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Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market

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  • Hao Bai

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shihong Miao

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xiaohong Ran

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chang Ye

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

A virtual power plant takes advantage of interactive communication and energy management systems to optimize and coordinate the dispatch of distributed generation, interruptible loads, energy storage systems and battery switch stations, so as to integrate them as an entity to exchange energy with the power market. This paper studies the optimal dispatch strategy of a virtual power plant, based on a unified electricity market combining day-ahead trading with real-time trading. The operation models of interruptible loads, energy storage systems and battery switch stations are specifically described in the paper. The virtual power plant applies an optimal dispatch strategy to earn the maximal expected profit under some fluctuating parameters, including market price, retail price and load demand. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and is solved by the fruit fly algorithm.

Suggested Citation

  • Hao Bai & Shihong Miao & Xiaohong Ran & Chang Ye, 2015. "Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market," Energies, MDPI, vol. 8(3), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:3:p:2268-2289:d:47202
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    References listed on IDEAS

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    Cited by:

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    2. Zuoyu Liu & Weimin Zheng & Feng Qi & Lei Wang & Bo Zou & Fushuan Wen & You Xue, 2018. "Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading," Energies, MDPI, vol. 11(6), pages 1-19, June.
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    12. Yuewen Jiang & Meisen Chen & Shi You, 2017. "A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration," Energies, MDPI, vol. 10(4), pages 1-19, April.
    13. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    14. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
    15. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    16. Chong Chen & Xuan Zhou & Xiaowei Yang & Zhiheng He & Zhuo Li & Zhengtian Li & Xiangning Lin & Ting Wen & Yixin Zhuo & Ning Tong, 2018. "Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads," Energies, MDPI, vol. 11(4), pages 1-22, April.
    17. Justin Ugwu & Kenneth C. Odo & Chibuike Peter Ohanu & Jorge García & Ramy Georgious, 2022. "Comprehensive Review of Renewable Energy Communication Modeling for Smart Systems," Energies, MDPI, vol. 16(1), pages 1-28, December.
    18. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    19. Tiago Pinto & Zita Vale & Isabel Praça & E. J. Solteiro Pires & Fernando Lopes, 2015. "Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning," Energies, MDPI, vol. 8(9), pages 1-26, September.

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