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Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty

Author

Listed:
  • Hao Sun

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
    SPIC Northeast Electric Power Co., Ltd., Shenyang 110181, China)

  • Yanmei Liu

    (Material Branch, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110004, China)

  • Penglong Qi

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Zhi Zhu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Zuoxia Xing

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Weining Wu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

In a highly competitive electricity spot market, virtual power plants (VPPs) that aggregate dispersed resources face various uncertainties during market transactions. These uncertainties directly impact the economic benefits of VPPs. To address the uncertainties in the economic optimization of VPPs, scenario analysis is employed to transform the uncertainties of wind turbines (WTs), photovoltaic (PV) system outputs, and electricity prices into deterministic problems. The objective is to maximize the VPP’s profits in day-ahead and intra-day markets (real-time balancing market) by constructing an economic optimization decision model based on two-stage stochastic programming. Gas turbines and electric vehicles (EVs) are scheduled and traded in the day-ahead market, while flexible energy storage systems (ESS) are deployed in the real-time balancing market. Based on simulation analysis, under the uncertainty of WTs and PV system outputs, as well as electricity prices, the proposed model demonstrates that orderly charging of EVs in the day-ahead stage can increase the revenue of the VPP by 6.1%. Additionally, since the ESS can adjust the deviations in day-ahead bid output during the intra-day stage, the day-ahead bidding strategy becomes more proactive, resulting in an additional 3.1% increase in the VPP revenue. Overall, this model can enhance the total revenue of the VPP by 9.2%.

Suggested Citation

  • Hao Sun & Yanmei Liu & Penglong Qi & Zhi Zhu & Zuoxia Xing & Weining Wu, 2024. "Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty," Energies, MDPI, vol. 17(16), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3940-:d:1452730
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    References listed on IDEAS

    as
    1. Abbasi, Mohammad Hossein & Taki, Mehrdad & Rajabi, Amin & Li, Li & Zhang, Jiangfeng, 2019. "Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach," Applied Energy, Elsevier, vol. 239(C), pages 1294-1307.
    2. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
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