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A Bi-Level Optimization Model for Virtual Power Plant Membership Selection Considering Load Time Series

Author

Listed:
  • Yantao Wang

    (School of Economics and Management, Northeast Electric Power University, Jilin 132012, China)

  • Yinhan Zhang

    (School of Economics and Management, Northeast Electric Power University, Jilin 132012, China)

  • Xuesong Qi

    (Jilin Power Supply Company, State Grid Jilin Electric Power Company Co., Ltd., Jilin 132001, China)

  • Meiqi Wang

    (School of Economics and Management, Northeast Electric Power University, Jilin 132012, China)

  • Xinyue Wang

    (School of Economics and Management, Northeast Electric Power University, Jilin 132012, China)

Abstract

In order to improve the level of new energy consumption and reduce the dependence of the power system on traditional fossil energy, this paper proposed a bi-level optimization model for virtual power plant member selection by means of coordination and complementarity among different power sources, aiming at optimizing system economy and clean energy consumption capacity and combining it with the time sequence of load power consumption. The method comprises the following steps: (1) The processing load, wind power, and photovoltaic data by using ordered clustering to reflect the time sequence correlation between new energy and load and (2) uses a double-layer optimization model, wherein the upper layer calculates the capacity configuration of thermal power and energy storage units in a virtual power plant and selects the new energy units to participate in dispatching by considering the utility coefficient of the new energy units and the environmental benefit of the thermal power units. The Latin hypercube sampling (LHS) method was used to generate a large number of subsequences and the mixed integer linear programming (MILP) algorithm was used to calculate the optimal operation scheme of the system. The simulation results showed that by reducing the combination of subsequences between units and establishing a reasonable unit capacity allocation model, the average daily VPP revenue increased by RMB 12,806 and the proportion of new energy generation increased by 1.8% on average, which verified the correctness of the proposed method.

Suggested Citation

  • Yantao Wang & Yinhan Zhang & Xuesong Qi & Meiqi Wang & Xinyue Wang, 2023. "A Bi-Level Optimization Model for Virtual Power Plant Membership Selection Considering Load Time Series," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2138-:d:1044817
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    References listed on IDEAS

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    1. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    2. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    3. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
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    1. Tianfeng Chu & Xingchen An & Wuyang Zhang & Yan Lu & Jiaqi Tian, 2023. "Multiple Virtual Power Plants Transaction Matching Strategy Based on Alliance Blockchain," Sustainability, MDPI, vol. 15(8), pages 1-16, April.

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