Simulation Study on Optimizing Microgrid Scheduling with Electric Vehicle Participation Under V2G Mode

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

Abstract

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

CLC Number:

TM73

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

Figures/Tables 21

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Electric vehicle parameters

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

Table 1

Fig. 5

Table 2

Table 3

Other parameters

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

Table 3

Table 4

Player payoff matrix

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

Table 4

Fig. 6

Table 5

Fig. 7

Fig. 8

Table 6

Fig. 9

Fig. 10

Table 7

Fig. 11

Fig. 12

Table 8

Fig. 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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