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An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm

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  • Shivaie, Mojtaba
  • Ameli, Mohammad T.

Abstract

In this paper, a new approach is presented for developing optimal double-sided bidding strategy in security-constrained electricity markets by considering emission of pollutants, as further objectives. In the proposed methodology, both Generation Companies (GenCos) and Distribution Companies (DisCos) try to maximize their profit by implementation of optimal strategies, whiles they have incomplete information about their rivals and market mechanism of payment is locational marginal pricing. In addition, each participant provides its strategic bids based on supply function equilibrium model and it modifies its bidding strategies until Nash equilibrium points are computed. The proposed approach is modeled as a bi-level optimization problem with the upper sub problem addressing individual GenCos and DisCos and the lower sub problem addressing the Independent System Operator (ISO). The upper level maximizes the individual market participant's profit and the lower one solves the ISO's market clearing problem for maximizing Community Welfare Function (CWF). A Self-adaptive Global-based Harmony Search Algorithm (SGHSA) is used to obtain optimal bidding strategies. The proposed methodology has been implemented on the 6-machine 8-bus system test system to demonstrate the feasibility and effectiveness of the proposed approach. Simulation results illustrate the profitableness of the newly developed approach in the obtaining optimal bidding strategies.

Suggested Citation

  • Shivaie, Mojtaba & Ameli, Mohammad T., 2015. "An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm," Renewable Energy, Elsevier, vol. 83(C), pages 881-896.
  • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:881-896
    DOI: 10.1016/j.renene.2015.05.024
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    References listed on IDEAS

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    1. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.
    2. Liu, Zhen & Zhang, Xiliang & Lieu, Jenny, 2010. "Design of the incentive mechanism in electricity auction market based on the signaling game theory," Energy, Elsevier, vol. 35(4), pages 1813-1819.
    3. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    4. 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.
    5. Högselius, Per & Kaijser, Arne, 2010. "The politics of electricity deregulation in Sweden: the art of acting on multiple arenas," Energy Policy, Elsevier, vol. 38(5), pages 2245-2254, May.
    6. McGovern, T. & Hicks, C., 2004. "Deregulation and restructuring of the global electricity supply industry and its impact upon power plant suppliers," International Journal of Production Economics, Elsevier, vol. 89(3), pages 321-337, June.
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    Cited by:

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    2. Mahdi, Fahad Parvez & Vasant, Pandian & Kallimani, Vish & Watada, Junzo & Fai, Patrick Yeoh Siew & Abdullah-Al-Wadud, M., 2018. "A holistic review on optimization strategies for combined economic emission dispatch problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3006-3020.
    3. Huiru Zhao & Yuwei Wang & Mingrui Zhao & Qingkun Tan & Sen Guo, 2017. "Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing," Energies, MDPI, vol. 10(7), pages 1-27, July.
    4. 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).
    5. Afshar, Karim & Ghiasvand, Farshad Shamsini & Bigdeli, Nooshin, 2018. "Optimal bidding strategy of wind power producers in pay-as-bid power markets," Renewable Energy, Elsevier, vol. 127(C), pages 575-586.

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