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Volume 13
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10.3390/pr13061658
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Article

Scheduling Strategy of Virtual Power Plant Alliance Based on Dynamic Electricity and Carbon Pricing Using Master–Slave Game

by
Qiang Zhang
1,
Shangang Ma
1,*,
Fubao Jin
1,
Jiawei Li
1,
Ruiting Zhao
1,
Zengyao Liang
2 and
Xuwei Ren
2
1
School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China
2
Xining Fangsheng Electric Power Design Co., Ltd., Xining 810000, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(6), 1658; https://doi.org/10.3390/pr13061658
Submission received: 21 April 2025 / Revised: 15 May 2025 / Accepted: 22 May 2025 / Published: 25 May 2025
(This article belongs to the Section Energy Systems)
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Abstract

In the context of electricity and carbon markets, with the in-depth research of virtual power plants and to realize the mutual assistance of electric energy in different regions within the same distribution network, a scheduling strategy of virtual power plant alliance based on dynamic electricity and carbon pricing using the Master–Slave game is proposed. Firstly, an interactive framework of virtual power plant alliance is designed in which the alliance operator formulates the electricity and carbon prices, and each user entity formulates the operation plan according to the prices. Secondly, the information gap decision theory is adopted to handle the uncertainties on the source–load side. Based on the Master–Slave game and source–load interaction, an economic optimal dispatching model for the virtual power plant alliance is established. Finally, the particle swarm optimization algorithm nested with the CPLEX solver is used to solve the model, and the rationality and effectiveness of the proposed strategy are demonstrated through case analysis. The simulation results show that, after considering the electricity energy interaction and dynamic electricity–carbon pricing, the daily operation cost of the virtual power plant alliance was reduced by 47.7%, carbon emissions decreased by 24.6%, and comprehensive benefits increased by 77.2%.
Keywords: dynamic electricity and carbon pricing; information gap decision theory; optimal scheduling; source–load interaction; virtual power plant dynamic electricity and carbon pricing; information gap decision theory; optimal scheduling; source–load interaction; virtual power plant

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MDPI and ACS Style

Zhang, Q.; Ma, S.; Jin, F.; Li, J.; Zhao, R.; Liang, Z.; Ren, X. Scheduling Strategy of Virtual Power Plant Alliance Based on Dynamic Electricity and Carbon Pricing Using Master–Slave Game. Processes 2025, 13, 1658. https://doi.org/10.3390/pr13061658

AMA Style

Zhang Q, Ma S, Jin F, Li J, Zhao R, Liang Z, Ren X. Scheduling Strategy of Virtual Power Plant Alliance Based on Dynamic Electricity and Carbon Pricing Using Master–Slave Game. Processes. 2025; 13(6):1658. https://doi.org/10.3390/pr13061658

Chicago/Turabian Style

Zhang, Qiang, Shangang Ma, Fubao Jin, Jiawei Li, Ruiting Zhao, Zengyao Liang, and Xuwei Ren. 2025. "Scheduling Strategy of Virtual Power Plant Alliance Based on Dynamic Electricity and Carbon Pricing Using Master–Slave Game" Processes 13, no. 6: 1658. https://doi.org/10.3390/pr13061658

APA Style

Zhang, Q., Ma, S., Jin, F., Li, J., Zhao, R., Liang, Z., & Ren, X. (2025). Scheduling Strategy of Virtual Power Plant Alliance Based on Dynamic Electricity and Carbon Pricing Using Master–Slave Game. Processes, 13(6), 1658. https://doi.org/10.3390/pr13061658

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