Application of probabilistic wind power forecasting in electricity markets

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Vladimiro Miranda (Author) FEUP; Ricardo Bessa (Author) FEUP; Hrvoje Keko (Author) FEUP; Jean Sumaili (Author) FEUP; Audun Botterud (Author) Other; Jianhui Wang (Author) Other; Zhi Zhou (Author) Other

Journal

Title: Wind Energy

Vol. 16

Pages: 321-338

ISSN: 1095-4244

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 30 Citations

ISI Web of Science

Scopus - 34 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: P-005-1XY

DOI: 10.1002/we.1496

Abstract (EN): 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 (c) 2012 John Wiley & Sons, Ltd.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18