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

• Published in: European journal of operational research Vol. 280; no. 2; pp. 639 - 655
• Main Authors: Wozabal, David, Rameseder, Gunther
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 16.01.2020
• Subjects: Intraday trading; Markov decision processes; Stochastic dual dynamic programming; Stochastic programming; Stochastic dual dynamic programming; Markov decision processes; Stochastic programming; Intraday trading
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2019.07.022

•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.