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Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model

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  • Ostadi, Bakhtiar
  • Motamedi Sedeh, Omid
  • Husseinzadeh Kashan, Ali

Abstract

Deregulation of power industry has entailed important changes in the energy market. With the power industry being restructured, a generation company (GenCo) sells energy through auctions in a daily market, and submission of the appropriate amount of electricity with the right bidding price is important for a GenCo to maximize their profits and minimize the acceptance risk. The objective of this paper is to propose a novel approach for determination of the optimal biding patterns among GenCos in the deregulated power market using a hybrid of Markowitz Model and Genetic Algorithm (GA). While Markowitz Model as an optimization model considers the risk premium for biding patterns and GA as a search engine, considering the acceptance risk in deregulated market. A case study is used to examine the findings of the proposed approach. Also, to compare the proposed model, neural network by back propagation learning algorithm and real proposed pattern were considered. The numerical results indicate that the proposed model is statistically efficient and offers effective curves and biding patterns by lesser risk and equal profitability in day-ahead market as it is able to achieve better results compared to the neural network.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s036054421932211x
    DOI: 10.1016/j.energy.2019.116516
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    Cited by:

    1. Cassidy K. Buhler & Hande Y. Benson, 2023. "Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification," Papers 2306.12639, arXiv.org.
    2. Mojtaba Shivaie & Mohammad Kiani-Moghaddam & Philip D Weinsier, 2022. "Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures," Energy & Environment, , vol. 33(4), pages 696-727, June.
    3. Kavita Jain & Muhammed Basheer Jasser & Muzaffar Hamzah & Akash Saxena & Ali Wagdy Mohamed, 2022. "Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    4. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    5. 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).
    6. Setya Budi, Rizki Firmansyah & Sarjiya, & Hadi, Sasongko Pramono, 2022. "Indonesia's deregulated generation expansion planning model based on mixed strategy game theory model for determining the optimal power purchase agreement," Energy, Elsevier, vol. 260(C).
    7. 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).

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