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An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms

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  • Aliabadi, Danial Esmaeili
  • Kaya, Murat
  • Şahin, Güvenç

Abstract

Deregulated electricity markets are expected to provide affordable electricity for consumers through promoting competition. Yet, the results do not always fulfill the expectations. The regulator's market-clearing mechanism is a strategic choice that may affect the level of competition in the market. We conceive of the market-clearing mechanism as composed of two components: pricing rules and rationing policies. We investigate the strategic behavior of power generation companies under different market-clearing mechanisms using an agent-based simulation model which integrates a game-theoretical understanding of the auction mechanism in the electricity market and generation companies' learning mechanism. Results of our simulation experiments are presented using various case studies representing different market settings. The market in simulations is observed to converge to a Nash equilibrium of the stage game or to a similar state under most parameter combinations. Compared to pay-as-bid pricing, bid prices are closer to marginal costs on average under uniform pricing while GenCos' total profit is also higher. The random rationing policy of the ISO turns out to be more successful in achieving lower bid prices and lower GenCo profits. In minimizing GenCos' total profit, a combination of pay-as-bid pricing rule and random rationing policy is observed to be the most promising.

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  • 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.
    • Handle: RePEc:eee:enepol:v:100:y:2017:i:c:p:191-205
    DOI: 10.1016/j.enpol.2016.09.063
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    2. Ruhang Xu & Zhilin Liu & Zhuangzhuang Yu, 2019. "Exploring the Profitability and Efficiency of Variable Renewable Energy in Spot Electricity Market: Uncovering the Locational Price Disadvantages," Energies, MDPI, vol. 12(14), pages 1-30, July.
    3. Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, vol. 12(1), pages 1-13, December.
    4. Liao, Qi & Tu, Renfu & Zhang, Wan & Wang, Bohong & Liang, Yongtu & Zhang, Haoran, 2023. "Auction design for capacity allocation in the petroleum pipeline under fair opening," Energy, Elsevier, vol. 264(C).
    5. Wu, Zhaoyuan & Zhou, Ming & Zhang, Ting & Li, Gengyin & Zhang, Yan & Liu, Xiaojuan, 2020. "Imbalance settlement evaluation for China's balancing market design via an agent-based model with a multiple criteria decision analysis method," Energy Policy, Elsevier, vol. 139(C).
    6. Pizarro-Irizar, Cristina, 2023. "Is it all about supply? Demand-side effects on the Spanish electricity market following Covid-19 lockdown policies," Utilities Policy, Elsevier, vol. 80(C).
    7. Csercsik, Dávid, 2021. "Strategic bidding via the interplay of minimum income condition orders in day-ahead power exchanges," Energy Economics, Elsevier, vol. 95(C).
    8. Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    9. Fang, Debin & Zhao, Chaoyang & Kleit, Andrew N., 2019. "The impact of the under enforcement of RPS in China: An evolutionary approach," Energy Policy, Elsevier, vol. 135(C).
    10. Yang, Peiwen & Dong, Jun & Lin, Jin & Liu, Yao & Fang, Debin, 2021. "Analysis of offering behavior of generation-side integrated energy aggregator in electricity market:A Bayesian evolutionary approach," Energy, Elsevier, vol. 228(C).
    11. Ye He & Siming Guo & Yu Wang & Yujia Zhao & Weidong Zhu & Fangyuan Xu & Chun Sing Lai & Ahmed F. Zobaa, 2022. "An Agent-Based Bidding Simulation Framework to Recognize Monopoly Behavior in Power Markets," Energies, MDPI, vol. 16(1), pages 1-19, December.
    12. 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.
    13. Motamedi Sedeh, Omid & Ostadi, Bakhtiar, 2020. "Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price," Energy Policy, Elsevier, vol. 145(C).
    14. Maximilian Borning & Larissa Doré & Michael Wolff & Julian Walter & Tristan Becker & Grit Walther & Albert Moser, 2020. "Opportunities and Challenges of Flexible Electricity-Based Fuel Production for the European Power System," Sustainability, MDPI, vol. 12(23), pages 1-26, November.
    15. 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).
    16. 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.
    17. 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).
    18. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klöckl, 2024. "Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 529-576, February.
    19. Xian Huang & Kun Liu, 2023. "Impact of Electricity Price Expectation in the Planning Period on the Evolution of Generation Expansion Planning in the Market Environment," Energies, MDPI, vol. 16(8), pages 1-21, April.
    20. 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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