A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets

Tewari, Saurabh; Geyer, Charles J.; Mohan, Ned

doi:10.1109/tpwrs.2011.2141159

Cited by 163 publications

(88 citation statements)

References 22 publications

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“…In the literature there is a conflict of selecting a distribution function that the historical error data fits [22,[30][31][32][33]. Our method uses the ECDF approach to model the historical data distribution.…”

Section: Resultsmentioning

confidence: 99%

“…Much of the literature makes the simplifying assumption that forecast errors follow a normal distribution [30][31][32], while actual studies [33,34] show that forecast errors do not follow a normal distribution. Tewari et al [32] concluded that the forecasting approach and site effects will change the shape of the distribution. Since modelling the error distribution is easier and more effective than modelling the power distribution, we proposed to model the forecast errors to generate scenarios around point forecasts.…”

Section: Modeling Forecast Errorsmentioning

confidence: 99%

“…On the other hand, forecast error distribution is not as complicated as power distribution. Much of the literature makes the simplifying assumption that forecast errors follow a normal distribution [30][31][32], while actual studies [33,34] show that forecast errors do not follow a normal distribution. Tewari et al [32] concluded that the forecasting approach and site effects will change the shape of the distribution.…”

Section: Modeling Forecast Errorsmentioning

confidence: 99%

See 2 more Smart Citations

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Yıldız¹,

Tekin²,

Ganı³

et al. 2017

Sustainability

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During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality.

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Section: Resultsmentioning

confidence: 99%

Section: Modeling Forecast Errorsmentioning

confidence: 99%

Section: Modeling Forecast Errorsmentioning

confidence: 99%

See 1 more Smart Citation

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Yıldız¹,

Tekin²,

Ganı³

et al. 2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…It can be observed that the distributions of forecast errors conditioned on y* are quite different. In [3], [27], Beta distributions are used to approximate those histograms one by one, while the authors in [4], [5], and [6] report that the accuracy of Beta still needs improvement. Beyond the variability, wind power uncertainties of adjacent wind farms have stochastic dependence.…”

Section: Wind Power Uncertaintymentioning

confidence: 99%

“…Beta distribution is suggested to model forecast error uncertainties. In a relevant study [4], the authors combine the Beta distribution and Dirac delta function, and obtain a "mixed beta distribution", improving the model accuracy. Further, Bruninx et al find that Beta distribution is not able to fully characterize the skewed and heavy-tailed forecast errors [5].…”

Section: Introductionmentioning

confidence: 99%

A conditional model of wind power forecast errors and its application in scenario generation

2018

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A B S T R A C TIn power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.

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Refined Bidding and Operating Strategy for a Renewable Energy Generation and Energy Storage System Union

2020

Energy Storage for Power System Planning and Operation

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A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets

Cited by 163 publications

References 22 publications

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

A conditional model of wind power forecast errors and its application in scenario generation

Refined Bidding and Operating Strategy for a Renewable Energy Generation and Energy Storage System Union

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