考虑条件风险价值的含EV虚拟电厂主从博弈优化调度

doi:10.3969/j.issn.2097-0706.2025.09.009

摘要/Abstract

摘要：

为研究电动汽车（EV）参与虚拟电厂（VPP）调度时的主从博弈情况，并分析条件风险价值（CVaR）对虚拟电厂收益产生的影响，文中建立考虑CVaR的含EV的VPP主从博弈调度双层模型。建立VPP与EV的主从博弈双层模型，其中VPP为上层领导者，制定电价并引导下层EV有序充电。在上层中引入CVaR理论衡量风光等可再生能源给模型带来的风险，建立以VPP收益最大，CVaR风险最小的上层目标函数，下层目标函数为EV充电成本最小。使用Matlab+Cplex求解器对该主从博弈调度模型进行求解，结果表明：EV参与VPP的比例不同，对VPP总收益产生的影响也不同，EV类型集中时获得的VPP收益最高，所建模型能够有效指导VPP规避风险，并使EV充电成本最小。

关键词: 条件风险价值, 电动汽车, 虚拟电厂, 主从博弈, 双层模型

Abstract:

To investigate the Stackelberg game when electric vehicles （EV） participate in virtual power plant （VPP） scheduling and to analyze the effect of conditional value at risk （CVaR） on VPP profits， a bilevel Stackelberg game scheduling model for EV-integrated VPP considering CVaR was established. A bilevel Stackelberg game model between the VPP and EV was established， in which the VPP acted as the upper-level leader， setting electricity prices and guiding the lower-level EV to carry out orderly charging. Then， CVaR theory was introduced into the upper level to measure the risks brought by renewable energy such as wind and solar power. An upper-level objective function was established to maximize VPP profits and minimize CVaR risks， and a lower-level objective function was set to minimize the charging costs of EV. The proposed Stackelberg game scheduling model was solved using MATLAB + CPLEX solver. The results showed that different participation ratios of EV in the VPP had different effects on the total VPP profits. The highest VPP profits were achieved when EV types were concentrated. The established model can effectively guide the VPP to avoid risks while minimizing the charging costs of EV.

Key words: onditional value at risk, electric vehicles, virtual power plant, Stackelberg game, bilevel model

中图分类号:

TK01：TM732

杨娜, 马龙腾, 刘浩睿, 刘耀泽, 杨国华. 考虑条件风险价值的含EV虚拟电厂主从博弈优化调度[J]. 综合智慧能源, 2025, 47(9): 80-88.

YANG Na, MA Longteng, LIU Haorui, LIU Yaoze, YANG Guohua. Optimal scheduling of Stackelberg game based dispatch of virtual power plant including EV considering conditional value at risk[J]. Integrated Intelligent Energy, 2025, 47(9): 80-88.

图/表 11

图1

图2

表1

表2

EV参数

参数	私家EV	公务EV	公交EV
$V i m a x$ /（kW·h）	57	57	57
$V i 0$ /（kW·h）	36.42	36.42	36.42
$P i, m a x E V s, c h$ /kW	8	8	8

表2

图3

图4

图5

图6

图7

图8

表3

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时段	电价
峰(07:00 — 09:00；17:00 — 23:00)	0.722
平(23：00 — 次日07:00)	0.488
谷(09:00 — 17:00)	0.255

风险厌恶系数	VPP总收益
0	2 894.866 3
0.2	2 853.002 5
0.4	2 810.888 1
0.6	2 769.024 3
0.8	2 726.909 9
0.9	2 687.051 6