Collaborative Optimal Dispatch of Multi-virtual Power Plants Considering Low-carbon Goals and Master-Slave Game

LI Deyi; XU Chunmei; LIU Hua; LIU Hongsheng; PENG Daogang

doi:10.19805/j.cnki.jcspe.2026.250005

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Collaborative Optimal Dispatch of Multi-virtual Power Plants Considering Low-carbon Goals and Master-Slave Game

更新时间：2026-10-10

- Collaborative Optimal Dispatch of Multi-virtual Power Plants Considering Low-carbon Goals and Master-Slave Game
- Chinese Journal of Power Engineering Vol. 46, Issue 4, Pages: 208-220(2026)
- 作者机构：
  
  1. 上海电力大学自动化工程学院,上海,200090
  2. 中电电力检修工程有限公司,上海,200086
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250005
  CLC： TM73
- Published：2026
- 稿件说明：
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李德毅, 徐春梅, 刘华, 刘红升, 彭道刚. 考虑低碳及主从博弈的多虚拟电厂协同优化调度[J]. 动力工程学报, 2026, 46(4): 208-220.
李德毅, 徐春梅, 刘华, 刘红升, 彭道刚. 考虑低碳及主从博弈的多虚拟电厂协同优化调度[J]. 动力工程学报, 2026, 46(4): 208-220. DOI： 10.19805/j.cnki.jcspe.2026.250005.

DOI： 10.19805/j.cnki.jcspe.2026.250005

摘要

为提高能源利用率

并提高虚拟电厂利益

降低碳排放量

提出了一种考虑低碳与主从博弈的多虚拟电厂协同优化调度方法。首先构建多虚拟电厂电热协同博弈优化架构。引入运营商(DSO)为领导者

根据虚拟电厂(VPP)调度策略制定能源交易价格

将虚拟电厂作为跟随者

根据交易价格制定能源调度及购售电策略

两者相互博弈。然后引入阶梯碳交易和双重补偿需求响应

进一步探索主从博弈的低碳潜力。最后建立DSO及VPP的优化调度数学模型

并采用粒子群算法(PSO)结合混合整数非线性优化(MINLP)求解。设置4种场景进行对比分析。结果表明:该博弈模型能够促进多虚拟电厂实现多能协同互补

同时保证各虚拟电厂利益最大;由于虚拟电厂之间能源互动增多

该模型结合了阶梯式碳交易

能够有效降低碳排放量

而双重补偿需求响应会引导负荷削减

提升低碳效率。

Abstract

To improve energy efficiency

increase the interests of virtual power plants (VPPs)

and reduce carbon emissions

a collaborative optimal dispatch method for multi-VPPs considering low-carbon goals and a master-slave game was proposed. First

the power-heat collaborative game optimization framework for multi-VPPs was constructed. The distribution system operator (DSO) was introduced as the leader

which formulated energy trading prices according to the dispatch strategies of VPPs. VPPs were regarded as followers

which developed energy dispatch and electricity purchase-sales strategies based on the trading prices

thus forming a mutual game relationship between the two parties. Then

a stepped carbon trading mechanism and a dual-compensation demand response were incorporated to further explore the low-carbon potential of the master-slave game. Finally

the optimal dispatch mathematical models for DSO and VPPs were developed

and the particle swarm optimization (PSO) algorithm combined with mixed-integer nonlinear programming (MINLP) was adopted for solution. Four scenarios were set up for comparative analysis. Results show that the proposed game model can promote multi-energy collaborative complementarity among multi-VPPs while maximizing the interests of each VPP. Owing to the increased energy interaction between VPPs and the integration of the stepped carbon trading mechanism

the model can effectively reduce carbon emissions. Meanwhile

the dual-compensation demand response can guide load curtailment and improve low-carbon efficiency.

关键词

Keywords

references

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