基于V2G模式下电动汽车参与的微电网优化调度仿真研究

doi:10.16182/j.issn1004731x.joss.24-0183

摘要/Abstract

摘要：

为解决源荷不确定性对电网稳定运行产生的负面影响，提出了一种基于vehicle-to-grid(V2G)模式下电动汽车参与的微电网两阶段优化调度策略。在第一阶段建立了计及电池损耗的电动汽车充放电成本与负荷波动目标，并通过零和博弈确定车主与微网两主体利益目标间客观权重，利用电动汽车移动储能特性实现了负荷曲线优化与可再生能源消纳；在第二阶段以微电网运营成本与联络线交互功率标准差最低为目标，优化微电网内部可控单元出力和与上级电网交互功率；通过CPLEX求解器与改进的多目标灰狼算法对模型联合求解。仿真实验结果表明：该策略能有效降低车主成本，减少负荷波动，实现微电网经济、稳定运行。

关键词: 微电网, V2G(vehicle-to-grid), 两阶段优化, 零和博弈, 改进多目标灰狼

Abstract:

To address the negative impact of source-load uncertainty on the stable operation of the grid, a two-stage optimization scheduling strategy for the microgrid participation of electric vehicles based on the vehicle-to-grid (V2G) mode is proposed. In the first stage, the charging and discharging costs of electric vehicles as well as the load fluctuation target are determined taking into account the battery losses. Through a zero-sum game, we objectively weigh the interests of both vehicle owners and the microgrid, utilizing the mobile energy storage characteristics of electric vehicles to optimize the load curve and integrate renewable energy; in the second stage, with the aim of minimizing microgrid operating costs and reducing the standard deviation of the interconnection power line, we optimize the output of controllable units within the microgrid and manage the interaction power with the upper-level grid; the model is jointly solved using the CPLEX solver and the improved multi-objective grey wolf optimizer. Simulation results demonstrate that our proposed approach effectively reduces vehicle owner costs, decreases load fluctuations, and achieves economic and stable operation of the microgrid.

Key words: microgrid, vehicle-to-grid(V2G), two-stage optimization, zero-sum game, improved multi-objective grey wolf

中图分类号:

TM73

于仲安,肖宏亮,夏强威等 . 基于V2G模式下电动汽车参与的微电网优化调度仿真研究[J]. 系统仿真学报, 2025, 37(6): 1412-1426.

Yu Zhongan,Xiao Hongliang,Xia Qiangwei,et al . Simulation Study on Optimizing Microgrid Scheduling with Electric Vehicle Participation Under V2G Mode[J]. Journal of System Simulation, 2025, 37(6): 1412-1426.

图/表 21

图1

图2

图3

图4

表1

电动汽车参数

参数	数值	参数	数值
$P E V, c h, P E V, d i s / k W$	5	$D m a x / k m$	180
$C E V / (k W ⋅ h)$	30	S_EV,min/S_EV,max	0.2/0.9
$η c h, η d i s$	0.9	$S e x p$	0.8

表1

图5

表2

表3

其他参数

参数	数值	参数	数值
$k, V$	2.9, 10.1	$θ M T$	0.064 8
$η M T$	0.196	$θ B A$	0.001 8
$τ, ρ$	65, 0.106 8	$α i$	0.152, 0.000 05, 0.012 6
$γ i$	0.075 3, 0.395, 0.417 4	$β i$	0.186 7, 0.026 7, 0.100 7

表3

表4

玩家收益矩阵

$A 1 \ A 2$	$x 1 *$	$x 2 *$
$f 1$	1 558.2	175.5
$f 2$	147.9	322.4

表4

图6

表5

图7

图8

表6

图9

图10

表7

图11

图12

表8

图13

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方案	权重比例	方法	负荷波动/kW	车主费用/元
I	(0, 0)		1 520.0	1 234.3
II	(1, 0)	极值法	1 558.2	175.5
III	(0, 1)	极值法	147.9	322.4
IV	(0.905 6, 0.094 4)	零和博弈	951.6	185.8

调度策略	负荷峰值/kW	负荷谷值/kW	负荷峰谷差/kW	负荷方差/kW²	负荷波动/kW	车主费用/元
无序充电	695.5	60.4	635.0	37 869.0	1 520.0	1 234.3
有序充电	598.2	131.7	466.5	19 732.0	1 061.4	866.5
有序充放电	495.9	132.2	363.7	5 569.8	951.6	185.8

策略	运营成本/元	联络线功率标准差/kW
EV无序充电参与	3 041.7	197.3
EV有序充电参与	2 329.1	141.7
本文策略	966.2	77.2

算法	运营成本/元			联络线功率标准差/kW
算法	平均值	最小值	标准差	平均值	最小值	标准差
MOPSO	1 131.29	1 016.12	86.15	84.16	77.71	5.20
MOEA/D	1 109.18	1 033.98	86.63	80.83	75.39	5.47
MOGWO	927.17	887.98	47.90	79.01	76.01	4.43
MOIGWO	889.66	866.37	39.43	73.21	71.62	3.27

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