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Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis

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  • Mahvi, M.
  • Ardehali, M.M.

Abstract

The objective of this study is to present a new method for determination of the optimal bidding strategies among generating companies (GenCo) in the electricity markets using agent-based approach and numerical sensitivity analysis (NSA). While agent-based approach provides for decision making, NSA can help with identifying the critical control points that lead to proper decisions to be taken by GenCos. To achieve the objective, the pricing mechanism used for settling the electricity market and determining the GenCos rewards is locational marginal pricing (LMP) and the sensitivity of each GenCo reward with respect to its bid is analyzed, then, the optimal strategy is determined. An example and a case study are used to illustrate the efficiency of the proposed method. The LMPs and allocated generations of GenCos show that the proposed method leads GenCos to learn a strategic manner and, as a result, increase prices and maximize their rewards. To validate the proposed method, the results from this study are compared with those available in the literature. The comparison of results shows an improved simulation time by 8.16 percent and total reward of market by 2.46 percent.

Suggested Citation

  • Mahvi, M. & Ardehali, M.M., 2011. "Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis," Energy, Elsevier, vol. 36(11), pages 6367-6374.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:11:p:6367-6374
    DOI: 10.1016/j.energy.2011.09.037
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    References listed on IDEAS

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    1. Junjie Sun & Leigh Tesfatsion, 2007. "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 291-327, October.
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    Cited by:

    1. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    2. Gao, Xiang & Chan, Ka Wing & Xia, Shiwei & Zhou, Bin & Lu, Xi & Xu, Da, 2019. "Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator," Energy, Elsevier, vol. 177(C), pages 183-191.
    3. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, vol. 9(9), pages 1-20, September.
    4. Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    5. Shinji Kuno & Kenji Tanaka & Yuji Yamada, 2022. "Effectiveness and Feasibility of Market Makers for P2P Electricity Trading," Energies, MDPI, vol. 15(12), pages 1-24, June.
    6. Ostadi, Bakhtiar & Motamedi Sedeh, Omid & Husseinzadeh Kashan, Ali, 2020. "Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model," Energy, Elsevier, vol. 191(C).
    7. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.

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