Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity

•Bidding on the Spanish spot markets can be planned as Markov decision process.•The problems solve sufficiently fast for the intended application.•Using intraday markets offers additional revenue over a pure day-ahead strategy.•Using complex bidding functions increases the out-of-sample profits. We...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 280; no. 2; pp. 639 - 655
Main Authors Wozabal, David, Rameseder, Gunther
Format Journal Article
LanguageEnglish
Published Elsevier B.V 16.01.2020
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Summary:•Bidding on the Spanish spot markets can be planned as Markov decision process.•The problems solve sufficiently fast for the intended application.•Using intraday markets offers additional revenue over a pure day-ahead strategy.•Using complex bidding functions increases the out-of-sample profits. We develop a multi-stage stochastic programming approach to optimize the bidding strategy of a virtual power plant (VPP) operating on the Spanish spot market for electricity. The VPP markets electricity produced in the wind parks it manages on the day-ahead market and on six staggered auction-based intraday markets. Uncertainty enters the problem via stochastic electricity prices as well as uncertain wind energy production. We set up the problem of bidding for one day of operation as a Markov decision process (MDP) that is solved using a variant of the stochastic dual dynamic programming algorithm. We conduct an extensive out-of-sample comparison demonstrating that the optimal policy obtained by the stochastic program clearly outperforms deterministic planning, a pure day-ahead strategy, a benchmark that only uses the day-ahead market and the first intraday market, as well as a proprietary stochastic programming approach developed in the industry. Furthermore, we study the effect of risk aversion as modeled by the nested Conditional Value-at-Risk as well as the impact of changes in various problem parameters.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2019.07.022