Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets

Ksenia Poplavskaya*, Jesus Lago, Laurens de Vries

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

45 Citations (Scopus)
161 Downloads (Pure)

Abstract

Market-based procurement of balancing services in Europe is prone to strategic bidding due to the relatively small market size and a limited number of providers. In the European Union, balancing markets are undergoing substantial regulatory changes driven the efforts to harmonize the market design and better align it with the goals of the energy transition. It is proposed to decouple the balancing energy (real-time) market from the (forward) balancing capacity market and the price of balancing energy will be based on the marginal bid. In this paper, the potential effects of these changes on market participants’ strategies are analyzed using an agent-based model. This model compares the effects of a standalone balancing energy market with different pricing rules on economic efficiency with agents that apply naïve, rule-based and reinforcement-learning strategies. The results indicate that the introduction of a standalone balancing energy market reduces the cost of balancing, even in a concentrated market with strategic bidders. Marginal pricing consistently leads to lower weighted average prices than pay-as-bid pricing, regardless of the level of competition. Nevertheless, in an oligopoly with actors bidding strategically, prices can deviate from the competitive benchmark by a factor of 4–5. This implies that the introduction of a standalone balancing energy market does not entirely solve the issue of strategic bidding, but helps dampen the prices, as compared to the balancing market prior to the design change.

Original languageEnglish
Article number115130
JournalApplied Energy
Volume270
DOIs
Publication statusPublished - 2020

Keywords

  • Agent-based modeling
  • Balancing market
  • Bidding strategy
  • Market efficiency
  • Reinforcement learning

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