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The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors

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  • Jingmin Wang

    (School of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Wenhai Yang

    (School of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Huaxin Cheng

    (State Grid Longyan Electric Power Company, Longyan 364000, China)

  • Lingyu Huang

    (State Grid Longyan Electric Power Company, Longyan 364000, China)

  • Yajing Gao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

A virtual power plant (VPP) is a special virtual unit that integrates various distributed energy resources (DERs) distributed in the generation and consumption sides. The optimal configuration scheme of the VPP needs to break the geographical restrictions to make full use of DERs, considering the uncertainties. First, the components of the DERs and the structure of the VPP are briefly introduced. Next, the cubic exponential smoothing method is adopted to predict the VPP load requirement. Finally, the optimal configuration of the DER capacities inside the VPP is calculated by using portfolio theory and genetic algorithms (GA). The results show that the configuration scheme can optimize the DER capacities considering uncertainties, guaranteeing economic benefits of investors, and fully utilizing the DERs. Therefore, this paper provides a feasible reference for the optimal configuration scheme of the VPP from the perspective of investors.

Suggested Citation

  • Jingmin Wang & Wenhai Yang & Huaxin Cheng & Lingyu Huang & Yajing Gao, 2017. "The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors," Energies, MDPI, vol. 10(7), pages 1-12, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:968-:d:104295
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    Cited by:

    1. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    2. 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.
    3. Henri van Soest, 2018. "Peer-to-peer electricity trading: A review of the legal context," Competition and Regulation in Network Industries, , vol. 19(3-4), pages 180-199, September.

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