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The Optimization of Supply–Demand Balance Dispatching and Economic Benefit Improvement in a Multi-Energy Virtual Power Plant within the Jiangxi Power Market

Author

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  • Tang Xinfa

    (School of Economic Management and Law, Jiangxi Science and Technology Normal University, Nanchang 330038, China)

  • Wang Jingjing

    (School of Economic Management and Law, Jiangxi Science and Technology Normal University, Nanchang 330038, China)

  • Wang Yonghua

    (State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330032, China)

  • Wan Youwei

    (School of Economic Management and Law, Jiangxi Science and Technology Normal University, Nanchang 330038, China)

Abstract

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply–demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply–demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

Suggested Citation

  • Tang Xinfa & Wang Jingjing & Wang Yonghua & Wan Youwei, 2024. "The Optimization of Supply–Demand Balance Dispatching and Economic Benefit Improvement in a Multi-Energy Virtual Power Plant within the Jiangxi Power Market," Energies, MDPI, vol. 17(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4691-:d:1482057
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    References listed on IDEAS

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    1. Xiangchu Xu & Zewei Zhan & Zengqiang Mi & Ling Ji, 2023. "An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    2. Yang, Yulong & Zhao, Yang & Yan, Gangui & Mu, Gang & Chen, Zhe, 2024. "Real time aggregation control of P2H loads in a virtual power plant based on a multi-period stackelberg game," Energy, Elsevier, vol. 303(C).
    3. Kong, Xiangyu & Xiao, Jie & Wang, Chengshan & Cui, Kai & Jin, Qiang & Kong, Deqian, 2019. "Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant," Applied Energy, Elsevier, vol. 249(C), pages 178-189.
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    Cited by:

    1. Xinfa Tang & Guozu Hao & Yonghua Wang & Youwei Wan & Jingjing Wang & Yan Luo & Musa Dirane Nubea, 2024. "Synergistic Development Pathways: An Exploratory Study on the Urban–Rural Mutual Assistance Model and Low-Carbon Transformation of Henan’s Power Supply Industry Towards Dual-Carbon Goals," Energies, MDPI, vol. 17(24), pages 1-19, December.

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