Application of probabilistic wind power forecasting in electricity markets. (14th March 2012)

• Record Type: Journal Article
• Authors: Zhou, Z.; Botterud, A.; Wang, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.
• Is Part Of: Wind energy. Volume 16:Number 3(2013:Apr.)
• Page Start: 321
• Page End: 338
• Publication Date: 2012-03-14
• Subjects: wind power forecasting -- probabilistic forecasts -- stochastic unit commitment -- electricity system operation -- dynamic operating reserves; Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.1496
• Languages: English
• ISSNs: 1095-4244

Abstract

ABSTRACT: This paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile‐copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two‐settlement electricity market with clearing of day‐ahead and real‐time markets for energy and operating reserves. At the day‐ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast‐starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real‐time dispatch is based on the realized availability of wind power. To evaluate the model in a large‐scale real‐world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright © 2012 John Wiley & Sons, Ltd.