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A Power Evolution Game Model and Its Application Contained in Virtual Power Plants

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  • Jinghong Zhou

    (Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique (China Electric Power Research Institute), Beijing 100192, China
    School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun 130012, China)

  • Ke Chen

    (Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique (China Electric Power Research Institute), Beijing 100192, China)

  • Weidong Wang

    (Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique (China Electric Power Research Institute), Beijing 100192, China)

Abstract

Demand response is an effective way to alleviate the pressure on power systems and improve energy utilisation efficiency. This study constructs a tripartite evolutionary game model on government, power companies and virtual power plants (VPPs), and analyses the dynamic behavioural selection mechanism of the three parties under demand-response mode. The results show that: (1) government guidance and management are effective means to promote the stability and equilibrium of the power system; (2) an increase in government subsidy, a reduction in the demand-response cost and an increase in opportunity cost will increase the enthusiasm for changes in demand-response behaviour in power companies; (3) government subsidies will improve the demand-response behaviour of VPPs. This study effectively provides theoretical support for the demand response of power systems, and realises the goal of power energy saving through the optimal choice of behaviour strategies for all parties in the power system.

Suggested Citation

  • Jinghong Zhou & Ke Chen & Weidong Wang, 2023. "A Power Evolution Game Model and Its Application Contained in Virtual Power Plants," Energies, MDPI, vol. 16(11), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4373-:d:1157695
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    Cited by:

    1. Yan Gao & Long Gao & Pei Zhang & Qiang Wang, 2023. "Two-Stage Optimization Scheduling of Virtual Power Plants Considering a User-Virtual Power Plant-Equipment Alliance Game," Sustainability, MDPI, vol. 15(18), pages 1-28, September.

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