Trading wind power through physically settled options and short-term electricity markets

Athanasios Papakonstantinou*, Georgia Champeri, Stefanos Delikaraoglou, Pierre Pinson

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Wind power producers participating in today's electricity markets face significant variability in revenue streams, with potential high losses mostly due to wind's limited predictability and the intermittent nature of the generated electricity. In order to further expand wind power generation despite such challenges, it is important to maximize its market value and move decisively towards economically sustainable and financially viable asset management. In this paper, we introduce a decision-making framework based on stochastic optimization that allows wind power producers to hedge their position in the market by trading physically settled options in futures markets in conjunction with their participation in the short-term electricity markets. The proposed framework relies on a series of two-stage stochastic optimization models that identify a combined trading strategy for wind power producers actively participating in both financial and day-ahead electricity markets. The proposed models take into consideration penalties from potential deviations between day-ahead market offers and real-time operation and incorporates different preferences of risk aversion, enabling a trade-off between the expected profit and its variability. Empirical analysis based on data from the Nordic region illustrates high efficiency of the stochastic model and reveals increased revenues for both risk neutral and risk averse wind producers opting for combined strategies.

Original languageEnglish
JournalWind Energy
Volume22
Issue number11
Pages (from-to)1487-1499
ISSN1095-4244
DOIs
Publication statusPublished - 2019

Keywords

  • Electricity markets
  • Futures markets
  • Options
  • Risk
  • Stochastic programming
  • Trading strategies
  • Uncertainty
  • Wind energy

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