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A Robust Day-Ahead Electricity Market Clearing Model Considering Wind Power Penetration

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  • Hongze Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping District, Beijing 102206, China)

  • Xuejie Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Fengyun Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yuwei Wang

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Xinhua Yu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the development and grid-connection of renewable energy sources such as wind power, there are more and more uncertainties in power systems, and large-scale wind power has brought many challenges to the security and stability the systems. These uncertainties have to be eliminated by means of upward or downward regulations of conventional generators and charging/discharging services of energy storage devices. Based on the analysis of the influence of wind power uncertainties on the day-ahead electricity market, this article proposes a robust clearing model for the day-ahead electricity market considering the wind power penetration, which can help to complete the dispatch of power system. Compared with the traditional models, the proposed model is a multi-objective model, which considers both the lowest operating cost and the least wind power curtailment of power system. Moreover, the obtained dual multipliers λ corresponding to the power balance constraints reflect the marginal cost of the power production in a certain period, that is, the locational marginal price (LMP), which can be used as the clearing prices. This robust market clearing model takes into consideration the economic and reliability of the system operation and accommodates as much renewable energy as possible. The simulation of three wind power producers was implemented on the IEEE 30-bus test system, which verified the rationality of the proposed approaches.

Suggested Citation

  • Hongze Li & Xuejie Wang & Fengyun Li & Yuwei Wang & Xinhua Yu, 2018. "A Robust Day-Ahead Electricity Market Clearing Model Considering Wind Power Penetration," Energies, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1772-:d:156495
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    References listed on IDEAS

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    1. 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.
    2. Li, Y.Z. & Wu, Q.H. & Li, M.S. & Zhan, J.P., 2014. "Mean-variance model for power system economic dispatch with wind power integrated," Energy, Elsevier, vol. 72(C), pages 510-520.
    3. Gokhan Ceyhan & Nermin Elif Kurt & H. Bahadir Sahin & Kurc{s}ad Derinkuyu, 2017. "Empirical comparison of three models for determining market clearing prices in Turkish day-ahead electricity market," Papers 1712.00235, arXiv.org.
    4. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    5. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:

    1. Roberto Felipe Andrade Menezes & Guilherme Delgado Soriano & Ronaldo Ribeiro Barbosa de Aquino, 2021. "Locational Marginal Pricing and Daily Operation Scheduling of a Hydro-Thermal-Wind-Photovoltaic Power System Using BESS to Reduce Wind Power Curtailment," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Zhang, Qian & Wu, Xiaohan & Deng, Xiaosong & Huang, Yaoyu & Li, Chunyan & Wu, Jiaqi, 2023. "Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market," Applied Energy, Elsevier, vol. 341(C).
    3. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).

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