Stochastic optimization of trading strategies in sequential electricity markets

Emil Kraft*, Marianna Russo, Dogan Keles, Valentin Bertsch

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day-ahead and intraday trading decisions jointly for a portfolio with controllable and intermittent renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other.

Original languageEnglish
JournalEuropean Journal of Operational Research
Publication statusAccepted/In press - 2023


  • Electricity markets
  • Multi-stage stochastic programming
  • OR in energy
  • Risk modeling
  • Uncertainty modeling


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