Optimization and scheduling method of virtual power plant with V2G charging robot participation based on carbon peaking and carbon neutrality goals

WANG Zhiqiang

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Optimization and scheduling method of virtual power plant with V2G charging robot participation based on carbon peaking and carbon neutrality goals

更新时间：2026-09-14

- Optimization and scheduling method of virtual power plant with V2G charging robot participation based on carbon peaking and carbon neutrality goals
- Vol. 63, Issue 9, (2026)
- 作者机构：
  
  南方电网调峰调频广东储能科技有限公司,广东,广州
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2026
- 稿件说明：
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WANG Zhiqiang. Optimization and scheduling method of virtual power plant with V2G charging robot participation based on carbon peaking and carbon neutrality goals[J]. 2026, 63(9).
DOI：

WANG Zhiqiang. Optimization and scheduling method of virtual power plant with V2G charging robot participation based on carbon peaking and carbon neutrality goals[J]. 2026, 63(9). DOI：

摘要

针对电动汽车通过电动汽车入网（vehicle to grid

V2G）充电机器人接入虚拟电厂后两者之间的利益协调问题，为了促进新能源消纳，降低电动汽车对电网的不利影响，在对含有V2G充电机器人和碳交易虚拟电厂调度系统进行分析的基础上，提出了一种基于双碳目标的V2G充电机器人参与的虚拟电厂优化调度模型。所提模型以电动汽车充放电、碳交易成本、碳排放、虚拟电厂收益、虚拟电厂管理和输出预测偏差补偿等成本综合最优为目标建立优化调度模型，并结合改进鲸鱼算法和改进粒子群算法对该模型进行求解。通过算例对所提模型的性能进行分析。结果表明，所提虚拟电厂优化调度方法能有效降低碳交易成本和CO2排放量，对电动汽车充放电状态进行优化，提高了虚拟电厂的整体收益。

Abstract

Aiming at the coordination of interests between electric vehicles and vehicle to grid（V2G）charging robots connected to virtual power plants

and to improve the consumption of new energy and reduce the impact of electric vehicles on the power grid

a virtual power plant optimization scheduling model based on carbon peaking and carbon neutrality goals is proposed after analyzing the scheduling system of virtual power plants containing V2G charging robots and carbon trading. This model aims to establish an optimal scheduling model with the comprehensive optimization of the cost of electric vehicle charging and discharging

carbon trading

carbon emissions

virtual power plant revenue

virtual power plant management

and output prediction deviation compensation. The model is solved by using the improved whale optimization algorithm and the improved particle swarm optimization algorithm. The performance is analyzed through a numerical example. The results indicate that

the proposed optimization scheduling method for virtual power plants can effectively reduce carbon trading costs and CO2 emissions

and optimize the charging and discharging status of electric vehicles

which can effectively improve the overall revenue of the virtual power plant.

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