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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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    1. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Trading Stochastic Production in Electricity Pools," International Series in Operations Research & Management Science, in: Integrating Renewables in Electricity Markets, edition 127, chapter 7, pages 205-242, Springer.
    2. Li, Gong & Shi, Jing, 2012. "Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions," Applied Energy, Elsevier, vol. 99(C), pages 13-22.
    3. Derek Bunn & Fernando Oliveira, 2003. "Evaluating Individual Market Power in Electricity Markets via Agent-Based Simulation," Annals of Operations Research, Springer, vol. 121(1), pages 57-77, July.
    4. James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics 0004005, University Library of Munich, Germany.
    5. Li, Hongyan & Tesfatsion, Leigh, 2012. "Co-learning patterns as emergent market phenomena: An electricity market illustration," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 395-419.
    6. Vehvilainen, Iivo & Keppo, Jussi, 2003. "Managing electricity market price risk," European Journal of Operational Research, Elsevier, vol. 145(1), pages 136-147, February.
    7. Aliabadi, Danial Esmaeili & Kaya, Murat & Şahin, Güvenç, 2017. "An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms," Energy Policy, Elsevier, vol. 100(C), pages 191-205.
    8. Egging, Ruud & Pichler, Alois & Kalvø, Øyvind Iversen & Walle–Hansen, Thomas Meyer, 2017. "Risk aversion in imperfect natural gas markets," European Journal of Operational Research, Elsevier, vol. 259(1), pages 367-383.
    9. Moghimi Ghadikolaei, Hadi & Ahmadi, Abdollah & Aghaei, Jamshid & Najafi, Meysam, 2012. "Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4734-4743.
    10. Dorea Chin & Afzal Siddiqui, 2014. "Capacity expansion and forward contracting in a duopolistic power sector," Computational Management Science, Springer, vol. 11(1), pages 57-86, January.
    11. Chen, J.J. & Zhuang, Y.B. & Li, Y.Z. & Wang, P. & Zhao, Y.L. & Zhang, C.S., 2017. "Risk-aware short term hydro-wind-thermal scheduling using a probability interval optimization model," Applied Energy, Elsevier, vol. 189(C), pages 534-554.
    12. Garcia-Gonzalez, Javier & Parrilla, Ernesto & Mateo, Alicia, 2007. "Risk-averse profit-based optimal scheduling of a hydro-chain in the day-ahead electricity market," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1354-1369, September.
    13. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, December.
    14. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    15. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    16. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    17. Di Lorenzo, Giuseppina & Pilidis, Pericles & Witton, John & Probert, Douglas, 2012. "Monte-Carlo simulation of investment integrity and value for power-plants with carbon-capture," Applied Energy, Elsevier, vol. 98(C), pages 467-478.
    18. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    19. Derek W. Bunn and Fernando Oliveira, 2001. "An Application of Agent-based Simulation to the New Electricity Trading Arrangements of England and Wales," Computing in Economics and Finance 2001 93, Society for Computational Economics.
    20. Veit, Daniel J. & Weidlich, Anke & Krafft, Jacob A., 2009. "An agent-based analysis of the German electricity market with transmission capacity constraints," Energy Policy, Elsevier, vol. 37(10), pages 4132-4144, October.
    21. Sahin, Cem & Shahidehpour, Mohammad & Erkmen, Ismet, 2012. "Generation risk assessment in volatile conditions with wind, hydro, and natural gas units," Applied Energy, Elsevier, vol. 96(C), pages 4-11.
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    Cited by:

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    2. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
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    5. 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.
    6. 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).
    7. 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).
    8. 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).
    9. Anwar, Muhammad Bashar & Stephen, Gord & Dalvi, Sourabh & Frew, Bethany & Ericson, Sean & Brown, Maxwell & O’Malley, Mark, 2022. "Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets," Applied Energy, Elsevier, vol. 306(PA).
    10. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
    11. Ogbuabor, Jonathan E. & Ukwueze, Ezebuilo R. & Mba, Ifeoma C. & Ojonta, Obed I. & Orji, Anthony, 2023. "The asymmetric impact of economic policy uncertainty on global retail energy markets: Are the markets responding to the fear of the unknown?," Applied Energy, Elsevier, vol. 334(C).
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    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).

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