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A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market

Author

Listed:
  • Rui Gao

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China)

  • Hongxia Guo

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China)

  • Ruihong Zhang

    (School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China)

  • Tian Mao

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Qianyao Xu

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Baorong Zhou

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Ping Yang

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
    National-Local Joint Engineering Laboratory for Wind Power Control and Integration Technology, Guangzhou 510640, China)

Abstract

The electricity spot market is now being implemented in China. Demand response, as a kind of flexible resource, is also being studied and explored for the constructed power market. Among the many demand response applications, the virtual power plant (VPP) as an aggregator of distributed energy resources (DERs), receives ever-increasing attention. However, the participation manner and related impacts of the VPP to the electricity spot market are still unknown within the current power market rules. Under this background, obeying the present trading rules of China’s electricity spot market, a two-stage dispatching model with optimized bidding and operating strategy in the day-ahead (DA) and real-time (RT) market for the VPP is proposed. In the designed model, the conditional risk value (CVaR) is adopted to address the risk encountered by the uncertainty of the electricity spot market price. The impact of the user-side over-deviated revenue mechanism (UORM) of the China spot market on the income of the VPP in the DA and RT market is also analyzed. For a full evaluation, different coefficients for the influence of DA and RT risk, UORM, and energy storage system (ESS) are tested to investigate their respective impacts on the revenue of the VPP. The simulation cases prove that the proposed method is helpful for the VPP to optimize DERs’ output in the electricity spot market according to its own risk preference.

Suggested Citation

  • Rui Gao & Hongxia Guo & Ruihong Zhang & Tian Mao & Qianyao Xu & Baorong Zhou & Ping Yang, 2019. "A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market," Energies, MDPI, vol. 12(17), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3402-:d:263780
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    References listed on IDEAS

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    1. Rakkyung Ko & Daeyoung Kang & Sung-Kwan Joo, 2019. "Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
    3. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    4. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    5. Kasaei, Mohammad Javad & Gandomkar, Majid & Nikoukar, Javad, 2017. "Optimal management of renewable energy sources by virtual power plant," Renewable Energy, Elsevier, vol. 114(PB), pages 1180-1188.
    6. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
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    Cited by:

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    4. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

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