Wind Power Trading Under Uncertainty in LMP Markets

• Type: Article in International Scientific Journal
• Date: 2012

Authors

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

Journal

Title: IEEE Transactions on Power Systems

Vol. 27 No. 2

Pages: 894-903

ISSN: 0885-8950

Publisher: IEEE

Indexing

ISI Web of Knowledge - 111 Citations

ISI Web of Science

Scopus - 133 Citations

Scientific classification

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

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: P-002-APM

DOI: 10.1109/tpwrs.2011.2170442

Resumo (PT): This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

Abstract (EN): This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10