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Competition, risk and learning in electricity markets: An agent-based simulation study

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  • Esmaeili Aliabadi, Danial
  • Kaya, Murat
  • Sahin, Guvenc

Abstract

This paper studies the effects of learning and risk aversion on generation company (GenCo) bidding behavior in an oligopolistic electricity market. To this end, a flexible agent-based simulation model is developed in which GenCo agents bid prices in each period. Taking transmission grid constraints into account, the ISO solves a DC-OPF problem to determine locational prices and dispatch quantities. Our simulations show how, due to competition and learning, the change in the risk aversion level of even one GenCo can have a significant impact on all GenCo bids and profits. In particular, some level of risk aversion is observed to be beneficial to GenCos, whereas excessive risk aversion degrades profits by causing intense price competition. Our comprehensive study on the effects of Q-learning parameters finds the level of exploration to have a large impact on the outcome. The results of this paper can help GenCos develop bidding strategies that consider their rivals’ as well as their own learning behavior and risk aversion levels. Likewise, the results can help regulators in designing market rules that take realistic GenCo behavior into account.

Suggested Citation

  • Esmaeili Aliabadi, Danial & Kaya, Murat & Sahin, Guvenc, 2017. "Competition, risk and learning in electricity markets: An agent-based simulation study," Applied Energy, Elsevier, vol. 195(C), pages 1000-1011.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:1000-1011
    DOI: 10.1016/j.apenergy.2017.03.121
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    7. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    8. Guo, Hongye & Chen, Qixin & Xia, Qing & Kang, Chongqing, 2019. "Electricity wholesale market equilibrium analysis integrating individual risk-averse features of generation companies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Leila Niamir & Gregor Kiesewetter & Fabian Wagner & Wolfgang Schöpp & Tatiana Filatova & Alexey Voinov & Hans Bressers, 2020. "Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions," Climatic Change, Springer, vol. 158(2), pages 141-160, January.
    10. Priyanka Shinde & Ioannis Boukas & David Radu & Miguel Manuel de Villena & Mikael Amelin, 2021. "Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach," Energies, MDPI, vol. 14(13), pages 1-31, June.
    11. Rajesh Panda & Prashant Kumar Tiwari, 2022. "An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 347-362, February.
    12. Poplavskaya, Ksenia & Lago, Jesus & de Vries, Laurens, 2020. "Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets," Applied Energy, Elsevier, vol. 270(C).
    13. Yu, Liying & Wang, Peng & Chen, Zhe & Li, Dewen & Li, Ning & Cherkaoui, Rachid, 2023. "Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market," Applied Energy, Elsevier, vol. 350(C).
    14. Li, Qirui & Yang, Zhifang & Yu, Juan & Li, Wenyuan, 2023. "Impacts of previous revenues on bidding strategies in electricity market: A quantitative analysis," Applied Energy, Elsevier, vol. 345(C).
    15. Faia, Ricardo & Lezama, Fernando & Soares, João & Pinto, Tiago & Vale, Zita, 2024. "Local electricity markets: A review on benefits, barriers, current trends and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    16. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    17. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).

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