490 kW and storage capacity for 75,000 liters of fuel. The plant site will have an area of ca 6,265 m 2 to allow for an eventual expansion to over 3,000 kW capacity, sufficient for satisfying forecast demand over the next 20 years. Estimated cost of the new plant is ca $9.5 million. The old plant will be decommissioned and the new plant will be built at a site outside of town. The natural and human environments characteristic of the Port Menier area are detailed and the two selected sites for the new plant are described and compared. A site in the industrial zone of Port-Menier is favored. The environmental impacts of the new plant are analyzed and mitigation measures during the preconstruction, construction, and operational phases are proposed. Local economic impacts are estimated at around $990,000. 20 refs., 12 figs., 12 tabs

Uncertainty Quantification of the Real-Time Reserves for Offshore Wind Power Plants

DEFF Research Database (Denmark)

Göçmen, Tuhfe; Giebel, Gregor; Réthoré, Pierre-Elouan

In order to retain the system stability, the wind power plants are required to provide ancillary services. One of those services is reserve power. Here in this study, we focus on the real-time reserves which can be traded in the balancing markets and are currently used for compensation under...... mandatory downregulation stated by the transmission system operators (TSOs). The PossPOW project (Possible Power of down-regulated Offshore Wind power plants) developed a real-time power curve of available power for offshore wind farms for use during down-regulation. The follow-up Concert project......(control and uncertainties in real-time power curves of offshore wind power plants) aims to quantify and finally reduce the uncertainty in reserve power, bringing the PossPOW algorithm and the state of the art forecasting methods together. The experiments designed to test the available power estimated by the Poss...

Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models

Directory of Open Access Journals (Sweden)

Hao Chen

2014-07-01

Full Text Available The scientific evaluation methodology for the forecast accuracy of wind power forecasting models is an important issue in the domain of wind power forecasting. However, traditional forecast evaluation criteria, such as Mean Squared Error (MSE and Mean Absolute Error (MAE, have limitations in application to some degree. In this paper, a modern evaluation criterion, the Diebold-Mariano (DM test, is introduced. The DM test can discriminate the significant differences of forecasting accuracy between different models based on the scheme of quantitative analysis. Furthermore, the augmented DM test with rolling windows approach is proposed to give a more strict forecasting evaluation. By extending the loss function to an asymmetric structure, the asymmetric DM test is proposed. Case study indicates that the evaluation criteria based on DM test can relieve the influence of random sample disturbance. Moreover, the proposed augmented DM test can provide more evidence when the cost of changing models is expensive, and the proposed asymmetric DM test can add in the asymmetric factor, and provide practical evaluation of wind power forecasting models. It is concluded that the two refined DM tests can provide reference to the comprehensive evaluation for wind power forecasting models.

Flood hazards for nuclear power plants

International Nuclear Information System (INIS)

Yen, B.C.

1988-01-01

Flooding hazards for nuclear power plants may be caused by various external geophysical events. In this paper the hydrologic hazards from flash floods, river floods and heavy rain at the plant site are considered. Depending on the mode of analysis, two types of hazard evaluation are identified: 1) design hazard which is the probability of flooding over an expected service period, and 2) operational hazard which deals with real-time forecasting of the probability of flooding of an incoming event. Hazard evaluation techniques using flood frequency analysis can only be used for type 1) design hazard. Evaluation techniques using rainfall-runoff simulation or multi-station correlation can be used for both types of hazard prediction. (orig.)

Forecasting analysis of runoff for reservoir regulation of dams and weirs in terms of hydro power plant operation

International Nuclear Information System (INIS)

Maradjieva, Mariana; Nikolov, Nikola

2008-01-01

In order to meet the needs of Hydropower Plant (HPP) production new algorithms and software were developed for daily, seasonal, annual and long-term control of the runoff for the design of dam and weirs. This control is carried out for monitored periods from 20 to 50 years. The control depends on economic considerations, namely that the accepted probability of required water power is 90%, i.e. concerning the runoff and in this way for the useful volume of water dams. The research is accomplished by a design with the observations. First the hydrometric stations are selected at the available analogy with the building project and then the correlative connection is found assessed by general and true correlative coefficients. The transferring to the project of the observations for the average annual and average monthly water discharges is made with the coefficient of the analogy. The theoretical probability curves are chosen with a minimum dispersion. By the last curves the average monthly distributions are settled with probability from 2% to 90% by statistical method. During the investigated period of the regulation the volumes of discharge, overflow and shortage are calculated as and the determination for the accepted volume of the reservoir if the normative probability of the need is executed. As well the power output of the HPP and its participation in the coverage of the charge diagram on the peak load, under peak load, daily and nightly part are determined in separate observed or forecasting periods. The upper problems about the design and the operation of HPP, water output, reservoir volume and coverage of the charge diagram are solved by iterations. Practical examples are given for the runoff and for the time forecasting system.

Forecast of wind energy production and ensuring required balancing power

International Nuclear Information System (INIS)

Merkulov, M.

2010-01-01

The wind energy is gaining larger part of the energy mix around the world as well as in Bulgaria. Having in mind the irregularity of the wind, we are in front of a challenge for management of the power grid in new unknown conditions. The world's experience has proven that there could be no effective management of the grid without forecasting tools, even with small scale of wind power penetration. Application of such tools promotes simple management of large wind energy production and reduction of the quantities of required balancing powers. The share of the expenses and efforts for forecasting of the wind energy is incomparably small in comparison with expenses for keeping additional powers in readiness. The recent computers potential allow simple and rapid processing of large quantities of data from different sources, which provides required conditions for modeling the world's climate and producing sophisticated forecast. (author)

Very-short-term wind power probabilistic forecasts by sparse vector autoregression

DEFF Research Database (Denmark)

Dowell, Jethro; Pinson, Pierre

2016-01-01

A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...

Forecasting loads and prices in competitive power markets

International Nuclear Information System (INIS)

Bunn, D.W.

2000-01-01

This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side

Elecnuc. Nuclear power plants in the world; Elecnuc. Les centrales nucleaires dans le monde

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-07-01

This small booklet summarizes in tables all the numerical data relative to the nuclear power plants worldwide. These data come from the French CEA/DSE/SEE Elecnuc database. The following aspects are reviewed: 1999 highlights; main characteristics of the reactor types in operation, under construction or on order; map of the French nuclear power plants; worldwide status of nuclear power plants at the end of 1999; nuclear power plants in operation, under construction and on order; capacity of nuclear power plants in operation; net and gross capacity of nuclear power plants on the grid and in commercial operation; grid connection forecasts; world electric power market; electronuclear owners and share holders in EU, capacity and load factor; first power generation of nuclear origin per country, achieved or expected; performance indicator of PWR units in France; worldwide trend of the power generation indicator; 1999 gross load factor by operator; nuclear power plants in operation, under construction, on order, planned, cancelled, shutdown, and exported; planning of steam generators replacement; MOX fuel program for plutonium recycling. (J.S.)

New tool for integration of wind power forecasting into power system operation

DEFF Research Database (Denmark)

Gubina, Andrej F.; Keane, Andrew; Meibom, Peter

2009-01-01

The paper describes the methodology that has been developed for transmission system operators (TSOs) of Republic of Ireland, Eirgrid, and Northern Ireland, SONI the TSO in Northern Ireland, to study the effects of advanced wind power forecasting on optimal short-term power system scheduling....... The resulting schedules take into account the electricity market conditions and feature optimal reserve scheduling. The short-term wind power prediction is provided by the Anemos tool, and the scheduling function, including the reserve optimisation, by the Wilmar tool. The proposed methodology allows...... for evaluation of the impacts that different types of wind energy forecasts (stochastic vs. deterministic vs. perfect) have on the schedules, and how the new incoming information via in-day scheduling impacts the quality of the schedules. Within the methodology, metrics to assess the quality of the schedules...

Short-term forecasting model for aggregated regional hydropower generation

International Nuclear Information System (INIS)

Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

2014-01-01

Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

Manpower simulation for the power plant design engineering

International Nuclear Information System (INIS)

Moon, B.S.; Juhn, P.E.

1982-01-01

Some observation from the examination of actual manhour curves for the power design engineering obtained from Sargent and Lundy Engineers and of a few of the model curves proposed by Bechtel, are analyzed in this paper. A model curve representing typical design engineering manhour has been determined as probability density function for the Gamma Distribution. By means of this model curve, we strategically forecast the future engineering manpower requirements to meet the Covernment's long range nuclear power plan. As a sensitivity analysis, the directions for the localization of nuclear power plant design engineering, are studied in terms of the performance factor for the experienced versus inexperienced engineers. (Author)

Applying machine learning techniques for forecasting flexibility of virtual power plants

DEFF Research Database (Denmark)

MacDougall, Pamela; Kosek, Anna Magdalena; Bindner, Henrik W.

2016-01-01

network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN...... approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single...... algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties....

Combination of Deterministic and Probabilistic Meteorological Models to enhance Wind Farm Power Forecasts

International Nuclear Information System (INIS)

Bremen, Lueder von

2007-01-01

Large-scale wind farms will play an important role in the future worldwide energy supply. However, with increasing wind power penetration all stakeholders on the electricity market will ask for more skilful wind power predictions regarding save grid integration and to increase the economic value of wind power. A Neural Network is used to calculate Model Output Statistics (MOS) for each individual forecast model (ECMWF and HIRLAM) and to model the aggregated power curve of the Middelgrunden offshore wind farm. We showed that the combination of two NWP models clearly outperforms the better single model. The normalized day-ahead RMSE forecast error for Middelgrunden can be reduced by 1% compared to single ECMWF. This is a relative improvement of 6%. For lead times >24h it is worthwhile to use a more sophisticated model combination approach than simple linear weighting. The investigated principle component regression is able to extract the uncorrelated information from two NWP forecasts. The spread of Ensemble Predictions is related to the skill of wind power forecasts. Simple contingency diagrams show that low spread corresponds is more often related to low forecast errors and high spread to large forecast errors

Assessing the need for power

International Nuclear Information System (INIS)

Chern, W.S.; Just, R.E.

1982-01-01

The growing controversy over nuclear power has demanded a critical evaluation of the need for power to justify proposed nuclear power plants. This paper discusses the use of an econometric model developed for the US Nuclear Regulatory Commission to conduct an independent assessment of electricity demand forecasts related to the licensing of nuclear power plants. The model forecasts electricity demand and price by sector and by state. The estimation and forecasting results for the New England region are presented as a case in point where an econometric model has been used to analyse alternative fuel price scenarios and to aid substantive public decision making regarding new nuclear power plant decisions. (author)

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

Science.gov (United States)

Efremenko, Vladimir; Belyaevsky, Roman; Skrebneva, Evgeniya

2017-11-01

In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

Power plant site evaluation, electric energy demand forecasts - Douglas Point Site. Volume 3. Final report

International Nuclear Information System (INIS)

Wilson, J.W.

1975-07-01

This is part of a series of reports containing an evaluation of the proposed Douglas Point nuclear generating station site located on the Potomac River in Maryland 30 miles south of Washington, D.C. This report contains chapters on the Potomac Electric Power Company's market, forecasting future demand, modelling, a residential demand model, a nonresidential demand model, the Southern Maryland Electric Cooperative Model, short term predictive accuracy, and total system requirements

Short term and medium term power distribution load forecasting by neural networks

International Nuclear Information System (INIS)

Yalcinoz, T.; Eminoglu, U.

2005-01-01

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

Spatial-temporal analysis of wind power forecast errors for West-Coast Norway

Energy Technology Data Exchange (ETDEWEB)

Revheim, Paal Preede; Beyer, Hans Georg [Agder Univ. (UiA), Grimstad (Norway). Dept. of Engineering Sciences

2012-07-01

In this paper the spatial-temporal structure of forecast errors for wind power in West-Coast Norway is analyzed. Starting on the qualitative analysis of the forecast error reduction, with respect to single site data, for the lumped conditions of groups of sites the spatial and temporal correlations of the wind power forecast errors within and between the same groups are studied in detail. Based on this, time-series regression models to be used to analytically describe the error reduction are set up. The models give an expected reduction in forecast error between 48.4% and 49%. (orig.)

Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting

DEFF Research Database (Denmark)

Xu, Man; Pinson, Pierre; Lu, Zongxiang

2016-01-01

of the lack of time adaptivity. In this paper, a refined local polynomial regression algorithm is proposed to yield an adaptive robust model of the time-varying scattered power curve for forecasting applications. The time adaptivity of the algorithm is considered with a new data-driven bandwidth selection......Wind farm power curve modeling, which characterizes the relationship between meteorological variables and power production, is a crucial procedure for wind power forecasting. In many cases, power curve modeling is more impacted by the limited quality of input data rather than the stochastic nature...... of the energy conversion process. Such nature may be due the varying wind conditions, aging and state of the turbines, etc. And, an equivalent steady-state power curve, estimated under normal operating conditions with the intention to filter abnormal data, is not sufficient to solve the problem because...

An independent system operator's perspective on operational ramp forecasting

Energy Technology Data Exchange (ETDEWEB)

Porter, G. [New Brunswick System Operator, Fredericton, NB (Canada)

2010-07-01

One of the principal roles of the power system operator is to select the most economical resources to reliably supply electric system power needs. Operational wind power production forecasts are required by system operators in order to understand the impact of ramp event forecasting on dispatch functions. A centralized dispatch approach can contribute to a more efficient use of resources that traditional economic dispatch methods. Wind ramping events can have a significant impact on system reliability. Power systems can have constrained or robust transmission systems, and may also be islanded or have large connections to neighbouring systems. Power resources can include both flexible and inflexible generation resources. Wind integration tools must be used by system operators to improve communications and connections with wind power plants. Improved wind forecasting techniques are also needed. Sensitivity to forecast errors is dependent on current system conditions. System operators require basic production forecasts, probabilistic forecasts, and event forecasts. Forecasting errors were presented as well as charts outlining the implications of various forecasts. tabs., figs.

The long term plan for the integration of nuclear power plants into the Turkish Electrical Power System

International Nuclear Information System (INIS)

Kutukcuoglu, A.

1974-03-01

The report covers in detail the study of the expansion of the Turkish Electric Power System for the period 1980-1987. Load forecast is done by sectors and regions and inter-regions power balances gave the basis for the high voltage network configurations. Expansion alternatives are defined giving priority to hydroelectric projects, to local resources and nuclear power plants concurrently with conventional plants (lignite and oil). Several reactor strategies are analysed with LWR, HWR, FBR and HTGR power plants. Present worth value method is used for comparison of alternatives and sensitivity analysis is done for those ranked in the first places. Load flow, transient stability and frequency deviation studies of the power system are studied carefully by means of A.C. calculator and digital computer codes in order to see the influence of the introduction of large-sized power plants (600-750MW(e)) and their location in the power system. A 600MW(e) nuclear plant in 1983 and a second one of 750MW(e) in 1987 should, it is found, be commissioned into the system. The economic optimization was done with two computer programmes developed by KFA (Juelich): IACO for fuelling nuclear plant and RESTRAPO for power system with high hydroelectric component. The report is bound in three volumes: Volume I: Summary and Conclusions; Volume II: System Planning; Volume III: Electrical Survey

Enhanced short-term wind power forecasting and value to grid operations. The wind forecasting improvement project

Energy Technology Data Exchange (ETDEWEB)

Orwig, Kirsten D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States). Transmission Grid Integration; Benjamin, Stan; Wilczak, James; Marquis, Melinda [National Oceanic and Atmospheric Administration, Boulder, CO (United States). Earth System Research Lab.; Stern, Andrew [National Oceanic and Atmospheric Administration, Silver Spring, MD (United States); Clark, Charlton; Cline, Joel [U.S. Department of Energy, Washington, DC (United States). Wind and Water Power Program; Finley, Catherine [WindLogics, Grand Rapids, MN (United States); Freedman, Jeffrey [AWS Truepower, Albany, NY (United States)

2012-07-01

The current state-of-the-art wind power forecasting in the 0- to 6-h timeframe has levels of uncertainty that are adding increased costs and risks to the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: (1) a one-year field measurement campaign within two regions; (2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and (3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provide an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis. (orig.)

Space-time wind speed forecasting for improved power system dispatch

KAUST Repository

Zhu, Xinxin; Genton, Marc G.; Gu, Yingzhong; Xie, Le

2014-01-01

direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast

Modeling of spatial dependence in wind power forecast uncertainty

DEFF Research Database (Denmark)

Papaefthymiou, George; Pinson, Pierre

2008-01-01

It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus...... neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially...... distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems...

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

Science.gov (United States)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

Short-term Wind Forecasting to Support Virtual Power Player Operation

OpenAIRE

Ramos, Sérgio; Soares, João; Pinto, Tiago; Vale, Zita

2013-01-01

This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed ...

A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

DEFF Research Database (Denmark)

Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

2012-01-01

Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes...... fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind...... power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov...

The Brazilian electric power market: historic and forecasting

International Nuclear Information System (INIS)

Carvalho Afonso, C.A. de; Azevedo, J.B.L. de

1992-01-01

A historical analysis of electric power market evolution in Brazil and in their regions during 1950 to 1990, is described, showing the forecasting for the next ten years. Some considerations about population, energy conservation and industrial consumers are also presented, including statistical data of the electrical power market. (C.G.C.)

Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Directory of Open Access Journals (Sweden)

Manoja Kumar Behera

2018-06-01

Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

Scaling forecast models for wind turbulence and wind turbine power intermittency

Science.gov (United States)

Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

2017-04-01

The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

Fusion power plant availability study

International Nuclear Information System (INIS)

Ladra, D.; Sanguinetti, G.P.; Stube, E.

2001-01-01

The consideration of fusion as an alternative energy source will need to demonstrate that Fusion Power Plant (FPP) design, operating and maintenance characteristics meet the electrical market requirements forecast for the second half of this century. Until now, fusion has been developed in the framework of research and development programmes following natural technological trends. To bring a greater sense of realism to commercial viability and to guarantee that technology-driven fusion development responds to the demands of the market, a conceptual study of future commercial FPPs has been performed with a Power Plant Availability (PPA) study aimed at identifying the aspects affecting the availability and generating costs of FPPs. EFET, who has also been involved in the study, can visualise it from two different points of view; that of the industry (ANSALDO, IBERTEF, SIEMENS, NNC) and that of the utilities (BELGATOM, FRAMATOME, FORTUM). The work carried out covered the following points: socio-economic forecasting; safety and licensing; operation and maintenance; waste and decommissioning; availability and reliability. The following are the most relevant findings, conclusions and recommendations for all these aspects: Demonstrate definitively that the physical principles of nuclear fusion have been validated by means of experiments; Establish a European Industrial Group to support the demonstration phases; Create the financial and contracting framework required to construct these installations. Secure the necessary budgets for the European Union's 5th and 6th Research Programmes. Look for supplementary long term financing sources; The existing Regulatory Bodies should combine to form a single Working Group with responsibility for fusion reactor safety and licensing activities, working on the harmonisation of the regulatory processes, developing FPP safety criteria and guidelines and reviewing industry standards; To be competitive, FPPs should have high availability

Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

Directory of Open Access Journals (Sweden)

Wenlei Bai

2017-12-01

Full Text Available The deterministic methods generally used to solve DC optimal power flow (OPF do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

Energy Technology Data Exchange (ETDEWEB)

Haupt, Sue Ellen [National Center for Atmospheric Research, Boulder, CO (United States)

2016-04-19

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solar power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few

Economic evaluation of short-term wind power forecast in ERCOT. Preliminary results

Energy Technology Data Exchange (ETDEWEB)

Orwig, Kirsten D.; Hodge, Bri-Mathias; Brinkman, Greg; Ela, Erik; Milligan, Michael [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Banunarayanan, Venkat; Nasir, Saleh [ICF International, Fairfax, VA (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States)

2012-07-01

A number of wind energy integration studies have investigated the monetary value of using day-ahead wind power forecasts for grid operation decisions. Historically, these studies have shown that large cost savings could be gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter term (0- to 6-h ahead) wind power forecasts. In 2010, the Department of Energy and the National Oceanic and Atmospheric Administration partnered to form the Wind Forecasting Improvement Project (WFIP) to fund improvements in short-term wind forecasts and determine the economic value of these improvements to grid operators. In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined and the economic results of a production cost model simulation are analyzed. (orig.)

Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment

International Nuclear Information System (INIS)

Delarue, Erik; Van Den Bosch, Pieterjan; D'haeseleer, William

2010-01-01

This paper discusses and quantifies the so-called loss of profit (i.e., the sub-optimality of profit) that can be expected in a Price Based Unit Commitment (PBUC), when incorrect price forecasts are used. For this purpose, a PBUC model has been developed and utilized, using Mixed Integer Linear Programming (MILP). Simulations are used to determine the relationship between the Mean Absolute Percentage Error (MAPE) of a certain price forecast and the loss of profit, for four different types of power plants. A Combined Cycle (CC) power plant and a pumped storage unit show highest sensitivity to incorrect forecasts. A price forecast with a MAPE of 15%, on average, yields 13.8% and 12.1% profit loss, respectively. A classic thermal power plant (coal fired) and cascade hydro unit are less affected by incorrect forecasts, with only 2.4% and 2.0% profit loss, respectively, at the same price forecast MAPE. This paper further demonstrates that if price forecasts show an average bias (upward or downward), using the MAPE as measure of the price forecast might not be sufficient to quantify profit loss properly. Profit loss in this case has been determined as a function of both shift and MAPE of the price forecast. (author)

Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

Science.gov (United States)

Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

2008-12-01

Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

From probabilistic forecasts to statistical scenarios of short-term wind power production

DEFF Research Database (Denmark)

Pinson, Pierre; Papaefthymiou, George; Klockl, Bernd

2009-01-01

on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multistage decision-making problems, e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures......Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform....... This issue is addressed here by describing a method that permits the generation of statistical scenarios of short-term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion...

Fuel combustion in thermal power plants in Japan

Energy Technology Data Exchange (ETDEWEB)

Kotler, V.R.

1983-11-01

The position of black coal in the energy balance of Japan is discussed. About 75% of electric energy is produced by thermal power plants. Eighty-five per cent of electricity is produced by power plants fired with liquid fuels and 3% by coal fired plants. Coal production in Japan, the forecast coal import to the country by 1990 (132 Mt/year), proportion of coal imported from various countries, chemical and physical properties of coal from Australia, China and Japan are discussed. Coal classification used in Japan is evaluated. The following topics associated with coal combustion in fossil-fuel power plants in Japan are discussed: coal grindability, types of pulverizing systems, slagging properties of boiler fuel in Japan, systems for slag removal, main types of steam boilers and coal fired furnaces, burner arrangement and design, air pollution control from fly ash, sulfur oxides and nitrogen oxides, utilization of fly ash for cement production, methods for removal of nitrogen oxides from flue gas using ammonia and catalysts or ammonia without catalysts, efficiency of nitrogen oxide control, abatement of nitrogen oxide emission from boilers by flue gas recirculation and reducing combustion temperatures. The results of research into air pollution control carried out by the Nagasaki Technical Institute are reviewed.

What will become of the european nuclear power plant market

International Nuclear Information System (INIS)

Goulden, O.A.

1976-01-01

In a forecast of the development of the future market for power plants and components in Europe a British consultant comes to the conclusion that the nuclear power programs established in various countries in 1974 are oversized in the light of the reduction in the increment of electricity consumption, which is expected to continue, if they are implemented in addition to existing and planned conventional thermal power stations, and that these programs are too costly if they are intended more or less only to substitute for other sources of energy. A streamlining process, which is deemed to be inescapable, is bound to result in a major cutback of the nuclear power station market in Europe and in a hard fight for survival among the power plant manufacturers now in the market. In the author's opinion, the only way out would be a uniform European electricity generation, transmission and distribution system with all the rationalization effects this would entail. (orig.) [de

A hybrid wind power forecasting model based on data mining and wavelets analysis

International Nuclear Information System (INIS)

Azimi, R.; Ghofrani, M.; Ghayekhloo, M.

2016-01-01

Highlights: • An improved version of K-means algorithm is proposed for clustering wind data. • A persistence based method is applied to select the best cluster for NN training. • A combination of DWT and HANTS methods is used to provide a deep learning for NN. • A hybrid of T.S.B K-means, DWT and HANTS and NN is developed for wind forecasting. - Abstract: Accurate forecasting of wind power plays a key role in energy balancing and wind power integration into the grid. This paper proposes a novel time-series based K-means clustering method, named T.S.B K-means, and a cluster selection algorithm to better extract features of wind time-series data. A hybrid of T.S.B K-means, discrete wavelet transform (DWT) and harmonic analysis time series (HANTS) methods, and a multilayer perceptron neural network (MLPNN) is developed for wind power forecasting. The proposed T.S.B K-means classifies data into separate groups and leads to more appropriate learning for neural networks by identifying anomalies and irregular patterns. This improves the accuracy of the forecast results. A cluster selection method is developed to determine the cluster that provides the best training for the MLPNN. This significantly accelerates the forecast process as the most appropriate portion of the data rather than the whole data is used for the NN training. The wind power data is decomposed by the Daubechies D4 wavelet transform, filtered by the HANTS, and pre-processed to provide the most appropriate inputs for the MLPNN. Time-series analysis is used to pre-process the historical wind-power generation data and structure it into input-output series. Wind power datasets with diverse characteristics, from different wind farms located in the United States, are used to evaluate the accuracy of the hybrid forecasting method through various performance measures and different experiments. A comparative analysis with well-established forecasting models shows the superior performance of the proposed

Analysis on the public acceptance of nuclear power plant and its policies

International Nuclear Information System (INIS)

Choi, Young Sung

1994-02-01

In the current situation of requiring the public acceptance of nuclear power plant, it may be necessary to understand what the public think about this plant and to find out the public preference values for its policies. For this purpose, multi-attribute utility (MAU) model was applied to analyze the public perception pattern for five power production systems. And the conjoint measurement technique was applied to measure quantitative values of public preferences for imaginary policy alternatives. To study the feasibility of these methods, mail survey was conducted to the qualified sample who had the experience of visiting nuclear power plant. Diagnosis of their perception pattern for five power production systems was made by the simplified MAU model. Estimation of the quantitative preference values for potential policy alternatives was made by the conjoint measurement technique, which made it possible to forecast the effectiveness of each option. The results from the qualified sample and the methods used in this study would be helpful to set up new policy of nuclear power plant

Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm

OpenAIRE

BeomJun Park; Jin Hur

2017-01-01

Short-term wind power forecasting is a technique which tells system operators how much wind power can be expected at a specific time. Due to the increasing penetration of wind generating resources into the power grids, short-term wind power forecasting is becoming an important issue for grid integration analysis. The high reliability of wind power forecasting can contribute to the successful integration of wind generating resources into the power grids. To guarantee the reliability of forecas...

A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

Energy Technology Data Exchange (ETDEWEB)

Mellit, Adel [Department of Electronics, Faculty of Sciences and Technology, LAMEL, Jijel University, Ouled-aissa, P.O. Box 98, Jijel 18000 (Algeria); Pavan, Alessandro Massi [Department of Materials and Natural Resources, University of Trieste Via A. Valerio, 2 - 34127 Trieste (Italy)

2010-05-15

Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45 40'N, longitude 13 46'E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model. (author)

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

Directory of Open Access Journals (Sweden)

Wei Sun

2015-01-01

Full Text Available Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1 (GM (1, 1, back propagation (BP and LSSVM, in terms of prediction accuracy and forecasting risk.

Dynamic model of frequency control in Danish power system with large scale integration of wind power

DEFF Research Database (Denmark)

Basit, Abdul; Hansen, Anca Daniela; Sørensen, Poul Ejnar

2013-01-01

This work evaluates the impact of large scale integration of wind power in future power systems when 50% of load demand can be met from wind power. The focus is on active power balance control, where the main source of power imbalance is an inaccurate wind speed forecast. In this study, a Danish...... power system model with large scale of wind power is developed and a case study for an inaccurate wind power forecast is investigated. The goal of this work is to develop an adequate power system model that depicts relevant dynamic features of the power plants and compensates for load generation...... imbalances, caused by inaccurate wind speed forecast, by an appropriate control of the active power production from power plants....

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

Energy Technology Data Exchange (ETDEWEB)

Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

Power and energy balances. Forecast 2008

International Nuclear Information System (INIS)

2005-01-01

Both the energy and power balance in 2008 is slightly better than the former Nordel estimate for 2007. This is due to additional investments in new generation capacity, new interconnections of total 1 000 MW to outside Nordel and reduced demand forecast in Sweden. The Nordic electricity system is able to meet the estimated consumption and the corresponding typical power demand pattern in average conditions. In long term the market is expected to maintain a reasonable balance between supply, imports and demand. Lower precipitation or colder temperature result in higher market prices that give incentives for increased imports, demand response and investments. This is expected to maintain the balance between supply and demand in the short and long term even in extreme situations. Allocation between imports and demand response in reality depends on the prevailing market prices and available generation resources outside Nordel. The interconnection capacities are expected to enable import volumes that can meet the increased peak demand. Some Nordic areas can be exposed to a risk for rationing or other measures because of extremely low precipitation. Nordic transmission capacities may prevent full utilization of Nordic thermal power in certain areas. The planned reinforcements in the 'five prioritised cross-sections' will improve the situation. The power balance and the internal bottlenecks in the continental Europe can have an effect on the import possibilities to the Nordic countries. The annual energy consumption in the Nordic market is estimated to grow by 20 TWh by year 2008 (1.2%la) from 395 TWh in 2004 (temperature corrected). In the three year period investments in power generation is expected to increase the available generation capacity and capability by 1500 MW and 10 TWhla in average conditions. Iceland is not included in the figures. The annual energy consumption in Iceland is estimated to grow by about 6.8 TWh by year 2008 (15 %la) due to two new aluminium

A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

Directory of Open Access Journals (Sweden)

Marino Marrocu

2017-11-01

Full Text Available An accurate forecast of the power generated by a wind turbine is of paramount importance for its optimal exploitation. Several forecasting methods have been proposed either based on a physical modeling or using a statistical approach. All of them rely on the availability of high quality measures of local wind speed, corresponding generated power and on numerical weather forecasts. In this paper, a simple and effective wind power forecast technique, based on the probability distribution mapping of wind speed forecast and observed power data, is presented and it is applied to two turbines located on the island of Borkum (Germany in the North Sea. The wind speed forecast of the ECMWF model at 100 m from the ground is used as the prognostic meteorological parameter. Training procedures are based entirely on relatively short time series of power measurements. Results show that our approach has skills that are similar or better than those obtained using more standard methods when measured with mean absolute error.

Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

Directory of Open Access Journals (Sweden)

Dirk Cannon

2017-06-01

Full Text Available State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state.The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6–8 days. The lower bound is found to be 1.4–2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5–5.5 days. The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days.

Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion

OpenAIRE

Le Cadre , Hélène; Aravena , Ignacio; Papavasiliou , Anthony

2015-01-01

International audience; We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the ag-gregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d'Azur, to evaluate ...

Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Zugno, Marco; Madsen, Henrik

2016-01-01

The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known...... that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic-type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state......-dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non-parametric modelling to explore conditional correlation...

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Directory of Open Access Journals (Sweden)

Fei Wang

2017-12-01

Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

Nuclear power plants

International Nuclear Information System (INIS)

1985-01-01

Data concerning the existing nuclear power plants in the world are presented. The data was retrieved from the SIEN (Nuclear and Energetic Information System) data bank. The information are organized in table forms as follows: nuclear plants, its status and type; installed nuclear power plants by country; nuclear power plants under construction by country; planned nuclear power plants by country; cancelled nuclear power plants by country; shut-down nuclear power plants by country. (E.G.) [pt

TradeWind Deliverable 2.2: Forecast error of aggregated wind power

DEFF Research Database (Denmark)

Giebel, Gregor; Sørensen, Poul Ejnar; Holttinen, Hannele

2007-01-01

Estimates of forecast error of aggregated production for time horizons of intraday and dayahead markets in future will be produced. This will be done by reference to published studies of forecasting for wind generation, and from internal knowledge of WP2 participants. Modelling of wind power fluctuations...

The coal fired power plant of Vado Ligure

International Nuclear Information System (INIS)

Ferrara, V.

1987-01-01

The problem of radiological impact from radioactive effluents released by the forecast new coal-fired power plant of Vado Ligure, is examinated. Using health physic metodologies of evaluation, the highest levels of dose equivalents to the population are computed. Taken into account the possible errors due to conservative models adopted, it is concluded that the induced radiological risks are to be considered negligible, both referring to the actual natural radiological levels in the environment, and considering the maximum permissible levels stated in international raccomandations

LNG plant combined with power plant

Energy Technology Data Exchange (ETDEWEB)

Aoki, I; Kikkawa, Y [Chiyoda Chemical Engineering and Construction Co. Ltd., Tokyo (Japan)

1997-06-01

The LNG plant consumers a lot of power of natural gas cooling and liquefaction. In some LNG plant location, a rapid growth of electric power demand is expected due to the modernization of area and/or the country. The electric power demand will have a peak in day time and low consumption in night time, while the power demand of the LNG plant is almost constant due to its nature. Combining the LNG plant with power plant will contribute an improvement the thermal efficiency of the power plant by keeping higher average load of the power plant, which will lead to a reduction of electrical power generation cost. The sweet fuel gas to the power plant can be extracted from the LNG plant, which will be favorable from view point of clean air of the area. (Author). 5 figs.

LNG plant combined with power plant

International Nuclear Information System (INIS)

Aoki, I.; Kikkawa, Y.

1997-01-01

The LNG plant consumers a lot of power of natural gas cooling and liquefaction. In some LNG plant location, a rapid growth of electric power demand is expected due to the modernization of area and/or the country. The electric power demand will have a peak in day time and low consumption in night time, while the power demand of the LNG plant is almost constant due to its nature. Combining the LNG plant with power plant will contribute an improvement the thermal efficiency of the power plant by keeping higher average load of the power plant, which will lead to a reduction of electrical power generation cost. The sweet fuel gas to the power plant can be extracted from the LNG plant, which will be favorable from view point of clean air of the area. (Author). 5 figs

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

OpenAIRE

Wen-Yeau Chang

2013-01-01

High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...

Position control of a floating nuclear power plant

International Nuclear Information System (INIS)

Motohashi, K.; Hamamoto, T.; Sasaki, R.; Kojima, M.

1993-01-01

In spite of the increasing demand of electricity in Japan, the sites of nuclear power plants suitable for conventional seismic regulations become severely limited. Under these circumstances, several types of advanced siting technology have been developed. Among them, floating power plants have a great advantage of seismic isolation that leads to the seismic design standardization and factory fabrication. The feasibility studies or preliminary designs of floating power plants enclosed by breakwaters in the shallow sea have been carried out last two decades in U.S. and Japan. On the other hand, there are few investigations on the dynamic behavior of floating power plants in the deep sea. The offshore floating nuclear power plants have an additional advantage in that large breakwaters are not required, although the safety checking is inevitable against wind-induced waves. The tension-leg platforms which have been constructed for oil drilling in the deep sea seem to be a promising offshore siting technology of nuclear power plants. The tension-leg mooring system can considerably restrain the heave and pitch of a floating power plant because of significant stiffness in the vertical direction. Different from seismic effects, wind-induced waves may be predicted in advance by making use of ocean weather forecasts using artificial satellites. According to the wave prediction, the position of the floating plant may be controlled by adjusting the water content in ballast tanks and the length of tension-legs before the expected load arrives. The position control system can reduce the wave force acting on the plant and to avoid the unfavorable response behavior of the plant. In this study a semi-submerged circular cylinder with tension-legs is considered as a mathematical model. The configuration of circular cylinder is effective because the dynamic behavior does not depend on incident wave directions. It is also unique in that it can obtain the closed-form solution of

Economic-financial analysis of 'Angra 3 Nuclear Power Plant' project

International Nuclear Information System (INIS)

Andrade, Ronaldo Barata de

2005-01-01

This paper presents an economic-financial evaluation of 'Angra 3 Nuclear Power Plant' project and estimates the lowest power tariff value at which power potentially made available may be commercialized and yet ensure the project a profitability level agreeable to the interests of economic agents and shareholders. According to the 'project evaluation' practice, Angra 3 power generation was considered separately from ELETRONUCLEAR's operating plants (Angra 1 and Angra 2), thus preventing result distortions bound to occur if the economic-financial variables of the new project were analyzed associated with financial commitments and commercialization conditions resulting from the project implementation process and the generated power commercialization conditions, respectively. For this evaluation different technical and economic scenarios were devised, and the theory of Capital Asset Pricing Model for Own Capital cost and that of Weighted Average Cost of Capital were used in addition to the forecast of the Statement of Results and Free Cash flow of Shareholders throughout the power plant life, which is the estimate basis for the lowest power tariff value and the Internal Return Rate of the project. The evaluation conclusion is that Angra 3 project is technically and economically feasible and competitive as compared to the new large power generation projects planned for power supply in Brazil in the next decade, mainly in the Southeast. (author)

A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

Directory of Open Access Journals (Sweden)

Henrik Madsen

2012-03-01

Full Text Available Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i the modeling of a nonlinear and non-stationary stochastic process; (ii the practical implementation of the model we proposed; (iii the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts.

Mid-term load forecasting of power systems by a new prediction method

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2008-01-01

Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem

Short-term load and wind power forecasting using neural network-based prediction intervals.

Science.gov (United States)

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

MODELLING OF RADIONUCLIDE MIGRATION IN THE SYSTEM OF NUCLEAR POWER PLANT BIOLOGICAL PONDS

Directory of Open Access Journals (Sweden)

Ю. Кутлахмедов

2011-04-01

Full Text Available Migration of radionuclide coming from nuclear power plant into the system of biological pondsand then into the water reservoir-cooler is considered in the article. The theme of the work ismodeling of radionuclide migration process in the system of biological ponds on the example of thePivdennoukrainska nuclear power plant using chamber models method. Typical water ecosystemconsisting of three chambers (chamber-water, chamber-biota and chamber-bed silt was the basistaken by the authors. Application of chamber models method allowed authors to develop thedynamic chamber model of radionuclide migration in nuclear power plant biological ponds. Thismodel allows to forecast values and dynamics of radioactive water pollution based on limitedecosystem monitoring data. Thus, parameters of radioactive capacity of nuclear power plantbiological ponds system and water reservoir-cooler were modeled by authors, the estimation andprognosis of radionuclide distribution and accumulation in the system of nuclear power plantbiological ponds were done. Authors also explain the roles of basin water, biomass and bed silt inradionuclide deposition

Evaluation of the performance of a meso-scale NWP model to forecast solar irradiance on Reunion Island for photovoltaic power applications

Science.gov (United States)

Kalecinski, Natacha; Haeffelin, Martial; Badosa, Jordi; Periard, Christophe

2013-04-01

Solar photovoltaic power is a predominant source of electrical power on Reunion Island, regularly providing near 30% of electrical power demand for a few hours per day. However solar power on Reunion Island is strongly modulated by clouds in small temporal and spatial scales. Today regional regulations require that new solar photovoltaic plants be combined with storage systems to reduce electrical power fluctuations on the grid. Hence cloud and solar irradiance forecasting becomes an important tool to help optimize the operation of new solar photovoltaic plants on Reunion Island. Reunion Island, located in the South West of the Indian Ocean, is exposed to persistent trade winds, most of all in winter. In summer, the southward motion of the ITCZ brings atmospheric instabilities on the island and weakens trade winds. This context together with the complex topography of Reunion Island, which is about 60 km wide, with two high summits (3070 and 2512 m) connected by a 1500 m plateau, makes cloudiness very heterogeneous. High cloudiness variability is found between mountain and coastal areas and between the windward, leeward and lateral regions defined with respect to the synoptic wind direction. A detailed study of local dynamics variability is necessary to better understand cloud life cycles around the island. In the presented work, our approach to explore the short-term solar irradiance forecast at local scales is to use the deterministic output from a meso-scale numerical weather prediction (NWP) model, AROME, developed by Meteo France. To start we evaluate the performance of the deterministic forecast from AROME by using meteorological measurements from 21 meteorological ground stations widely spread around the island (and with altitudes from 8 to 2245 m). Ground measurements include solar irradiation, wind speed and direction, relative humidity, air temperature, precipitation and pressure. Secondly we study in the model the local dynamics and thermodynamics that

Short-term Power Load Forecasting Based on Balanced KNN

Science.gov (United States)

Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

2018-03-01

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg; Madsen, Henrik; Nielsen, Torben Skov

2006-01-01

speed (due to the non-linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field...

Forecasted balance sheet of the power supply and demand equilibrium in France. 2007 issue

International Nuclear Information System (INIS)

2007-01-01

Conformably with the law from February 10, 2000, RTE, the French power transportation network is liable for establishing, at least every two year, a pluri-annual forecasted balance sheet of the power supply and demand equilibrium. Its aim is to identify the unbalance risks between the power consumption and the available generation means. To perform this technical expertise, RTE establishes some forecasts of domestic power consumption which are compared to the known perspectives of evolution of the production means. Two main changes have been taken into consideration in this analysis: the improvement of the energy efficiency, and the decay of power consumption in the big industry. Therefore, the new reference scenario indicates a consumption growth of 1.3% per year up to 2010 and 1% only for the next decade, i.e. 534 TWh of annual power consumption for 2020. On the offer side, several projects of new production means (mainly gas combined cycles) have been accepted during the last two years which represent more than 13000 MW of additional power. On the other hand, the decommissioning of several old fossil fuel power plants is foreseen for 2015 and represent 4400 MW. The offer based on decentralized production means is changing too, mainly thanks to the development of the wind power industry. In order to reach the supply-demand equilibrium, an acceptability threshold for failure duration has been defined by the public authorities and is limited to 3 hours per year. According to the reference scenario, the security of supplies in France seems to be reasonably assured for the next five years to come. A complement of 10500 MW will be necessary to meet the demand foreseen for 2020. (J.S.)

Villacidro solar demo plant: Integration of small-scale CSP and biogas power plants in an industrial microgrid

Science.gov (United States)

Camerada, M.; Cau, G.; Cocco, D.; Damiano, A.; Demontis, V.; Melis, T.; Musio, M.

2016-05-01

The integration of small scale concentrating solar power (CSP) in an industrial district, in order to develop a microgrid fully supplied by renewable energy sources, is presented in this paper. The plant aims to assess in real operating conditions, the performance, the effectiveness and the reliability of small-scale concentrating solar power technologies in the field of distributed generation. In particular, the potentiality of small scale CSP with thermal storage to supply dispatchable electricity to an industrial microgrid will be investigated. The microgrid will be realized in the municipal waste treatment plant of the Industrial Consortium of Villacidro, in southern Sardinia (Italy), which already includes a biogas power plant. In order to achieve the microgrid instantaneous energy balance, the analysis of the time evolution of the waste treatment plant demand and of the generation in the existing power systems has been carried out. This has allowed the design of a suitable CSP plant with thermal storage and an electrochemical storage system for supporting the proposed microgrid. At the aim of obtaining the expected energy autonomy, a specific Energy Management Strategy, which takes into account the different dynamic performances and characteristics of the demand and the generation, has been designed. In this paper, the configuration of the proposed small scale concentrating solar power (CSP) and of its thermal energy storage, based on thermocline principle, is initially described. Finally, a simulation study of the entire power system, imposing scheduled profiles based on weather forecasts, is presented.

Meteorological considerations in emergency response capability at nuclear power plant

International Nuclear Information System (INIS)

Fairobent, J.E.

1985-01-01

Meteorological considerations in emergency response at nuclear power plants are discussed through examination of current regulations and guidance documents, including discussion of the rationale for current regulatory requirements related to meteorological information for emergency response. Areas discussed include: major meteorological features important to emergency response; onsite meteorological measurements programs, including redundant and backup measurements; access to offsite sources of meteorological information; consideration of real-time and forecast conditions and atmospheric dispersion modeling

Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

International Nuclear Information System (INIS)

Garcia-Ascanio, Carolina; Mate, Carlos

2010-01-01

Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

Standardized small diesel power plants for rural electrification in Tanzania

International Nuclear Information System (INIS)

Holmqvist, A.; Soerman, J.; Gullberg, M.; Kjellstroem, B.

1993-01-01

This study focuses on small townships where the forecasted power demand stays below 500 kW during the ten first years. Case study calculations were made where two hypothetical load centres form the base. Each load centre is assumed to be supplied by two alternative standardized diesel power plants. One option is a power plant consisting of two medium speed (750 rpm) generator sets, one always on stand-by. Alternatively, a power plant consisting of three high speed (1500 rpm) generator sets is evaluated for each hypothetical load centre. The calculations clearly show that the high speed, three unit option comes out cheaper than the two unit, medium speed option in all the considered cases. The fuel costs per kWh generated are almost the same in all the cases studied, i.e. between 6 and 7 US cents. The medium speed engine tends to consume more fuel per kWh generated than the high speed, as it runs more often on part load. Consequently, the fuel costs will be slightly higher for this option. It is also of interest to compare the plant failure rate of the two options. In this study no proper probability evaluation has been made, but some general reflections can be worth considering. The availability of spare parts in Tanzania is doubtful. Many small diesel power plants presently operating have to wait indefinitely, when a failure appears that requires spare parts. As long as the individual sets have the same, or nearly the same failure rate, a three unit plant has lower probability for total loss of generating capacity than a two unit plant. The main conclusion of this evaluation is that for electricity generation in rural Tanzanian villages, power plants with three small, high speed generator sets are preferable to plants with two, medium speed generator sets. A power plant made out of small sets requires less capital, consumes less fuel and is not as likely to loose its generating capacity totally. 16 refs, 10 figs, 21 tabs

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Institute of Scientific and Technical Information of China (English)

Xia Hua; Gang Zhang; Jiawei Yang; Zhengyuan Li

2015-01-01

Aiming at the low accuracy problem of power system short⁃term load forecasting by traditional methods, a back⁃propagation artifi⁃cial neural network (BP⁃ANN) based method for short⁃term load forecasting is presented in this paper. The forecast points are re⁃lated to prophase adjacent data as well as the periodical long⁃term historical load data. Then the short⁃term load forecasting model of Shanxi Power Grid (China) based on BP⁃ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP⁃ANN method is simple and with higher precision and practicality.

Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events

Science.gov (United States)

DeChant, C. M.; Moradkhani, H.

2014-12-01

Hydrometeorological events (i.e. floods, droughts, precipitation) are increasingly being forecasted probabilistically, owing to the uncertainties in the underlying causes of the phenomenon. In these forecasts, the probability of the event, over some lead time, is estimated based on some model simulations or predictive indicators. By issuing probabilistic forecasts, agencies may communicate the uncertainty in the event occurring. Assuming that the assigned probability of the event is correct, which is referred to as a reliable forecast, the end user may perform some risk management based on the potential damages resulting from the event. Alternatively, an unreliable forecast may give false impressions of the actual risk, leading to improper decision making when protecting resources from extreme events. Due to this requisite for reliable forecasts to perform effective risk management, this study takes a renewed look at reliability assessment in event forecasts. Illustrative experiments will be presented, showing deficiencies in the commonly available approaches (Brier Score, Reliability Diagram). Overall, it is shown that the conventional reliability assessment techniques do not maximize the ability to distinguish between a reliable and unreliable forecast. In this regard, a theoretical formulation of the probabilistic event forecast verification framework will be presented. From this analysis, hypothesis testing with the Poisson-Binomial distribution is the most exact model available for the verification framework, and therefore maximizes one's ability to distinguish between a reliable and unreliable forecast. Application of this verification system was also examined within a real forecasting case study, highlighting the additional statistical power provided with the use of the Poisson-Binomial distribution.

The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

Energy Technology Data Exchange (ETDEWEB)

Martin Wilde, Principal Investigator

2012-12-31

ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational

Solar PV power forecasting using extreme machine learning and experts advice fusion

OpenAIRE

Le Cadre, Hélène; Aravena Solís, Ignacio Andrés; Papavasiliou, Anthony; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

2015-01-01

We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the aggregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d’Azur, to evaluate the algorithm performance...

Are atomic power plants saver than nuclear power plants

International Nuclear Information System (INIS)

Roeglin, H.C.

1977-01-01

It is rather impossible to establish nuclear power plants against the resistance of the population. To prevail over this resistance, a clarification of the citizens-initiatives motives which led to it will be necessary. This is to say: It is quite impossible for our population to understand what really heappens in nuclear power plants. They cannot identify themselves with nuclear power plants and thus feel very uncomfortable. As the total population feels the same way it is prepared for solidarity with the citizens-initiatives even if they believe in the necessity of nuclear power plants. Only an information-policy making transparent the social-psychological reasons of the population for being against nuclear power plants could be able to prevail over the resistance. More information about the technical procedures is not sufficient at all. (orig.) [de

Evaluation of different operational strategies for lithium ion battery systems connected to a wind turbine for primary frequency regulation and wind power forecast accuracy improvement

Energy Technology Data Exchange (ETDEWEB)

Swierczynski, Maciej; Stroe, Daniel Ioan; Stan, Ana Irina; Teodorescu, Remus; Andreasen, Soeren Juhl [Aalborg Univ. (Denmark). Dept. of Energy Technology

2012-07-01

High penetration levels of variable wind energy sources can cause problems with their grid integration. Energy storage systems connected to wind turbine/wind power plants can improve predictability of the wind power production and provide ancillary services to the grid. This paper investigates economics of different operational strategies for Li-ion systems connected to wind turbines for wind power forecast accuracy improvement and primary frequency regulation. (orig.)

Powering Up With Space-Time Wind Forecasting

KAUST Repository

Hering, Amanda S.

2010-03-01

The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.

Powering Up With Space-Time Wind Forecasting

KAUST Repository

Hering, Amanda S.; Genton, Marc G.

2010-01-01

The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.

Impact of onsite solar generation on system load demand forecast

International Nuclear Information System (INIS)

Kaur, Amanpreet; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

2013-01-01

Highlights: • We showed the impact onsite solar generation on system demand load forecast. • Forecast performance degrades by 9% and 3% for 1 h and 15 min forecast horizons. • Error distribution for onsite case is best characterized as t-distribution. • Relation between error, solar penetration and solar variability is characterized. - Abstract: Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3–54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution

Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

DEFF Research Database (Denmark)

Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

2018-01-01

The share of wind energy in total installed power capacity has grown rapidly in recent years. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build...... spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western...... Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have...

Employing modern power plant simulators in nuclear power plants

International Nuclear Information System (INIS)

Niedorf, V.; Storm, J.

2005-01-01

At the present state of the art, modern power plant simulators are characterized by new qualitative features, thus enabling operators to use them far beyond the traditional field of training. In its first part, this contribution presents an overview of the requirements to be met by simulators for multivalent uses. In part two, a survey of the uses and perspectives of simulation technology in power plants is presented on the basis of experience accumulated by Rheinmetall Defence Electronics (RDE).Modern simulators are shown to have applications by far exceeding traditional training areas. Modular client - sever systems on standard computers allow inexpensive uses to be designed at several levels, thus minimizing maintenance cost. Complex development and running time environments, like the SEMS developed by RDE, have made power plant simulators the workhorses of power plant engineers in all power plant areas. (orig.)

Evaluation of Power Generation Efficiency of Cascade Hydropower Plants: A Case Study

Directory of Open Access Journals (Sweden)

Jiahua Wei

2013-02-01

Full Text Available Effective utilization of scarce water resources has presented a significant challenge to respond to the needs created by rapid economic growth in China. In this study, the efficiency of the joint operation of the Three Gorges and Gezhouba cascade hydropower plants in terms of power generation was evaluated on the basis of a precise simulation-optimization technique. The joint operation conditions of the Three Gorges and Gezhouba hydropower plants between 2004 and 2010 were utilized in this research in order to investigate the major factors that could affect power output of the cascade complex. The results showed that the current power output of the Three Gorges and Gezhouba cascade complex had already reached around 90% of the maximum theoretical value. Compared to other influencing factors evaluated in this study, the accuracy of hydrological forecasts and flood control levels can have significant impact on the power generating efficiency, whereas the navigation has a minor influence. This research provides a solid quantitative-based methodology to assess the operation efficiency of cascade hydropower plants, and more importantly, proposes potential methods that could improve the operation efficiency of cascade hydropower plants.

Managed maintenance, the next step in power plant maintenance

International Nuclear Information System (INIS)

Butterworth, G.; Anderson, T.M.

1984-01-01

The Westinghouse Nuclear Services Integration Division managed maintenance services are described. Essential to the management and control of a total plant maintenance programme is the development of a comprehensive maintenance specification. During recent years Westinghouse has jointly developed total plant engineering-based maintenance specifications with a number of utilities. The process employed and the experience to date are described. To efficiently implement the maintenance programme Westinghouse has developed a computer software program specifically designed for day to day use at the power plant by maintenance personnel. This program retains an equipment maintenance history, schedules maintenance activities, issues work orders and performs a number of sophisticated analyses of the maintenance backlog and forecast, equipment failure rates, etc. The functions of this software program are described and details of Westinghouse efforts to support the utilities in reducing outage times through development of predefined outage plans for critical report maintenance activities are given. Also described is the experience gained in the training of specialized maintenance personnel, employing competency-based training techniques and equipment mock-ups, and the benefits experienced, in terms of improved quality and productivity of maintenance performed. The success experienced with these methods has caused Westinghouse to expand the use of these training techniques to the more routine skill areas of power plant maintenance. A significant reduction in the operating costs of nuclear power plants will only be brought about by a significant improvement in the quality of maintenance. Westinghouse intends to effect this change by expanding its international service capabilities and to make major investments in order to promote technological developments in the area of power plant maintenance. (author)

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

Directory of Open Access Journals (Sweden)

Dehua Zheng

2017-12-01

Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-05-01

Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

Data on Support Vector Machines (SVM model to forecast photovoltaic power

Directory of Open Access Journals (Sweden)

M. Malvoni

2016-12-01

Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

9{sup th} international workshop on large-scale integration of wind power into power systems as well as on transmission networks for offshore wind power plants. Proceedings

Energy Technology Data Exchange (ETDEWEB)

Betancourt, Uta; Ackermann, Thomas (eds.)

2010-07-01

Within the 9th International Workshop on large-scale integration of wind power into power systems as well as on transmission networks for offshore wind power plants at 18th to 19th October, 2010 in Quebec (Canada), lectures and poster papers were presented to the following themes: (1) Keynote session and panel; (2) European grid integration studies; (3) Modeling; (4) Wind forecasting; (5) North American grid integration studies; (6) Voltage stability and control; (7) Grid codes and impact studies; (8) Canadian University research (WESNet); (9) Operation and dispatch; (9) Offshore wind power plants; (10) Frequency Control; (11) Methodologies to estimate wind power impacts on power systems, summaries from IEAWIND collaboration; (12) HVDC; (13) Grid codes and system impact studies; (14) Modeling and validation; (15) Regulations, markets and offshore wind energy; (16) Integration issues; (17) Wind turbine control system; (18) Energy management and IT solutions.

Modelling the Economics of a New Nuclear Power Plant in Switzerland

International Nuclear Information System (INIS)

Winkler, Thomas; Streit, Marco

2008-01-01

Financing a new nuclear power plant is challenging right from the beginning and the problem of very high capital costs during the construction phase is not the only one. Long planning periods with high risk capital as well as many political influences during a long decision and planning process are the factors that are complicating the economic modelling. Nevertheless, the Net Present Value (NPV) and the Internal Rate of Return (IRR) which are calculated by discounting forecasted future cash flows are important numbers for decision makers. But these numbers strongly depend on the scenarios and input data used during the calculations. This study offers an overview of present Swiss electricity situation and the economics of the existing Swiss Nuclear Power Plants. Furthermore, a modelling tool will be introduced which allows comparing different scenarios for the whole life cycle of a nuclear power plant (planning, licensing, construction, commercial operation, decommissioning). In the third part, this study will show on a calculation example both the range of results and the different influences of escalation rates or higher costs by means of a calculation example. (authors)

Modelling the Economics of a New Nuclear Power Plant in Switzerland

Energy Technology Data Exchange (ETDEWEB)

Winkler, Thomas [University of Applied Sciences Ansbach, Residenzstrasse 8, 91522 Ansbach (Germany); Aare-Tessin Ltd for Electricity, Bahnhofquai 12, 4601 Olten (Switzerland); Streit, Marco [Aare-Tessin Ltd for Electricity, Bahnhofquai 12, 4601 Olten (Switzerland)

2008-07-01

Financing a new nuclear power plant is challenging right from the beginning and the problem of very high capital costs during the construction phase is not the only one. Long planning periods with high risk capital as well as many political influences during a long decision and planning process are the factors that are complicating the economic modelling. Nevertheless, the Net Present Value (NPV) and the Internal Rate of Return (IRR) which are calculated by discounting forecasted future cash flows are important numbers for decision makers. But these numbers strongly depend on the scenarios and input data used during the calculations. This study offers an overview of present Swiss electricity situation and the economics of the existing Swiss Nuclear Power Plants. Furthermore, a modelling tool will be introduced which allows comparing different scenarios for the whole life cycle of a nuclear power plant (planning, licensing, construction, commercial operation, decommissioning). In the third part, this study will show on a calculation example both the range of results and the different influences of escalation rates or higher costs by means of a calculation example. (authors)

U.S. plans for new nuclear power plants: who, what, how, why?

International Nuclear Information System (INIS)

Petroll, M.; Tveiten, B.

2007-01-01

Energy forecasts predict electricity consumption in the United States of America to rise 40 percent by 2030. The new baseload capacity to be added on different scales for different regions can be met by coal fired or by nuclear power plants. Climate change is increasingly seen as the No. 1 environmental problem. It is to be expected that measures of carbon dioxide control will inflict an economic penalty on the use of coal. More than a dozen firms are examining the construction of new nuclear power plants. The licensing procedure was reformed so as to answer questions of safety and environmental impact before any decisions to build are taken. Combined construction and operating permits as well as standardization and tight project management are to help avoid past mistakes. The first application for building permits will probably be filed in the autumn. The government, within its policy supporting low-emission power technologies, created incentives for building new nuclear power plants. In addition to financing, there are a number of other problems to be solved, i.e. hiring qualified labor; ensuring the supply chain; expanding the high-voltage power grid. As nuclear power policy in the US is less polarized than in Germany, the construction of new nuclear power plants is not going to depend on the next presidential elections. (orig.)

Probabilistic forecasting of wind power at the minute time-scale with Markov-switching autoregressive models

DEFF Research Database (Denmark)

Pinson, Pierre; Madsen, Henrik

2008-01-01

Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed...... consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute. The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed....... methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise...

Detecting, categorizing and forecasting large romps in wind farm power output using meteorological observations and WPPT

DEFF Research Database (Denmark)

Cutler, N.; Kay, M.; Jacka, K.

2007-01-01

The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind form. This article analyses the general performance of WPPT as well as its performance during large romps...... (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real-time wind power observations to make hourly...... forecasts of the wind farm power output. The general performances of MesoLAPS and WPPTore evaluated over I year using the root mean square error (RMSE). The errors are significantly lower than for basic benchmark forecasts but higher than for many other WPPT installations, where the site conditions...

Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine)

Science.gov (United States)

Antonenkov, D. V.; Solovev, D. B.

2017-10-01

The article covers the aspects of forecasting and consideration of the wholesale market environment in generating the power demand forecast. Major mining companies that operate in conditions of the present day power market have to provide a reliable energy demand request for a certain time period ahead, thus ensuring sufficient reduction of financial losses associated with deviations of the actual power demand from the expected figures. Normally, under the power supply agreement, the consumer is bound to provide a per-month and per-hour request annually. It means that the consumer has to generate one-month-ahead short-term and medium-term hourly forecasts. The authors discovered that empiric distributions of “Yakutugol”, Holding Joint Stock Company, power demand belong to the sustainable rank parameter H-distribution type used for generating forecasts based on extrapolation of such distribution parameters. For this reason they justify the need to apply the mathematic rank analysis in short-term forecasting of the contracted power demand of “Neryungri” coil strip mine being a component of the technocenosis-type system of the mining company “Yakutugol”, Holding JSC.

Development of hydrogeological modelling approaches for assessment of consequences of hazardous accidents at nuclear power plants

International Nuclear Information System (INIS)

Rumynin, V.G.; Mironenko, V.A.; Konosavsky, P.K.; Pereverzeva, S.A.

1994-07-01

This paper introduces some modeling approaches for predicting the influence of hazardous accidents at nuclear reactors on groundwater quality. Possible pathways for radioactive releases from nuclear power plants were considered to conceptualize boundary conditions for solving the subsurface radionuclides transport problems. Some approaches to incorporate physical-and-chemical interactions into transport simulators have been developed. The hydrogeological forecasts were based on numerical and semi-analytical scale-dependent models. They have been applied to assess the possible impact of the nuclear power plants designed in Russia on groundwater reservoirs

Forecast of energy demand in China and introduction of nuclear power using the clean development mechanism

International Nuclear Information System (INIS)

Ikemoto, Ichiro

2003-01-01

As an economic energy source with low greenhouse gas emissions and essentially no resource limitations, nuclear power is a promising option for meeting the rapidly growing energy demands of China that is being driven by rapid population and economic growth. This paper examines an introduction scenario for nuclear power in China by using the clean development mechanism, based on quantitative evaluation of energy demand forecasts and the nuclear fuel cycle through 2100. The results of the case study concluded that in the short to mid term, large-scale light water reactors will primarily be sited in coastal areas where infrastructure development is advanced. In the future, as dispersed power sources in inland areas, small scale FBRs will be preferred due to their promising safety, operation and maintenance characteristics, ease of transportation of plant equipment and plant construction and the possibility of on-site nuclear fuel cycle. Evaluation of nuclear fuel cycle showed that this introduction scenario is feasible considering natural Uranium demand, Uranium enrichment capacity and reprocessing capacity. (author)

Combined time-varying forecast based on the proper scoring approach for wind power generation

DEFF Research Database (Denmark)

Chen, Xingying; Jiang, Yu; Yu, Kun

2017-01-01

Compared with traditional point forecasts, combined forecast have been proposed as an effective method to provide more accurate forecasts than individual model. However, the literature and research focus on wind-power combined forecasts are relatively limited. Here, based on forecasting error...... distribution, a proper scoring approach is applied to combine plausible models to form an overall time-varying model for the next day forecasts, rather than weights-based combination. To validate the effectiveness of the proposed method, real data of 3 years were used for testing. Simulation results...... demonstrate that the proposed method improves the accuracy of overall forecasts, even compared with a numerical weather prediction....

Kansas Power Plants

Data.gov (United States)

Kansas Data Access and Support Center — The Kansas Power Plants database depicts, as point features, the locations of the various types of power plant locations in Kansas. The locations of the power plants...

Economic consequences of alternative nuclear power plant lifetimes in Germany

International Nuclear Information System (INIS)

Lindenberger, D.; Wissen, R.; Bartels, M.; Buttermann, H.G.; Hillebrand, B.

2006-01-01

The coalition agreement of the Christian Democratic (CDU), Christian Social (CSU), and Social Democratic (SPD) parties contains a provision under which the existing regulations about phasing out the peaceful use of nuclear power will remain in force because of different opinions about the use of nuclear power in Germany. This article studies the consequences of longer lifetimes of the nuclear power plants currently in operation as compared to the provisions in opt-out legislation. The details examined include the effects of longer nuclear power plant lifetimes on the development of generating capacities in Germany, electricity generation, fuel consumption and fuel imports, the resultant CO 2 emissions, costs of electricity generation and electricity prices as well as the associated impact on production and employment in this sector and in industry as a whole. A summary is presented of the findings of a comprehensive study published under the same title in October 2005. The study was compiled by the Institute of Power Economics of the University of Cologne (EWI) and by Energy Environment Forecast Analysis GmbH, and had been commissioned by the Federation of German Industries (BDI). (orig.)

Probabilistic wind power forecasting with online model selection and warped gaussian process

International Nuclear Information System (INIS)

Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

2014-01-01

Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

A short-term ensemble wind speed forecasting system for wind power applications

Science.gov (United States)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

Directory of Open Access Journals (Sweden)

Jiani Heng

2016-01-01

Full Text Available Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm, and WNN (Wavelet Neural Network, is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network models such as BPNN (Back Propagation Neural Network, GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm, and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

Science.gov (United States)

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

DEFF Research Database (Denmark)

Tastu, Julija; Pinson, Pierre; Kotwa, Ewelina

2011-01-01

of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power......Forecasts of wind power production are increasingly being used in various management tasks. So far, such forecasts and related uncertainty information have usually been generated individually for a given site of interest (either a wind farm or a group of wind farms), without properly accounting...

Hydroelectric Power Plants Dobsina

International Nuclear Information System (INIS)

Majercak, V.; Srenkelova, Z.; Kristak, J.G.

1997-01-01

In this brochure the Hydroelectric Power Plants Dobsina, (VED), subsidiary of the utility Slovenske Elektrarne, a.s. (Slovak Electric, plc. Bratislava) are presented. VED is mainly aimed at generating peak-load electrical energy and maintenance of operational equipment. Reaching its goals, company is first of all focused on reliability of production, economy and effectiveness, keeping principles of work safety and industry safety standards and also ecology. VED operates eight hydroelectric power plants, from which PVE Ruzin I and PVE Dobsina I are pump storage ones and they are controlled directly by the Slovak Energy Dispatch Centre located in Zilina thought the system LS 3200. Those power plants participate in secondary regulation of electrical network of Slovakia. They are used to compensate balance in reference to foreign electrical networks and they are put into operation independently from VED. Activity of the branch is focused mainly on support of fulfilment of such an important aim as electric network regulation. Beginnings of the subsidiary Hydroelectric Power Plants Dobsina are related to the year of 1948. After commissioning of the pump storage Hydroelectric Power Plants Dobsina in 1953, the plant started to carry out its mission. Since that time the subsidiary has been enlarged by other seven power plants, through which it is fulfilling its missions nowadays. The characteristics of these hydroelectric power plants (The pump-storage power plant Dobsina, Small hydroelectric power plant Dobsina II, Small hydroelectric power plant Rakovec, Small hydroelectric power plant Svedlar, Hydroelectric power plant Domasa, The pump-storage power plant Ruzin, and Small hydroelectric power plant Krompachy) are described in detail. Employees welfare and public relations are presented

Do we need demographic data to forecast plant population dynamics?

Science.gov (United States)

Tredennick, Andrew T.; Hooten, Mevin B.; Adler, Peter B.

2017-01-01

Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction.In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types.In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist

Comparative studies between nuclear power plants and hydroelectric power plants

International Nuclear Information System (INIS)

Menegassi, J.

1984-01-01

This paper shows the quantitative evolution of the power plants in the main countries of the world. The Brazilian situation is analysed, with emphasys in the technical and economical aspects related to power production by hidroelectric or nuclear power plants. The conclusion is that the electricity produced by hidro power plants becomes not economics when is intended to be produced at large distances from the demand centers. (Author) [pt

An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power

Directory of Open Access Journals (Sweden)

Antonio Bracale

2015-09-01

Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.

Sea water pumping-up power plant system combined with nuclear power plant

International Nuclear Information System (INIS)

Ichiki, Tadaharu; Tanaka, Masayuki.

1991-01-01

It is difficult to find a site suitable to construction for a sea water pumping-up power plant at a place relatively near the electric power consumption area. Then, a nuclear power plant is set at the sea bottom or the land portion of a sea shore near the power consumption area. A cavity is excavated underground or at the bottom of the sea in the vicinity of the power plant to form a lower pond, and the bottom of the sea, as an upper pond and the lower pond are connected by a water pressure pipe and a water discharge pipe. A pump water turbine is disposed therebetween, to which electric power generator is connected. In addition, an ordinary or emergency cooling facility in the nuclear power plant is constituted such that sea water in the cavity is supplied by a sea water pump. Accordingly, the sea water pumping-up plant system in combination with the nuclear power plant is constituted with no injuring from salts to animals and plants on land in the suburbs of a large city. The cost for facilities for supplying power from a remote power plant to large city areas and power loss are decreased and stable electric power can be supplied. (N.H.)

The Possibility Using the Power Production Function of Complex Variable for Economic Forecasting

Directory of Open Access Journals (Sweden)

Sergey Gennadyevich Svetunkov

2016-09-01

Full Text Available The possibility of dynamic analysis and forecasting production results using the power production functions of complex variables with real coefficients is considered. This model expands the arsenal of instrumental methods and allows multivariate production forecasts which are unattainable by other methods of real variables as the functions of complex variables simulate the production differently in comparison with the models of real variables. The values of coefficients of the power production functions of complex variables can be calculated for each statistical observation. This allows to consider the change of the coefficients over time, to analyze this trend and predict the values of the coefficients for a given term, thereby to predict the form of the production function, which forecasts the operating results. Thus, the model of the production function with variable coefficients is introduced into the scientific circulation. With this model, the inverse problem of forecasting might be solved, such as the determination of the necessary quantities of labor and capital to achieve the desired operational results. The study is based on the principles of the modern methodology of complex-valued economy, one of its sections is the complex-valued patterns of production functions. In the article, the possibility of economic forecasting is tested on the example of the UK economy. The results of this prediction are compared with the forecasts obtained by other methods, which have led to the conclusion about the effectiveness of the proposed approach and the method of forecasting at the macro levels of production systems. A complex-valued power model of the production function is recommended for the multivariate prediction of sustainable production systems — the global economy, the economies of individual countries, major industries and regions.

Increasing the temporal resolution of direct normal solar irradiance forecasted series

Science.gov (United States)

Fernández-Peruchena, Carlos M.; Gastón, Martin; Schroedter-Homscheidt, Marion; Marco, Isabel Martínez; Casado-Rubio, José L.; García-Moya, José Antonio

2017-06-01

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain

International Nuclear Information System (INIS)

González-Aparicio, I.; Zucker, A.

2015-01-01

Highlights: • Reduction wind power forecasting uncertainty for day ahead and intraday markets. • Statistical relationship between total load and wind power generation. • Accurately forecast expected revenues from wind producer’s perspective. - Abstract: The growing share of electricity production from variable renewable energy sources increases the stochastic nature of the power system. This has repercussions on the markets for electricity. Deviations from forecasted production schedules require balancing of a generator’s position within a day. Short term products that are traded on power and/or reserve markets have been developed for this purpose, providing opportunities to actors who can offer flexibility in the short term. The value of flexibility is typically modelled using stochastic scenario extensions of dispatch models which requires, as a first step, understanding the nature of forecast uncertainties. This study provides a new approach for determining the forecast errors of wind power generation in the time period between the closure of the day ahead and the opening of the first intraday session using Spain as an example. The methodology has been developed using time series analysis for the years 2010–2013 to find the explanatory variables of the wind error variability by applying clustering techniques to reduce the range of uncertainty, and regressive techniques to forecast the probability density functions of the intra-day price. This methodology has been tested considering different system actions showing its suitability for developing intra-day bidding strategies and also for the generation of electricity generated from Renewable Energy Sources scenarios. This methodology could help a wind power producer to optimally bid into the intraday market based on more accurate scenarios, increasing their revenues and the system value of wind.

Evaluation of Nonparametric Probabilistic Forecasts of Wind Power

DEFF Research Database (Denmark)

Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008

Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...

Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

DEFF Research Database (Denmark)

Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

2013-01-01

A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time...

Concerning the justiciability of demand forecasts

International Nuclear Information System (INIS)

Nierhaus, M.

1977-01-01

This subject plays at present in particular a role in the course of judicial examinations of immediately enforceable orders for the partial construction licences of nuclear power plants. The author distinguishes beween three kinds of forecast decisions: 1. Appraising forecast decisions with standards of judgment taken mainly from the fields of the art, culture, morality, religion are, according to the author, only legally verifyable to a limited extent. 2. With regard to forecast decisions not arguable, e.g. where the future behaviour of persons is concerned, the same should be applied basically. 3. In contrast to this, the following is applicable for programmatic, proceedingslike, or creative forecast decisions, in particular in economics: 'An administrative estimation privilege in a prognostic sense with the consequence that the court has to accept the forecast decision which lies within the forecast margins and which cannot be disproved, and that the court may not replace this forecast decision by its own probability judgment. In these cases, administration has the right to create its own forecast standards.' Judicial control in these cases was limited to certain substantive and procedural mistakes made by the administration in the course of forecast decision finding. (orig./HP) [de

Concerning the justiciability of demand forecasts

Energy Technology Data Exchange (ETDEWEB)

Nierhaus, M [Koeln Univ. (Germany, F.R.)

1977-01-01

This subject plays at present in particular a role in the course of judicial examinations of immediately enforceable orders for the partial construction licences of nuclear power plants. The author distinguishes beween three kinds of forecast decisions: 1. Appraising forecast decisions with standards of judgment taken mainly from the fields of the art, culture, morality, religion are, according to the author, only legally verifyable to a limited extent. 2. With regard to forecast decisions not arguable, e.g. where the future behaviour of persons is concerned, the same should be applied basically. 3. In contrast to this, the following is applicable for programmatic, proceedingslike, or creative forecast decisions, in particular in economics: 'An administrative estimation privilege in a prognostic sense with the consequence that the court has to accept the forecast decision which lies within the forecast margins and which cannot be disproved, and that the court may not replace this forecast decision by its own probability judgment. In these cases, administration has the right to create its own forecast standards.' Judicial control in these cases was limited to certain substantive and procedural mistakes made by the administration in the course of forecast decision finding.

Prospects of nuclear power plants for sustainable energy development in Islamic Republic of Iran

International Nuclear Information System (INIS)

Ghorashi, Amir Hossien

2007-01-01

This paper presents the feasible contributive share of electricity generation from each energy resources. This includes the economical feasibilities and all demographic projections involved in forecasting methodology, which explicitly reflect on overall national power demand projection in the Energy prospects of Islamic Republic of Iran till 2033. The Energy demand and reliability are presented with a view to elaborate on significant role and required capacity of Nuclear Power Plants (NPP) towards fulfillment of an energy mix policy in the country

Research on energy supply, demand and economy forecasting in Japan

International Nuclear Information System (INIS)

Shiba, Tsuyoshi; Kamezaki, Hiroshi; Yuyama, Tomonori; Suzuki, Atsushi

1999-10-01

This project aims to do research on forecasts of energy demand structure and electricity generation cost in each power plant in Japan in the 21st century, considering constructing successful FBR scenario. During the process of doing research on forecasts of energy demand structure in Japan, documents published from organizations in inside and outside of Japan were collected. These documents include prospects of economic growth rate, forecasts of amount for energy supply and demand, the maximum amount of introducing new energy resources, CO2 regulation, and evaluation of energy best mixture. Organizations in Japan such as Economic Council and Japan Energy Economic Research Institute have provided long-term forecasts until the early 21st century. Meanwhile, organizations overseas have provided forecasts of economic structure, and demand and supply for energy in OECD and East Asia including Japan. In connection with forecasts of electricity generation cost in each power plant, views on the ultimate reserves and cost of resources are reviewed in this report. According to some views on oil reserves, making assumptions based on reserves/production ratio, the maximum length of the time that oil reserves will last is 150 years. In addition, this report provides summaries of cost and potential role of various resources, including solar energy and wind energy; and views on waste, safety, energy security-related externality cost, and the price of transferring CO2 emission right. (author)

Day ahead forecast of wind power through optimal application of multivariate analyzing methods

Energy Technology Data Exchange (ETDEWEB)

Arnoldt, Alexander; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group

2011-07-01

This paper presents two algorithms in identifying input models for artificial neural networks. The algorithms are based on an entropy analysis and an eigenvalue analysis of the correlation matrix. The resulting input models are used for investigating a feed forward and a recurrent artificial neural network structure to simulate a 24 hour forecast of wind power production. The limitation of the forecast error distribution is investigated through successful implementation of hybridization of single forecast models. Errors of the best forecast model stay between a normalized root mean square error from 3.5% to 6.1%. (orig.)

Outlook for gas turbine plant utilization in htgr power facilities

International Nuclear Information System (INIS)

Beknev, V.S.; Leont'ev, A.I.; Shmidt, K.L.; Surovtsev, I.G.

1983-01-01

The nuclear reactor power plants that have found greatest favor in the nuclear power industry worldwide are pressurized water reactors, boiling-water reactors, and uranium-graphite channel reactors with saturated-steam steam turbine units (PTU). The efficiency of power generating stations built around reactors such as these does not exceed 30 to 32%, and furthermore they are ''tied down'' to water reservoirs, with the entailed severe thermal effects on the environmental surroundings. The low efficiency range cited is evidence of inefficacious utilization of the nuclear fuel, reserves of which have their limits just as there are limits to available reserves of fossil fuels. Forecasts are being floated of a possible uranium crisis (profitable mining of uranium) in the mid-1990's, even with the expected development of breeder reactors to bridge the gap

Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines

International Nuclear Information System (INIS)

Li, Yanting; He, Yong; Su, Yan; Shu, Lianjie

2016-01-01

Highlights: • Suggests a nonparametric model based on MARS for output power prediction. • Compare the MARS model with a wide variety of prediction models. • Show that the MARS model is able to provide an overall good performance in both the training and testing stages. - Abstract: Both linear and nonlinear models have been proposed for forecasting the power output of photovoltaic systems. Linear models are simple to implement but less flexible. Due to the stochastic nature of the power output of PV systems, nonlinear models tend to provide better forecast than linear models. Motivated by this, this paper suggests a fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS), as an alternative to forecasting of solar power output. The MARS model is a data-driven modeling approach without any assumption about the relationship between the power output and predictors. It maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity. It is simpler in format than other nonlinear models such as ANN, k-nearest neighbors (KNN), classification and regression tree (CART), and support vector machine (SVM). The MARS model was applied on the daily output of a grid-connected 2.1 kW PV system to provide the 1-day-ahead mean daily forecast of the power output. The comparisons with a wide variety of forecast models show that the MARS model is able to provide reliable forecast performance.

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

Energy Technology Data Exchange (ETDEWEB)

Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

2011-04-15

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (author)

Advancing solar energy forecasting through the underlying physics

Science.gov (United States)

Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

2017-12-01

As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

Modeling and forecasting of electrical power demands for capacity planning

International Nuclear Information System (INIS)

Al-Shobaki, S.; Mohsen, M.

2007-01-01

Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs

Modeling and forecasting of electrical power demands for capacity planning

Energy Technology Data Exchange (ETDEWEB)

Al-Shobaki, S. [Hashemite Univ., Zarka (Jordan). Dept. of Industrial Engineering; Mohsen, M. [Hashemite Univ., Zarka (Jordan). Dept. of Mechanical Engineering

2007-07-01

Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs.

Integration of wind generation forecasts. Volume 2

International Nuclear Information System (INIS)

Ahlstrom, M.; Zavadil, B.; Jones, L.

2005-01-01

WindLogics is a company that specializes in atmospheric modelling, visualization and fine-scale forecasting systems for the wind power industry. A background of the organization was presented. The complexities of wind modelling were discussed. Issues concerning location and terrain, shear, diurnal and interannual variability were reviewed. It was suggested that wind power producers should aim to be mainstream, and that variability should be considered as intrinsic to fuel supply. Various utility operating impacts were outlined. Details of an Xcel NSP wind integration study were presented, as well as a studies conducted in New York state and Colorado. It was concluded that regulations and load following impacts with wind energy integration are modest. Overall impacts are dominated by costs incurred to accommodate wind generation variability and uncertainty in the day-ahead time frame. Cost impacts can be reduced with adjustments to operating strategies, improvements in wind forecasting and access to real-time markets. Details of WindLogic's wind energy forecast system were presented, as well as examples of day ahead and hour ahead forecasts and wind speed and power forecasts. Screenshots of control room integration, EMS integration and simulations were presented. Details of a utility-scale wind energy forecasting system funded by Xcel Renewable Development Fund (RDF) were also presented. The goal of the system was to optimize the way that wind forecast information is integrated into the control room environment. Project components were outlined. It was concluded that accurate day-ahead forecasting can lead to significant asset optimization. It was recommended that wind plants share data, and aim to resolve issues concerning grid codes and instrumentation. refs., tabs., figs

Application of linear scheduling method (LSM) for nuclear power plant (NPP) construction

International Nuclear Information System (INIS)

Kim, Woojoong; Ryu, Dongsoo; Jung, Youngsoo

2014-01-01

Highlights: • Mixed use of linear scheduling method with traditional CPM is suggested for NPP. • A methodology for selecting promising areas for LSM application is proposed. • A case-study is conducted to validate the proposed LSM selection methodology. • A case-study of reducing NPP construction duration by using LSM is introduced. - Abstract: According to a forecast, global energy demand is expected to increase by 56% from 2010 to 2040 (EIA, 2013). The nuclear power plant construction market is also growing with sharper competition. In nuclear power plant construction, scheduling is one of the most important functions due to its large size and complexity. Therefore, it is crucial to incorporate the ‘distinct characteristics of construction commodities and the complex characteristics of scheduling techniques’ (Jung and Woo, 2004) when selecting appropriate schedule control methods for nuclear power plant construction. However, among various types of construction scheduling techniques, the traditional critical path method (CPM) has been used most frequently in real-world practice. In this context, the purpose of this paper is to examine the viability and effectiveness of linear scheduling method (LSM) applications for specific areas in nuclear power plant construction. In order to identify the criteria for selecting scheduling techniques, the characteristics of CPM and LSM were compared and analyzed first through a literature review. Distinct characteristics of nuclear power plant construction were then explored by using a case project in order to develop a methodology to select effective areas of LSM application to nuclear power plant construction. Finally, promising areas for actual LSM application are suggested based on the proposed evaluation criteria and the case project. Findings and practical implications are discussed for further implementation

Application of linear scheduling method (LSM) for nuclear power plant (NPP) construction

Energy Technology Data Exchange (ETDEWEB)

Kim, Woojoong, E-mail: minidung@nate.com [Central Research Institute, Korea Hydro and Nuclear Power Co., Ltd, Daejeon 305-343 (Korea, Republic of); Ryu, Dongsoo, E-mail: energyboy@khnp.co.kr [Central Research Institute, Korea Hydro and Nuclear Power Co., Ltd, Daejeon 305-343 (Korea, Republic of); Jung, Youngsoo, E-mail: yjung97@mju.ac.kr [College of Architecture, Myongji University, Yongin 449-728 (Korea, Republic of)

2014-04-01

Highlights: • Mixed use of linear scheduling method with traditional CPM is suggested for NPP. • A methodology for selecting promising areas for LSM application is proposed. • A case-study is conducted to validate the proposed LSM selection methodology. • A case-study of reducing NPP construction duration by using LSM is introduced. - Abstract: According to a forecast, global energy demand is expected to increase by 56% from 2010 to 2040 (EIA, 2013). The nuclear power plant construction market is also growing with sharper competition. In nuclear power plant construction, scheduling is one of the most important functions due to its large size and complexity. Therefore, it is crucial to incorporate the ‘distinct characteristics of construction commodities and the complex characteristics of scheduling techniques’ (Jung and Woo, 2004) when selecting appropriate schedule control methods for nuclear power plant construction. However, among various types of construction scheduling techniques, the traditional critical path method (CPM) has been used most frequently in real-world practice. In this context, the purpose of this paper is to examine the viability and effectiveness of linear scheduling method (LSM) applications for specific areas in nuclear power plant construction. In order to identify the criteria for selecting scheduling techniques, the characteristics of CPM and LSM were compared and analyzed first through a literature review. Distinct characteristics of nuclear power plant construction were then explored by using a case project in order to develop a methodology to select effective areas of LSM application to nuclear power plant construction. Finally, promising areas for actual LSM application are suggested based on the proposed evaluation criteria and the case project. Findings and practical implications are discussed for further implementation.

PA activity by using nuclear power plant safety demonstration and analysis

International Nuclear Information System (INIS)

Tsuchiya, Mitsuo; Kamimae, Rie

1999-01-01

INS/NUPEC presents one of Public acceptance (PA) methods for nuclear power in Japan, 'PA activity by using Nuclear Power Plant Safety Demonstration and Analysis', by using one of videos which is explained and analyzed accident events (Loss of Coolant Accident). Safety regulations of The National Government are strictly implemented in licensing at each of basic design and detailed design. To support safety regulation activities conducted by the National Government, INS/NLTPEC continuously implement Safety demonstration and analysis. With safety demonstration and analysis, made by assuming some abnormal conditions, what impacts could be produced by the assumed conditions are forecast based on specific design data on a given nuclear power plants. When analysis results compared with relevant decision criteria, the safety of nuclear power plants is confirmed. The decision criteria are designed to help judge if or not safety design of nuclear power plants is properly made. The decision criteria are set in the safety examination guidelines by taking sufficient safety allowance based on the latest technical knowledge obtained from a wide range of tests and safety studies. Safety demonstration and analysis is made by taking the procedure which are summarized in this presentation. In Japan, various PA (Public Acceptance) pamphlets and videos on nuclear energy have been published. But many of them focused on such topics as necessity or importance of nuclear energy, basic principles of nuclear power generation, etc., and a few described safety evaluation particularly of abnormal and accident events in accordance with the regulatory requirements. In this background, INS/NUPEC has been making efforts to prepare PA pamphlets and videos to explain the safety of nuclear power plants, to be simple and concrete enough, using various analytical computations for abnormal and accident events. In results, PA activity of INS/NUPEC is evaluated highly by the people

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

Science.gov (United States)

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Nuclear power plant life management. Proceedings of a symposium

International Nuclear Information System (INIS)

2003-01-01

Presently, an area of major interest of the IAEA is the management of the nuclear power plant (NPP) life cycle from concept development to decommissioning and disposal, with the primary objective of maximising the return on investment in nuclear facilities through efficient operation of NPPs. 441 NPPs, with a capacity of about 350GW(e) supplied 16% of global electricity in 2002. Of these, about 300 NPPs have been in operation for 15 years or more and these older units with partially or fully amortized capital costs have proven to be the most profitable. Moreover, there are no significant safety or economic reasons not to continue the operation of well managed NPPs over a longer period and consequently the issues of plant life management and license extension are receiving increasing emphasis in many countries. Forecasts of nuclear power growth over the next two decades range from 350GW(e) in the worst case to 500GW(e) in the best case. This will need additional personnel and expansion of the infrastructure in the developing countries, particularly as much of the new demand growth is forecast to take place outside the countries where most of the existing infrastructure resides. All aspects of NPP life cycle management are addressed by the IAEA and are briefly described in these proceedings. The IAEA Technical Working Group on Life Management of Nuclear Power Plants (TWG-LMNPP) recommended, during its regular meeting in February 1999, that the IAEA should consider holding a symposium on this subject area in 2002. This TWG-LMNPP Proposal was approved and, this symposium was held, attended by 138 participants from 32 Member States and 2 international organizations. The objectives of the symposium were as follows: Emphasise the role of NPP life management programmes in assuring a safe and reliable NPP operating cycle; Identify progress in methodological and technological developments for managing ageing processes and understanding ageing mechanisms; Provide a forum for

Inclusion of routine wind and turbulence forecasts in the Savannah River Plant's emergency response capabilities

International Nuclear Information System (INIS)

Pendergast, M.M.; Gilhousen, D.B.

1980-01-01

The Savannah River Plant's emergency response computer system was improved by the implementation of automatic forecasts of wind and turbulence for periods up to 30 hours. The forecasts include wind direction, wind speed, and horizontal and vertical turbulence intensity at 10, 91, and 243 m above ground for the SRP area, and were obtained by using the Model Output Statistics (MOS) technique. A technique was developed and tested to use the 30-hour MOS forecasts of wind and turbulence issued twice daily from the National Weather Service at Suitland, Maryland, into SRP's emergency response program. The technique for combining MOS forecasts, persistence, and adjusted-MOS forecast is used to generate good forecasts any time of day. Wind speed and turbulence forecasts have been shown to produce smaller root mean square errors (RMSE) than forecasts of persistence for time periods over about two hours. For wind direction, the adjusted-MOS forecasts produce smaller RMSE than persistence for times greater than four hours

Risk-averse formulations and methods for a virtual power plant

KAUST Repository

Lima, Ricardo M.; Conejo, Antonio J.; Langodan, Sabique; Hoteit, Ibrahim; Knio, Omar M.

2017-01-01

In this paper we address the optimal operation of a virtual power plant using stochastic programming. We consider one risk-neutral and two risk-averse formulations that rely on the conditional value at risk. To handle large-scale problems, we implement two decomposition methods with variants using single- and multiple-cuts. We propose the utilization of wind ensembles obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) to quantify the uncertainty of the wind forecast. We present detailed results relative to the computational performance of the risk-averse formulations, the decomposition methods, and risk management and sensitivities analysis as a function of the number of scenarios and risk parameters. The implementation of the two decomposition methods relies on the parallel solution of subproblems, which turns out to be paramount for computational efficiency. The results show that one of the two decomposition methods is the most efficient.

Risk-averse formulations and methods for a virtual power plant

KAUST Repository

Lima, Ricardo M.

2017-12-15

In this paper we address the optimal operation of a virtual power plant using stochastic programming. We consider one risk-neutral and two risk-averse formulations that rely on the conditional value at risk. To handle large-scale problems, we implement two decomposition methods with variants using single- and multiple-cuts. We propose the utilization of wind ensembles obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) to quantify the uncertainty of the wind forecast. We present detailed results relative to the computational performance of the risk-averse formulations, the decomposition methods, and risk management and sensitivities analysis as a function of the number of scenarios and risk parameters. The implementation of the two decomposition methods relies on the parallel solution of subproblems, which turns out to be paramount for computational efficiency. The results show that one of the two decomposition methods is the most efficient.

Reducing the network load and optimization of the economic efficiency of CHP plants by forecast-guided control; Verringerung der Netzbelastung und Optimierung der Wirtschaftlichkeit von KWK-Anlagen durch prognosegefuehrte Steuerung

Energy Technology Data Exchange (ETDEWEB)

Glaser, Daniel; Adelhardt, Stefan [Erlangen-Nuernberg Univ., Erlangen (Germany). Lehrstuhl fuer Sensorik; beECO GmbH, Erlangen (Germany)

2012-07-01

Heat-guided combined heat and power (CHP) plants often cause large compensation energy amounts, additional costs to the operator respectively and another burden on the parent network. The balance energy is caused by errors in the production forecast whose quality heavily depends on the heat load performance. This paper identifies the forecasting problems with heat-guided CHP and reveals how the accompanying cost and the network burden can be reduced. This is achieved by an improvement of the forecast in conjunction with a forecast-guided control without affecting the heat supply. In addition, an outlook on further measures to the earnings with the system is presented. (orig.)

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Directory of Open Access Journals (Sweden)

Haoran Zhao

2018-03-01

Full Text Available As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N decomposition approach, the Least Square Support Vector Machine (LSSVM, and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA. For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM, and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM, it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

Less power plants

International Nuclear Information System (INIS)

TASR

2003-01-01

In the Slovak Republic the number of company power plants decreased as against 2001 by two sources. In present time only 35 companies have their own power plants. The companies Slovnaft, Kappa Sturovo, Slovensky hodvab Senica, Matador Puchov, Maytex Liptovsky MikuIas, Kovohuty Krompachy, Chemko Strazske and some Slovak sugar factories belong to the largest company power plants in force of distributing companies. Installed output of present 35 company sources is 531 MW. The largest of separate power plants as Paroplynovy cyklus Bratislava (218 MW) and VD Zilina (72 MW) belong to independent sources. Total installed output of Slovak sources was 8306 MW in the end of last year

Forecast demand and supply of energy in the short period. Its forecast and sensitivity analysis until the 2004 fiscal year

International Nuclear Information System (INIS)

Yamashita, Yukari; Suehiro, Shigeru; Yanagisawa, Akira; Imaeda, Toshiya; Komiyama, Ryouichi

2004-01-01

The object of this report is forecast demand and supply of energy in the 2003 and 2004 fiscal year, which correspond to a business recovery period. A macroeconomics model and an energy supply model are calculated by changing actual GNP, crude oil rate and the rerunning period of nuclear power plants. The calculation results are compared with the reference case. In the first chapter, forecast Japanese economy until the 2004 fiscal year is explained. In the second chapter, the results of energy demand and supply in the first chapter are investigated by the home supply and consumption of primary energy (the reference case) and each energy resources. The sensitivity analytical results of actual GNP, consumer price index, home supply of the primary energy, energy expenditure, sales account of electric power, city gas and fuel by five cases such as reference, increase and decrease of oil cost and increase and decrease of economic growth are investigated. The effects of fast rerunning period of nuclear power plant and atmosphere temperature on these above demands of energies are indicated in the third chapter. (S.Y.)

Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting

International Nuclear Information System (INIS)

Tang, Pingzhou; Chen, Di; Hou, Yushuo

2016-01-01

As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.

Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Directory of Open Access Journals (Sweden)

Zhongrong Zhang

2016-01-01

Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

The reliability evaluation of reclaimed water reused in power plant project

Science.gov (United States)

Yang, Jie; Jia, Ru-sheng; Gao, Yu-lan; Wang, Wan-fen; Cao, Peng-qiang

2017-12-01

The reuse of reclaimed water has become one of the important measures to solve the shortage of water resources in many cities, But there is no unified way to evaluate the engineering. Concerning this issue, it took Wanneng power plant project in Huai city as a example, analyzed the reliability of wastewater reuse from the aspects of quality in reclaimed water, water quality of sewage plant, the present sewage quantity in the city and forecast of reclaimed water yield, in particular, it was necessary to make a correction to the actual operation flow rate of the sewage plant. the results showed that on the context of the fluctuation of inlet water quality, the outlet water quality of sewage treatment plants is basically stable, and it can meet the requirement of circulating cooling water, but suspended solids(SS) and total hardness in boiler water exceed the limit, and some advanced treatment should be carried out. In addition, the total sewage discharge will reach 13.91×104m3/d and 14.21×104m3/d respectively in the two planning level years of the project. They are greater than the normal collection capacity of the sewage system which is 12.0×104 m3/d, and the reclaimed water yield can reach 10.74×104m3/d, which is greater than the actual needed quantity 8.25×104m3/d of the power plant, so the wastewater reuse of this sewage plant are feasible and reliable to the power plant in view of engineering.

Review on the application of system engineer model in nuclear power plant

International Nuclear Information System (INIS)

Chen Guocai

2005-01-01

system engineer was adopted deeply and play important roles in nuclear power plants in United States and Canada, the plant performance indicates that system engineer mode is a good practice. Qinshan CANDU nuclear power plant, established the system engineer mode since commissioning, as a core, system engineer took charge of the preparation of commissioning procedures, organization, coordination and guidance of commissioning execution. Unit 1 was put into commercial operation 43 days in advance and 112 days ahead of schedule for Unit 2 with excellent quality. Commissioning period are just 10.5 and 7.8 months for both Units respectively. Which is the shortest record in the history of CANDU nuclear power plant commissioning up to now. During operation, systems engineer has strength in routine operating and units reliability improvement. Based on the practice of Qinshan CANDU nuclear power plant commissioning and production technical management, the main form of the article in the era of knowledge: its characteristics and advantage and operating mode of the system engineer mode. System engineer is different from project engineer, he act as the master of systems and takes full responsibility for systems technical management. System engineer should do many jobs and improvement schedule to ensure his system in health status. System health monitor is a basic tool in system management, which is useful for equipment performance improvement. At last, the author made a forecast and comment on the prospects for the system engineer in the future. (author)

Projecting labor demand and worker immigration at nuclear power plant construction sites: an evaluation of methodology

International Nuclear Information System (INIS)

Herzog, H.W. Jr; Schlottmann, A.M.; Schriver, W.R.

1981-12-01

The study evaluates methodology employed for the projection of labor demand at, and worker migration to, nuclear power plant construction sites. In addition, suggestions are offered as to how this projection methodology might be improved. The study focuses on projection methodologies which forecast either construction worker migration or labor requirements of alternative types of construction activity. Suggested methodological improvements relate both to institutional factors within the nuclear power plant construction industry, and to a better use of craft-specific data on construction worker demand/supply. In addition, the timeliness and availability of the regional occupational data required to support, or implement these suggestions are examined

Lifetime and economic analyses of lithium-ion batteries for balancing wind power forecast error

DEFF Research Database (Denmark)

Swierczynski, Maciej Jozef; Stroe, Daniel Ioan; Stroe, Ana-Irina

2015-01-01

is considered. In this paper, the economic feasibility of lithium-ion batteries for balancing the wind power forecast error is analysed. In order to perform a reliable assessment, an ageing model of lithium-ion battery was developed considering both cycling and calendar life. The economic analysis considers two......, it was found that for total elimination of the wind power forecast error, it is required to have a 25-MWh Li-ion battery energy storage system for the considered 2 MW WT....

Nuclear power plants

International Nuclear Information System (INIS)

Margulova, T.Ch.

1976-01-01

The textbook focuses on the technology and the operating characteristics of nuclear power plants equiped with pressurized water or boiling water reactors, which are in operation all over the world at present. The following topics are dealt with in relation to the complete plant and to economics: distribution and consumption of electric and thermal energy, types and equipment of nuclear power plants, chemical processes and material balance, economical characteristics concerning heat and energy, regenerative preheating of feed water, degassing and condenser systems, water supply, evaporators, district heating systems, steam generating systems and turbines, coolant loops and pipes, plant siting, ventilation and decontamination systems, reactor operation and management, heat transfer including its calculation, design of reactor buildings, and nuclear power plants with gas or sodium cooled reactors. Numerous technical data of modern Soviet nuclear power plants are included. The book is of interest to graduate and post-graduate students in the field of nuclear engineering as well as to nuclear engineers

Trading wind generation from short-term probabilistic forecasts of wind power

DEFF Research Database (Denmark)

Pinson, Pierre; Chevallier, Christophe; Kariniotakis, Georges

2007-01-01

Due to the fluctuating nature of the wind resource, a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs. Accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue......, as well as on modeling of the sensitivity a wind power producer may have to regulation costs. The benefits resulting from the application of these strategies are clearly demonstrated on the test case of the participation of a multi-MW wind farm in the Dutch electricity market over a year....... participation. Such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation. This paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation...

Accident prevention in power plants

International Nuclear Information System (INIS)

Steyrer, H.

Large thermal power plants are insured to a great extent at the Industrial Injuries Insurance Institute of Instrument and Electric Engineering. Approximately 4800 employees are registered. The accident frequency according to an evaluation over 12 months lies around 79.8 per year and 1000 employees in fossil-fired power plants, around 34.1 per year and 1000 employees in nuclear power plants, as in nuclear power plants coal handling and ash removal are excluded. Injuries due to radiation were not registered. The crucial points of accidents are mechanical injuries received on solid, sharp-edged and pointed objects (fossil-fired power plants 28.6%, nuclear power plants 41.5%), stumbling, twisting or slipping (fossil-fired power plants 21.8%, nuclear power plants 19.5%) and injuries due to moving machine parts (only nuclear power plants 12.2%). However, accidents due to burns or scalds obtain with 4.2% and less a lower portion than expected. The accident statistics can explain this fact in a way that the typical power plant accident does not exist. (orig./GL) [de

Neural network based photovoltaic electrical forecasting in south Algeria

International Nuclear Information System (INIS)

Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

2014-01-01

Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

Energy Technology Data Exchange (ETDEWEB)

Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M. (Mathematics and Computer Science); (Univ. of Chicago); (New York Univ.)

2009-10-09

We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

Advanced inflow forecasting for a hydropower plant in an Alpine hydropower regulated catchment - coupling of operational and hydrological forecasts

Science.gov (United States)

Tilg, Anna-Maria; Schöber, Johannes; Huttenlau, Matthias; Messner, Jakob; Achleitner, Stefan

2017-04-01

Hydropower is a renewable energy source which can help to stabilize fluctuations in the volatile energy market. Especially pumped-storage infrastructures in the European Alps play an important role within the European energy grid system. Today, the runoff of rivers in the Alps is often influenced by cascades of hydropower infrastructures where the operational procedures are triggered by energy market demands, water deliveries and flood control aspects rather than by hydro-meteorological variables. An example for such a highly hydropower regulated river is the catchment of the river Inn in the Eastern European Alps, originating in the Engadin (Switzerland). A new hydropower plant is going to be built as transboundary project at the boarder of Switzerland and Austria using the water of the Inn River. For the operation, a runoff forecast to the plant is required. The challenge in this case is that a high proportion of runoff is turbine water from an upstream situated hydropower cascade. The newly developed physically based hydrological forecasting system is mainly capable to cover natural hydrological runoff processes caused by storms and snow melt but can model only a small degree of human impact. These discontinuous parts of the runoff downstream of the pumped storage are described by means of an additional statistical model which has been developed. The main goal of the statistical model is to forecast the turbine water up to five days in advance. The lead time of the data driven model exceeds the lead time of the used energy production forecast. Additionally, the amount of turbine water is linked to the need of electricity production and the electricity price. It has been shown that especially the parameters day-ahead prognosis of the energy production and turbine inflow of the previous week are good predictors and are therefore used as input parameters for the model. As the data is restricted due to technical conditions, so-called Tobit models have been used to

Analysis of policy alternatives on the public acceptance of nuclear power plant in Korea

International Nuclear Information System (INIS)

Choi, Young-Sung; Lee, Byong-Whi

1995-01-01

Public acceptance has become an important factor in nuclear power program particularly after Chernobyl accident and recent rapid democratization in Korea. A method reflecting public opinions in order to improve public acceptance is to find out the public preference values for its policy alternatives. In this study, the conjoint analysis was applied to find out the quantitative values of public preferences for twelve policy alternatives to support communities surrounding nuclear power plants in Korea. To implement the analysis, questionnaires of trade-off matrix form were mailed to the science teachers of middle or high school through-out the country who had the experience of visiting nuclear power plant. The quantitative preference values for potential policy alternatives were estimated, which made it possible to forecast the effectiveness of each option. It was revealed that the improvement of reactor safety 100 times and the establishment of civilian monitoring system for nuclear safety would be two best options to improve public acceptance of nuclear power in Korea. (author)

Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error

International Nuclear Information System (INIS)

Xie, Mengfei; Zhou, Jianzhong; Li, Chunlong; Zhu, Shuang

2015-01-01

Highlights: • Monthly streamflow forecasting error is considered. • An improved parallel progressive optimality algorithm is proposed. • Forecasting dispatching chart is manufactured accompanying with a set of rules. • Applications in Xiluodu and Xiangjiaba cascade hydro plants. - Abstract: Reliable streamflow forecasts are very significant for reservoir operation and hydropower generation. But for monthly streamflow forecasting, the forecasting result is unreliable and it is hard to be utilized, although it has a certain reference value for long-term hydro generation scheduling. Current researches mainly focus on deterministic scheduling, and few of them consider the uncertainties. So this paper considers the forecasting error which exists in monthly streamflow forecasting and proposes a new long-term hydro generation scheduling method called forecasting dispatching chart for Xiluodu and Xiangjiaba cascade hydro plants. First, in order to consider the uncertainties of inflow, Monte Carlo simulation is employed to generate streamflow data according to the forecasting value and error distribution curves. Then the large amount of data obtained by Monte Carlo simulation is used as inputs for long-term hydro generation scheduling model. Because of the large amount of streamflow data, the computation speed of conventional algorithm cannot meet the demand. So an improved parallel progressive optimality algorithm is proposed to solve the long-term hydro generation scheduling problem and a series of solutions are obtained. These solutions constitute an interval set, unlike the unique solution in the traditional deterministic long-term hydro generation scheduling. At last, the confidence intervals of the solutions are calculated and forecasting dispatching chart is proposed as a new method for long-term hydro generation scheduling. A set of rules are proposed corresponding to forecasting dispatching chart. The chart is tested for practical operations and achieves

Forecasting of electricity prices with neural networks

Energy Technology Data Exchange (ETDEWEB)

Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

2006-08-15

During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

Forecasting of electricity prices with neural networks

International Nuclear Information System (INIS)

Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

2006-01-01

During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

OpenAIRE

Saber Talari; Miadreza Shafie-khah; Gerardo J. Osório; Fei Wang; Alireza Heidari; João P. S. Catalão

2017-01-01

Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind...

Fossil fuel power plant combustion control: Research in Italy

International Nuclear Information System (INIS)

Pasini, S.; Trebbi, G.

1991-01-01

Electric power demand forecasts for Italy to the year 2000 indicate an increase of about 50% which, due to the current moratorium on nuclear energy, should be met entirely by fossil fuel power plants. Now, there is growing public concern about possible negative health impacts due to the air pollution produced through the combustion of fossil fuels. In response to these concerns, ENEL (Italian National Electricity Board) is investing heavily in air pollution abatement technology R ampersand D. The first phase involves the investigation of pollution mechanisms in order to develop suitable mathematical models and diagnostic techniques. The validity of the models is being tested through through measurements made by sophisticated instrumentation placed directly inside the combustion chambers of steam generator systems. These are allowing engineers to develop improved combustion control methods designed to reduce air pollution at source

Addressing forecast uncertainty impact on CSP annual performance

Science.gov (United States)

Ferretti, Fabio; Hogendijk, Christopher; Aga, Vipluv; Ehrsam, Andreas

2017-06-01

This work analyzes the impact of weather forecast uncertainty on the annual performance of a Concentrated Solar Power (CSP) plant. Forecast time series has been produced by a commercial forecast provider using the technique of hindcasting for the full year 2011 in hourly resolution for Ouarzazate, Morocco. Impact of forecast uncertainty has been measured on three case studies, representing typical tariff schemes observed in recent CSP projects plus a spot market price scenario. The analysis has been carried out using an annual performance model and a standard dispatch optimization algorithm based on dynamic programming. The dispatch optimizer has been demonstrated to be a key requisite to maximize the annual revenues depending on the price scenario, harvesting the maximum potential out of the CSP plant. Forecasting uncertainty affects the revenue enhancement outcome of a dispatch optimizer depending on the error level and the price function. Results show that forecasting accuracy of direct solar irradiance (DNI) is important to make best use of an optimized dispatch but also that a higher number of calculation updates can partially compensate this uncertainty. Improvement in revenues can be significant depending on the price profile and the optimal operation strategy. Pathways to achieve better performance are presented by having more updates both by repeatedly generating new optimized trajectories but also more often updating weather forecasts. This study shows the importance of working on DNI weather forecasting for revenue enhancement as well as selecting weather services that can provide multiple updates a day and probabilistic forecast information.

Optimal operation of a pumped-storage hydro plant that compensates the imbalances of a wind power producer

OpenAIRE

Duque, Álvaro Jaramillo; Castronuovo, Edgardo D.; Sánchez, Ismael; Usaola, Julio

2011-01-01

The participation of wind energy in electricity markets requires providing a forecast for future energy production of a wind generator, whose value will be its scheduled energy. Deviations from this schedule because of prediction errors could imply the payment of imbalance costs. In order to decrease these costs, a joint operation between a wind farm and a hydro-pump plant is proposed; the hydro-pump plant changes its production to compensate wind power prediction errors. In order to optimize...

Power program and nuclear power

International Nuclear Information System (INIS)

Chernilin, Yu.F.

1990-01-01

Main points of the USSR power program and the role of nuclear power in fuel and power complex of the country are considered. Data on dynamics of economic indices of electric power generation at nuclear power plants during 1980-1988 and forecasts till 2000 are presented. It is shown that real cost of 1 kW/h of electric power is equal to 1.3-1.8 cop., and total reduced cost is equal to 1.8-2.4 cop

Off-shore nuclear power plant

International Nuclear Information System (INIS)

Nakanishi, T.

1980-01-01

In order to avoid losses of energy and seawater pollution an off-shore nuclear power plant is coupled with a power plant which utilizes the temperature difference between seawater and hot reactor cooling water. According to the invention the power plant has a working media loop which is separated from the nuclear power plant. The apparative equipment and the operational characteristics of the power plant are the subject of the patent. (UWI) [de

Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

Directory of Open Access Journals (Sweden)

Constantin Junk

2015-04-01

Full Text Available Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn, which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS minimization and a principal component analysis (PCA of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF, including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10‑m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

Solar thermal power plants

International Nuclear Information System (INIS)

Schnatbaum, L.

2009-01-01

The solar thermal power plant technology, the opportunities it presents and the developments in the market are outlined. The focus is on the technology of parabolic trough power plants, a proven technology for solar power generation on a large scale. In a parabolic trough power plant, trough-shaped mirrors concentrate the solar irradiation onto a pipe in the focal line of the collector. The thermal energy thus generated is used for electricity generation in a steam turbine. Parabolic trough plants can be combined with thermal storage and fossil or biomass fired heat exchangers to generate electricity even when the sun is not shining. Solar Millennium AG in Erlangen has developed the first power plant of this kind in Europe. After two years of construction the plant started operation in Southern Spain in 2008. This one and its sister projects are important steps leading the way for the whole market. The paper also covers the technological challenges, the key components used and the research and development activities concerning this technology. Solar thermal power plants are ideal for covering peak and medium loads in power grids. In hybrid operation they can also cover base-load. The Solar Chimney power plant, another striking technology for the conversion of solar into electric energy, is described briefly. The paper concludes with a look at the future - the import of solar energy from the deserts of North Africa to central Europe. (author)

Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

DEFF Research Database (Denmark)

Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

2013-01-01

A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

Power plant cycle chemistry - a currently neglected power plant chemistry discipline

International Nuclear Information System (INIS)

Bursik, A.

2005-01-01

Power plant cycle chemistry seems to be a stepchild at both utilities and universities and research organizations. It is felt that other power plant chemistry disciplines are more important. The last International Power Cycle Chemistry Conference in Prague may be cited as an example. A critical review of the papers presented at this conference seems to confirm the above-mentioned statements. This situation is very unsatisfactory and has led to an increasing number of component failures and instances of damage to major cycle components. Optimization of cycle chemistry in fossil power plants undoubtedly results in clear benefits and savings with respect to operating costs. It should be kept in mind that many seemingly important chemistry-related issues lose their importance during forced outages of units practicing faulty plant cycle chemistry. (orig.)

Alternative off-site power supply improves nuclear power plant safety

International Nuclear Information System (INIS)

Gjorgiev, Blaže; Volkanovski, Andrija; Kančev, Duško; Čepin, Marko

2014-01-01

Highlights: • Additional power supply for mitigation of the station blackout event in NPP is used. • A hydro power plant is considered as an off-site alternative power supply. • An upgrade of the probabilistic safety assessment from its traditional use is made. • The obtained results show improvement of nuclear power plant safety. - Abstract: A reliable power system is important for safe operation of the nuclear power plants. The station blackout event is of great importance for nuclear power plant safety. This event is caused by the loss of all alternating current power supply to the safety and non-safety buses of the nuclear power plant. In this study an independent electrical connection between a pumped-storage hydro power plant and a nuclear power plant is assumed as a standpoint for safety and reliability analysis. The pumped-storage hydro power plant is considered as an alternative power supply. The connection with conventional accumulation type of hydro power plant is analysed in addition. The objective of this paper is to investigate the improvement of nuclear power plant safety resulting from the consideration of the alternative power supplies. The safety of the nuclear power plant is analysed through the core damage frequency, a risk measure assess by the probabilistic safety assessment. The presented method upgrades the probabilistic safety assessment from its common traditional use in sense that it considers non-plant sited systems. The obtained results show significant decrease of the core damage frequency, indicating improvement of nuclear safety if hydro power plant is introduced as an alternative off-site power source

Control of power plants and power systems. Proceedings

International Nuclear Information System (INIS)

Canales-Ruiz, R.

1996-01-01

The 88 papers in this volume constitute the proceedings of the International Federation of Automatic Control Symposium held in Mexico in 1995. The broad areas which they cover are: self tuning control; power plant operations; dynamic stability; fuzzy logic applications; power plants modelling; artificial intelligence applications; power plants simulation; voltage control; control of hydro electric units; state estimation; fault diagnosis and monitoring systems; system expansion and operation planning; security assessment; economic dispatch and optimal load flow; adaptive control; distribution; transient stability and preventive control; modelling and control of nuclear plant; knowledge data bases for automatic learning methods applied to power system dynamic security assessment; control of combined cycle units; power control centres. Separate abstracts have been prepared for the three papers relating to nuclear power plants. (UK)

Wet snow hazard for power lines: a forecast and alert system applied in Italy

Directory of Open Access Journals (Sweden)

P. Bonelli

2011-09-01

Full Text Available Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly.

The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast, developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

Wet snow hazard for power lines: a forecast and alert system applied in Italy

Science.gov (United States)

Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.

2011-09-01

Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

Electric peak power forecasting by year 2025

International Nuclear Information System (INIS)

Alsayegh, O.A.; Al-Matar, O.A.; Fairouz, F.A.; Al-Mulla Ali, A.

2005-01-01

Peak power demand in Kuwait up to the year 2025 was predicted using an artificial neural network (ANN) model. The aim of the study was to investigate the effect of air conditioning (A/C) units on long-term power demand. Five socio-economic factors were selected as inputs for the simulation: (1) gross national product, (2) population, (3) number of buildings, (4) imports of A/C units, and (5) index of industrial production. The study used socio-economic data from 1978 to 2000. Historical data of the first 10 years of the studied time period were used to train the ANN. The electrical network was then simulated to forecast peak power for the following 11 years. The calculated error was then used for years in which power consumption data were not available. The study demonstrated that average peak power rates increased by 4100 MW every 5 years. Various scenarios related to changes in population, the number of buildings, and the quantity of A/C units were then modelled to estimate long-term peak power demand. Results of the study demonstrated that population had the strongest impact on future power demand, while the number of buildings had the smallest impact. It was concluded that peak power growth can be controlled through the use of different immigration policies, increased A/C efficiency, and the use of vertical housing. 7 refs., 2 tabs., 6 figs

Electric peak power forecasting by year 2025

Energy Technology Data Exchange (ETDEWEB)

Alsayegh, O.A.; Al-Matar, O.A.; Fairouz, F.A.; Al-Mulla Ali, A. [Kuwait Inst. for Scientific Research, Kuwait City (Kuwait). Div. of Environment and Urban Development

2005-07-01

Peak power demand in Kuwait up to the year 2025 was predicted using an artificial neural network (ANN) model. The aim of the study was to investigate the effect of air conditioning (A/C) units on long-term power demand. Five socio-economic factors were selected as inputs for the simulation: (1) gross national product, (2) population, (3) number of buildings, (4) imports of A/C units, and (5) index of industrial production. The study used socio-economic data from 1978 to 2000. Historical data of the first 10 years of the studied time period were used to train the ANN. The electrical network was then simulated to forecast peak power for the following 11 years. The calculated error was then used for years in which power consumption data were not available. The study demonstrated that average peak power rates increased by 4100 MW every 5 years. Various scenarios related to changes in population, the number of buildings, and the quantity of A/C units were then modelled to estimate long-term peak power demand. Results of the study demonstrated that population had the strongest impact on future power demand, while the number of buildings had the smallest impact. It was concluded that peak power growth can be controlled through the use of different immigration policies, increased A/C efficiency, and the use of vertical housing. 7 refs., 2 tabs., 6 figs.

Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

DEFF Research Database (Denmark)

Bremnes, John Bjørnar; Giebel, Gregor

2017-01-01

resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

Analysis on perception of nuclear power plant and the preference of its policy alternatives for public acceptance

International Nuclear Information System (INIS)

Choi, Young Sung; Lee, Byong Whi

1995-01-01

Public acceptance has become an important factor in nuclear power program particularly after Chernobyl accident and recent rapid democratization in Korea. Methods reflection public opinions in order to improve public acceptance are firstly to understand what the public think about nuclear power plant and secondly to find out the public preference values for its policies. For this purpose, simplified multi-attribute utility(MAU) model was applied to analyze the public perception for five power production system. And the conjoint analysis was applied to find out he quantitative values of public preferences for twelve policy alternatives to improve the safety and support communities surrounding nuclear power plants in Korea. To implement these perception and preference analyses, mail survey was conducted to the qualified sample who had the experience of visiting nuclear power plant. Diagnosis of their perception pattern for five power production systems was made by the simplified MAU model. Estimation of the quantitative preference values for potential policy alternatives was made by the conjoint measurement technique, which made it possible to forecast the effectiveness of each option. The results from the qualified sample and the methods used in this study would be helpful to set up new policy of nuclear power plant. 4 figs., 7 tabs., 18 refs. (Author)

Model for optimization of plant investments in combined power and heat production systems

Energy Technology Data Exchange (ETDEWEB)

Jantunen, E.; Sinisalo, A.; Koskelainen, L.

1980-01-01

A mathematical model is developed for optimal dimensioning and timing the investments of power and heat production system in a community. The required electric power may be purchased by different production systems, such as: thermal power plants, gas turbines, diesel plants, etc. or by delivering all or part of it from a national power company. Also the required heat may be produced in many different ways in single-purpose or combined plants. The model assumes the extent of the heating system fixed, and it is not optimized. It is assumed that the same company is responsible for supplying both the power and heat for the community. It's aim is to allocate the existing capital in an optimal way, and the model may be used for facilitating the decision in such questions as: what kind of production capacity should be purchased in future; how high should the heat and power capacities be; and when should this additional capacity be available. The report also reviews the methods for forecasting the demand of power and heat and their fluctuation during the planning period. The solution of this large-scale non-linear optimization problem is searched via successive linearizations by using the Method of Approximate Programming (MAP). It was found that the solution method is very suitable for this kind of multivariable problems. The computing times with the Functional Mathematical Programmin System (FMPS) in Univac 1108 computer were quite reasonable.

Industrial safety in power plants

International Nuclear Information System (INIS)

1987-01-01

The proceedings of the VGB conference 'Industrial safety in power plants' held in the Gruga-Halle, Essen on January 21 and 22, 1987, contain the papers reporting on: Management responsibility for and legal consequences of industrial safety; VBG 2.0 Industrial Accident Prevention Regulation and the power plant operator; Operational experience gained with wet-type flue gas desulphurization systems; Flue gas desulphurization systems: Industrial-safety-related requirements to be met in planning and operation; the effects of the Hazardous Substances Ordinance on power plant operation; Occupational health aspects of heat-exposed jobs in power plants; Regulations of the Industrial Accident Insurance Associations concerning heat-exposed jobs and industrial medical practice; The new VBG 30 Accident Prevention Regulation 'Nuclear power plants'; Industrial safety in nuclear power plants; safe working on and within containers and confined spaces; Application of respiratory protection equipment in power plants. (HAG) [de

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data.

Science.gov (United States)

Nou, Julien; Chauvin, Rémi; Eynard, Julien; Thil, Stéphane; Grieu, Stéphane

2018-04-01

Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon [Formula: see text] is up to [Formula: see text] ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models - either a persistence of DNI or a persistence of the clear-sky index - are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about [Formula: see text], with [Formula: see text], and [Formula: see

NUCLEAR POWER PLANT

Science.gov (United States)

Carter, J.C.; Armstrong, R.H.; Janicke, M.J.

1963-05-14

A nuclear power plant for use in an airless environment or other environment in which cooling is difficult is described. The power plant includes a boiling mercury reactor, a mercury--vapor turbine in direct cycle therewith, and a radiator for condensing mercury vapor. (AEC)

Influence of fossil-fuel power plant emissions on the surface fine particulate matter in the Seoul Capital Area, South Korea.

Science.gov (United States)

Kim, Byeong-Uk; Kim, Okgil; Kim, Hyun Cheol; Kim, Soontae

2016-09-01

The South Korean government plans to reduce region-wide annual PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) concentrations in the Seoul Capital Area (SCA) from 2010 levels of 27 µg/m(3) to 20 µg/m(3) by 2024. At the same time, it is inevitable that emissions from fossil-fuel power plants will continue to increase if electricity generation expands and the generation portfolio remains the same in the future. To estimate incremental PM2.5 contributions due to projected electricity generation growth in South Korea, we utilized an ensemble forecasting member of the Integrated Multidimensional Air Quality System for Korea based on the Community Multi-scale Air Quality model. We performed sensitivity runs with across-the-board emission reductions for all fossil-fuel power plants in South Korea to estimate the contribution of PM2.5 from domestic fossil-fuel power plants. We estimated that fossil-fuel power plants are responsible for 2.4% of the annual PM2.5 national ambient air quality standard in the SCA as of 2010. Based on the electricity generation and the annual contribution of fossil-fuel power plants in 2010, we estimated that annual PM2.5 concentrations may increase by 0.2 µg/m(3) per 100 TWhr due to additional electricity generation. With currently available information on future electricity demands, we estimated that the total future contribution of fossil-fuel power plants would be 0.87 µg/m(3), which is 12.4% of the target reduction amount of the annual PM2.5 concentration by 2024. We also approximated that the number of premature deaths caused by existing fossil-fuel power plants would be 736 in 2024. Since the proximity of power plants to the SCA and the types of fuel used significantly impact this estimation, further studies are warranted on the impact of physical parameters of plants, such as location and stack height, on PM2.5 concentrations in the SCA due to each precursor. Improving air quality by reducing fine particle

Project 'WINDBANK mittleres Aaretal' - Analysis, Diagnosis and Forecast of Wind Fields around the Nuclear Power Plant Goesgen; Projekt 'WINDBANK mittleres Aaretal' - Analyse, Diagnose und Prognose der Windverhaeltnisse um das Kernkraftwerk Goesgen

Energy Technology Data Exchange (ETDEWEB)

Graber, W.K.; Tinguely, M

2002-07-01

An emergency decision support system for accidental releases of radioactivity into the atmosphere providing regional wind field information is presented. This system is based on intensive meteorological field campaigns each lasting 3-4 months in the regions around the Swiss nuclear power plants. The wind data from temporary and permanent stations are analysed to evaluate the typical wind field patterns occurring in these regions. A cluster analysis for these data-sets lead to 12 different wind field classes with a high separation quality. In the present report, it is demonstrated that an on-line acquisition of meteorological data from existing permanent stations is enough to diagnose the recent wind field class in a region with a radius of 25 km around the nuclear power station of Goesgen with a probability of 95% to hit the correct class. Furthermore, a method is presented to use a high resolution weather prediction model to forecast the future wind field classes. An average probability of 76% to hit the correct class for a forecast time of 24 hours is evaluated. Finally, a method for parameterization of turbulence providing input for dispersion models from standard meteorological online data is presented. (author)

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

Directory of Open Access Journals (Sweden)

Ping Jiang

2015-01-01

Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

UD-WCMA: An Energy Estimation and Forecast Scheme for Solar Powered Wireless Sensor Networks

KAUST Repository

Dehwah, Ahmad H.

2017-04-11

Energy estimation and forecast represents an important role for energy management in solar-powered wireless sensor networks (WSNs). In general, the energy in such networks is managed over a finite time horizon in the future based on input solar power forecasts to enable continuous operation of the WSNs and achieve the sensing objectives while ensuring that no node runs out of energy. In this article, we propose a dynamic version of the weather conditioned moving average technique (UD-WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location to update the prediction model. The UD-WCMA scheme is based on adaptive weighting parameters depending on the weather changes which makes it flexible compared to the existing estimation schemes without any precalibration. A performance analysis has been performed considering real irradiance profiles to assess the UD-WCMA prediction accuracy. Comparative numerical tests to standard forecasting schemes (EWMA, WCMA, and Pro-Energy) shows the outperformance of the new algorithm. The experimental validation has proven the interesting features of the UD-WCMA in real time low power sensor nodes.

Early tube leak detection system for steam boiler at KEV power plant

Directory of Open Access Journals (Sweden)

Ismail Firas B.

2016-01-01

Full Text Available Tube leakage in boilers has been a major contribution to trips which eventually leads to power plant shut downs. Training of network and developing artificial neural network (ANN models are essential in fault detection in critically large systems. This research focusses on the ANN modelling through training and validation of real data acquired from a sub-critical boiler unit. The artificial neural network (ANN was used to develop a compatible model and to evaluate the working properties and behaviour of boiler. The training and validation of real data has been applied using the feed-forward with back-propagation (BP. The right combination of number of neurons, number of hidden layers, training algorithms and training functions was run to achieve the best ANN model with lowest error. The ANN was trained and validated using real site data acquired from a coal fired power plant in Malaysia. The results showed that the Neural Network (NN with one hidden layers performed better than two hidden layer using feed-forward back-propagation network. The outcome from this study give us the best ANN model which eventually allows for early detection of boiler tube leakages, and forecast of a trip before the real shutdown. This will eventually reduce shutdowns in power plants.

Nuclear power. Volume 1. Nuclear power plant design

International Nuclear Information System (INIS)

Pedersen, E.S.

1978-01-01

NUCLEAR POWER PLANT DESIGN is intended to be used as a working reference book for management, engineers and designers, and as a graduate-level text for engineering students. The book is designed to combine theory with practical nuclear power engineering and design experience, and to give the reader an up-to-date view of the status of nuclear power and a basic understanding of how nuclear power plants function. Volume 1 contains the following chapters; (1) nuclear reactor theory; (2) nuclear reactor design; (3) types of nuclear power plants; (4) licensing requirements; (5) shielding and personnel exposure; (6) containment and structural design; (7) main steam and turbine cycles; (8) plant electrical system; (9) plant instrumentation and control systems; (10) radioactive waste disposal (waste management) and (11) conclusion

Ideal Operation of a Photovoltaic Power Plant Equipped with an Energy Storage System on Electricity Market

Directory of Open Access Journals (Sweden)

Markku Järvelä

2017-07-01

Full Text Available There is no natural inertia in a photovoltaic (PV generator and changes in irradiation can be seen immediately at the output power. Moving cloud shadows are the dominant reason for fast PV power fluctuations taking place typically within a minute between 20 to 100% of the clear sky value roughly 100 times a day, on average. Therefore, operating a utility scale grid connected PV power plant is challenging. Currently, in many regions, renewable energy sources such as solar and wind receive feed-in tariffs that ensure a certain price for the energy. On the other hand, electricity markets operate on a supply-demand principle and a typical imbalance settlement period is one hour. This paper presents the energy, power and corresponding requirements for an energy storage system in a solar PV power plant to feed the power to the grid meeting the electricity spot markets practices. An ideal PV energy production forecast is assumed to be available to define reference powers of the system for the studied imbalance settlement periods. The analysis is done for three different PV system sizes using the existing irradiance measurements of the Tampere University of Technology solar PV power station research plant.

Power plants 2010. Lectures

International Nuclear Information System (INIS)

2010-01-01

The proceedings include the following lectures: Facing the challenges - new structures for electricity production. Renewable energies in Europe - chances and challenges. Nuclear outlook in the UK. Sustainable energy for Europe. Requirements of the market and the grid operator at the electricity production companies. Perspectives for the future energy production. Pumped storage plants - status and perspectives. Nuclear power/renewable energies -partners or opponents? New fossil fired power stations in Europe - status and perspectives. Nuclear energy: outlook for new build and lifetime extension in Europe. Biomass in the future European energy market - experiences for dong energy. Meeting the EU 20:20 renewable energy targets: the offshore challenges. DESERTEC: sustainable electricity for Europe, Middle East and North Africa. New power plants in Europe - a challenge for project and quality management. Consideration of safely in new build activities of power plants. Challenges to an integrated development in Maasvlakte, Netherlands. Power enhancement in EnBW power plants. Operational experiences of CCS pilot plants worldwide. Two years of operational experiences with Vattenfall's oxyfuel pilot plant. Pre-conditions for CCS. Storage technologies for a volatile generation. Overview: new generation of gas turbines.

The year 2000 power plant

International Nuclear Information System (INIS)

Roman, H.T.

1989-01-01

Every utility seeks extended service life from its existing power plants before building new ones. It is not easy to justify a new power plant. The licensing and cost of new plants have become uncertain. In response to these conditions, electric utilities are undertaking plant life-extension studies and, in some cases, reconditioning/upgrading old power plants to significantly increase useful service life. Other technologies like robotics and artificial intelligence/expert systems are also being developed to reduce operating and maintenance (O and M) expenses, to remove workers from potentially hazardous environments, and to reduce plant downtime. Together, these steps represent an interim solution, perhaps providing some relief for the next few decades. However, there are serious physical and economic limits to retrofitting new technology into existing power plants. Some old plants will simply be beyond their useful life and require retirement. In nuclear plants, for instance, retrofit may raise important and time-consuming licensing/safety issues. Based on their robotics and artificial intelligence experience, the authors of this article speculate bout the design of the year 2000 power plant - a power plant they feel will naturally incorporate liberal amounts of robotic and artificial intelligence technologies

Modeling and forecasting of wind power generation - Regime-switching approaches

DEFF Research Database (Denmark)

Trombe, Pierre-Julien

The present thesis addresses a number of challenges emerging from the increasing penetration of renewable energy sources into power systems. Focus is placed on wind energy and large-scale offshore wind farms. Indeed, offshore wind power variability is becoming a serious obstacle to the integration...... of more renewable energy into power systems since these systems are subjected to maintain a strict balance between electricity consumption and production, at any time. For this purpose, wind power forecasts offer an essential support to power system operators. In particular, there is a growing demand...... case study is the Horns Rev wind farm located in the North Sea. Regime-switching aspects of offshore wind power fluctuations are investigated. Several formulations of Markov-Switching models are proposed in order to better characterize the stochastic behavior of the underlying process and improve its...

Estimation of environmental external costs between coal fired power plant and nuclear power plant

International Nuclear Information System (INIS)

Moon, G. H.; Kim, S. S.

2000-01-01

First of all, this study evaluated the impacts on the health and the environment of air pollutants emitted from coal power plant and nuclear power pant, two major electric power generating options in Korea. Then, the environmental external costs of those two options were estimated by transforming the health and environment impact into monetary values. To do this, AIRPACTS and Impacts of Atmospheric Release model developed by IAEA were used. The environmental external cost of Samcheonpo coal power plant was estimated about 25 times as much as that of Younggwang nuclear power plant. This result implies that nuclear power plant is a clean technology compared with coal power plant. This study suggests that the external cost should be reflected in the electric system expansion plan in order to allocate energy resources efficiently and to reduce economic impact stemming from the environmental regulation emerged recently on a global level

Organization patterns of PWR power plants

International Nuclear Information System (INIS)

Leicman, J.

1980-01-01

Organization patterns are shown for the St. Lucia 1, North Anna, Sequoyah, and Beaver Valley nuclear power plants, for a typical PWR power plant in the USA, for the Biblis/RWE-KWU nuclear power plants and for a four-unit nuclear power plant operated by Electricite de France as well as for the Loviisa power plant. Organization patterns are also shown for relatively independent and non-independent nuclear power plants according to IAEA recommendations. (J.P.)

Nuclear power plant siting

International Nuclear Information System (INIS)

Sulkiewicz, M.; Navratil, J.

The construction of a nuclear power plant is conditioned on territorial requirements and is accompanied by the disturbance of the environment, land occupation, population migration, the emission of radioactive wastes, thermal pollution, etc. On the other hand, a nuclear power plant makes possible the introduction of district heating and increases the economic and civilization activity of the population. Due to the construction of a nuclear power plant the set limits of negative impacts must not be exceeded. The locality should be selected such as to reduce the unfavourable effects of the plant and to fully use its benefits. The decision on the siting of the nuclear power plant is preceded by the processing of a number of surveys and a wide range of documentation to which the given criteria are strictly applied. (B.H.)

Medium- and long-term electric power demand forecasting based on the big data of smart city

Science.gov (United States)

Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

2017-08-01

Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

Evaluation of different operating strategies to integrate storage in a linear Fresnel ORC power plant

Science.gov (United States)

Zoschke, Theda; Seubert, Bernhard; Fluri, Thomas

2017-06-01

An existing linear Fresnel power plant with ORC process located in Ben Guerir, Morocco, is retrofitted with a thermal energy storage system and additional collector loops. Two different plant configurations are investigated in this paper. In the first configuration two separate solar fields are built and only the minor one can charge the storage. In the second configuration, there is only one large solar field which offers more flexibility. Two different control strategies are assessed by comparing simulation results. It shows that the simulations of the systems with two solar fields results in higher energy yields throughout the year, but the power production of the system with one solar field is much more flexible and demand oriented. Also it offers great potential for improvement when it comes to weather forecasting.

Wind power plant system services

DEFF Research Database (Denmark)

Basit, Abdul; Altin, Müfit

Traditionally, conventional power plants have the task to support the power system, by supplying power balancing services. These services are required by the power system operators in order to secure a safe and reliable operation of the power system. However, as in the future the wind power...... is going more and more to replace conventional power plants, the sources of conventional reserve available to the system will be reduced and fewer conventional plants will be available on-line to share the regulation burden. The reliable operation of highly wind power integrated power system might...... then beat risk unless the wind power plants (WPPs) are able to support and participate in power balancing services. The objective of this PhD project is to develop and analyse control strategies which can increase the WPPs capability to provide system services, such as active power balancing control...

Forecasting climate change impacts on plant populations over large spatial extents

Science.gov (United States)

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; Homer, Collin G.; Kleinhesselink, Andrew R.; Adler, Peter B.

2016-01-01

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Images of nuclear power plants

International Nuclear Information System (INIS)

Hashiguchi, Katsuhisa; Misumi, Jyuji; Yamada, Akira; Sakurai, Yukihiro; Seki, Fumiyasu; Shinohara, Hirofumi; Misumi, Emiko; Kinjou, Akira; Kubo, Tomonori.

1995-01-01

This study was conducted to check and see, using Hayashi's quantification method III, whether or not the respondents differed in their images of a nuclear power plant, depending on their demographic variables particularly occupations. In our simple tabulation, we compared subject groups of nuclear power plant employees with general citizens, nurses and students in terms of their images of a nuclear power plant. The results were that while the nuclear power plant employees were high in their evaluations of facts about a nuclear power plant and in their positive images of a nuclear power plant, general citizens, nurses and students were overwhelmingly high in their negative images of a nuclear power plant. In our analysis on category score by means of the quantification method III, the first correlation axis was the dimension of 'safety'-'danger' and the second correlation axis was the dimension of 'subjectivity'-'objectivity', and that the first quadrant was the area of 'safety-subjectivity', the second quadrant was the area of 'danger-subjectivity', the third quadrant as the area of 'danger-objectivity', and the forth quadrant was the area of 'safety-objectivity'. In our analysis of sample score, 16 occupation groups was compared. As a result, it was found that the 16 occupation groups' images of a nuclear power plant were, in the order of favorableness, (1) section chiefs in charge, maintenance subsection chiefs, maintenance foremen, (2) field leaders from subcontractors, (3) maintenance section members, operation section members, (4) employees of those subcontractors, (5) general citizens, nurses and students. On the 'safety-danger' dimension, nuclear power plant workers on the one hand and general citizens, nurses and students on the other were clearly divided in terms of their images of a nuclear power plant. Nuclear power plant workers were concentrated in the area of 'safety' and general citizens, nurses and students in the area of 'danger'. (J.P.N.)

On nuclear power plant uprating

International Nuclear Information System (INIS)

Ho, S. Allen; Bailey, James V.; Maginnis, Stephen T.

2004-01-01

Power uprating for commercial nuclear power plants has become increasingly attractive because of pragmatic reasons. It provides quick return on investment and competitive financial benefits, while involving low risks regarding plant safety and public objection. This paper briefly discussed nuclear plant uprating guidelines, scope for design basis analysis and engineering evaluation, and presented the Salem nuclear power plant uprating study for illustration purposes. A cost and benefit evaluation of the Salem power uprating was also included. (author)

Plant calendar pattern based on rainfall forecast and the probability of its success in Deli Serdang regency of Indonesia

Science.gov (United States)

Darnius, O.; Sitorus, S.

2018-03-01

The objective of this study was to determine the pattern of plant calendar of three types of crops; namely, palawija, rice, andbanana, based on rainfall in Deli Serdang Regency. In the first stage, we forecasted rainfall by using time series analysis, and obtained appropriate model of ARIMA (1,0,0) (1,1,1)12. Based on the forecast result, we designed a plant calendar pattern for the three types of plant. Furthermore, the probability of success in the plant types following the plant calendar pattern was calculated by using the Markov process by discretizing the continuous rainfall data into three categories; namely, Below Normal (BN), Normal (N), and Above Normal (AN) to form the probability transition matrix. Finally, the combination of rainfall forecasting models and the Markov process were used to determine the pattern of cropping calendars and the probability of success in the three crops. This research used rainfall data of Deli Serdang Regency taken from the office of BMKG (Meteorologist Climatology and Geophysics Agency), Sampali Medan, Indonesia.

Wind power forecast error smoothing within a wind farm

International Nuclear Information System (INIS)

Saleck, Nadja; Bremen, Lueder von

2007-01-01

Smoothing of wind power forecast errors is well-known for large areas. Comparable effects within a wind farm are investigated in this paper. A Neural Network was taken to predict the power output of a wind farm in north-western Germany comprising 17 turbines. A comparison was done between an algorithm that fits mean wind and mean power data of the wind farm and a second algorithm that fits wind and power data individually for each turbine. The evaluation of root mean square errors (RMSE) shows that relative small smoothing effects occur. However, it can be shown for this wind farm that individual calculations have the advantage that only a few turbines are needed to give better results than the use of mean data. Furthermore different results occurred if predicted wind speeds are directly fitted to observed wind power or if predicted wind speeds are first fitted to observed wind speeds and then applied to a power curve. The first approach gives slightly better RMSE values, the bias improves considerably

Small hydroelectric power plants

International Nuclear Information System (INIS)

Helgesen, Boerre

2002-01-01

Small hydroelectric power plants are power plants of 1 - 10 MW. For a supplier, this is an unnatural limit. A more natural limit involves compact engine design and simplified control system. The article discusses most of the engine and electrotechnical aspects in the development, construction and operation of such a plant

The Kuroshio power plant

CERN Document Server

Chen, Falin

2013-01-01

By outlining a new design or the Kuroshio power plant, new approaches to turbine design, anchorage system planning, deep sea marine engineering and power plant operations and maintenance are explored and suggested. The impact on the local environment, particularly in the face of natural disasters, is also considered to provide a well rounded introduction to plan and build a 30MW pilot power plant. Following a literature review, the six chapters of this book propose a conceptual design by focusing on the plant's core technologies and establish the separate analysis logics for turbine design and

Latin America wind market assessment. Forecast 2013-2022

Energy Technology Data Exchange (ETDEWEB)

NONE

2013-10-15

Wind Power Activities by Country: Developers/Owners, Wind Plant Sizes, Wind Turbines Deployed, Commissioning Dates, Market Share, and Capacity Forecasts Latin American markets are a subject of intense interest from the global wind industry. Wind plant construction across Latin America is modest compared to the more established markets like the United States, Europe, and China, but it is an emerging market that is taking off at a rapid pace. The region has become the hottest alternative growth market for the wind energy industry at a time when growth rates in other markets are flat due to a variety of policy and macroeconomic challenges. Globalization is driving sustainable economic growth in most Latin American countries, resulting in greater energy demand. Wind is increasingly viewed as a valuable and essential answer to increasing electricity generation across most markets in Latin America. Strong wind resources, coupled with today's sophisticated wind turbines, are providing cost-effective generation that is competitive with fossil fuel generation. Most Latin American countries also rely heavily on hydroelectricity, which balances well with variable wind generation. Navigant Research forecasts that if most wind plants under construction with planned commissioning go online as scheduled, annual wind power installations in Latin America will grow from nearly 2.2 GW in 2013 to 4.3 GW by 2022. This Navigant Research report provides a comprehensive view of the wind energy market dynamics at play in Latin America. It offers a country-by-country analysis, outlining the key energy policies and development opportunities and barriers and identifying which companies own operational wind plants and which wind turbine vendors supplied those projects. Market forecasts for wind power installations, capacity, and market share in Latin America, segmented by country and company, extend through 2022. The report also offers an especially close analysis of Brazil and Mexico

Inflow forecasting at BPA

Energy Technology Data Exchange (ETDEWEB)

McManamon, A. [Bonneville Power Administration, Portland, OR (United States)

2007-07-01

The Columbia River Power System operates with consideration for flood control, endangered species, navigation, irrigation, water supply, recreation, other fish and wildlife concerns and power production. The Bonneville Power Association (BPA) located in Portland, Oregon is responsible for 35-40 per cent of the power consumed within the region. This presentation discussed inflow power concerns at BPA. The presentation illustrated elevational relief of projects; annual and daily variability; the hydrologic cycle; national river service weather forecasting service (NRSWFS); components of NRSWFS; and hydrologic forecast locations. Project operations and inventory were included along with a comparison of the 71-year average unregulated flow with regulated flow at the Dalles. Consistency between short-term and long-term forecasts and long-term streamflow forecasts were also illustrated in graphical format. The presentation also discussed the issue of reducing model and parameter uncertainty; reducing initial conditions uncertainty; snow updating; and reducing meteorological uncertainty. tabs., figs.