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Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

Author

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  • Huiru Zhao

    (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, Beijing 102206, China)

  • Hao Lu

    (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, Beijing 102206, China)

  • Bingkang 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, Beijing 102206, China)

  • Xuejie Wang

    (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, Beijing 102206, China)

  • Shiying Zhang

    (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, Beijing 102206, China)

  • Yuwei Wang

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

Abstract

More and more attention has been paid to the development of renewable energy in the world. Microgrids with flexible regulation abilities provide an effective way to solve the problem of renewable energy connected to power grids. In this article, an optimization strategy of a microgrid participating in day-ahead market operations considering demand responses is proposed, where the uncertainties of distributed renewable energy generation, electrical load, and day-ahead market prices are taken into account. The results show that, when the microgrid implements the demand response, the operation cost of the microgrid decreases by 4.17%. Meanwhile, the demand response program can transfer the peak load of the high-price period to the low-price period, which reduces the peak valley difference of the load and stabilizes the load curve. Finally, a sensitivity analysis of three factors is carried out, finding that, with the increase of the demand response adjustable ratio or the maximum capacity of the electrical storage devices, the operation cost of the microgrid decreases, while, with the increase of the demand response cost, the operation cost of the microgrid increases and, finally, tends to the cost without the demand response. The sensitivity analysis reveals that the demand response cost has a reasonable pricing range to maximize the value of the demand response.

Suggested Citation

  • Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1255-:d:330001
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    References listed on IDEAS

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    2. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    3. Yanfeng Liu & Yaxing Wang & Xi Luo, 2020. "Design and Operation Optimization of Distributed Solar Energy System Based on Dynamic Operation Strategy," Energies, MDPI, vol. 14(1), pages 1-26, December.
    4. Jianying Li & Minsheng Yang & Yuexing Zhang & Jianqi Li & Jianquan Lu, 2023. "Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-15, April.
    5. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    6. Kalim Ullah & Quanyuan Jiang & Guangchao Geng & Rehan Ali Khan & Sheraz Aslam & Wahab Khan, 2022. "Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses," Energies, MDPI, vol. 15(9), pages 1-22, April.
    7. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    8. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    9. Zhao, Huiru & Li, Bingkang & Lu, Hao & Wang, Xuejie & Li, Hongze & Guo, Sen & Xue, Wanlei & Wang, Yuwei, 2022. "Economy-environment-energy performance evaluation of CCHP microgrid system: A hybrid multi-criteria decision-making method," Energy, Elsevier, vol. 240(C).
    10. Huiru Zhao & Hao Lu & Xuejie Wang & Bingkang Li & Yuwei Wang & Pei Liu & Zhao Ma, 2020. "Research on Comprehensive Value of Electrical Energy Storage in CCHP Microgrid with Renewable Energy Based on Robust Optimization," Energies, MDPI, vol. 13(24), pages 1-22, December.
    11. Seyed Mohammad Sharifhosseini & Taher Niknam & Mohammad Hossein Taabodi & Habib Asadi Aghajari & Ehsan Sheybani & Giti Javidi & Motahareh Pourbehzadi, 2024. "Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications," Energies, MDPI, vol. 17(21), pages 1-35, October.

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