Regulation Method for Active Distribution Network of Electric Vehicles Considering User Travel Uncertainty

doi:10.16183/j.cnki.jsjtu.2024.312

Abstract

Abstract:

To address the issues of the insufficient regulation resources and flexibility in current active distribution networks, this paper proposes a two-stage regulation method for electric vehicle (EV) participation in the optimization of active distribution networks. Considering the impact of EV regulation on charging demand of users under the uncertainty of their travel behavior, the proposed method intends to fully exploit the regulation potential of EVs while reducing the impact of regulation on the travel of vehicle owners. First, a power deviation index is established to characterize the impact on EV charging behavior, and EV aggregation is performed with the power deviation taken into account. Then, the optimal regulation of active distribution networks containing EV aggregations, energy storage systems, and distributed generation units is conducted. Finally, the aggregated EV charging/ discharging commands are disaggregated to individual EVs by minimizing user-side impact. The effectiveness of the proposed method is validated through simulation on the IEEE 33-bus system. Numerical experiments show that the strategy can balance the regulation capability of EVs and charging demand of users. Compared with the traditional method, the EV power deviation is reduced by 25%, and the accommodation capacity is increased by 57%.

Key words: active distribution network, electric vehicle (EV), ordered charging, travel behavior uncertainty, two-stage optimization

CLC Number:

TM732

CAI Muliang, FAN Ruixiang, HE Guidong, ZHU Yan, CHE Liang. Regulation Method for Active Distribution Network of Electric Vehicles Considering User Travel Uncertainty[J]. Journal of Shanghai Jiao Tong University, 2026, 60(6): 882-891.

Figures/Tables 12

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资源类型	并网节点	相关特性参数
PV	7,14,25,28	容量:650 kW
WT	10,33	容量:200 kW
ES	5,21	容量:200 kW·h 充放电功率上限:100 kW
EV	9,14,27,21	容量:50 kW·h 充放电功率上限:11 kW

场景	系统指标			约束边界
场景	风光弃电量/ (kW·h)	线路损耗/ (kW·h)	平均净负荷波动率 (p.u.)	电压波动区间 (p.u.)	最大净负荷波动率(p.u.)
1	8693.382	1886.472	0.0324	[0.925,1]	0.0499
2	5165.472	1820.186	0.0326	[0.926,1]	0.0493
3	635.730	1747.896	0.0228	[0.940,1.001]	0.0493
4	144.010	1707.752	0.0182	[0.943,1]	0.0498

方法	弃电量/ (kW·h)	线路损耗/ (kW·h)	平均负荷波动率(p.u.)
1	144.010	1707.752	0.0182
2	121.749	1703.328	0.0181
3	2271.459	1784.630	0.0276

方法	EV电能未达标率(p.u.)			未达标EV的平均电能缺值(p.u.)
方法	提前离站3 h	提前离站2 h	提前离站1 h	未达标EV的平均电能缺值(p.u.)
1	0.049	0.038	0.030	0.08
2	0.101	0.089	0.079	0.33
3	0.028	0.021	0.008	0.05

阶段	潮流最大误差 (p.u.)	PV、WT最大误差(p.u.)	EV最大误差		ES最大误差
阶段	潮流最大误差 (p.u.)	PV、WT最大误差(p.u.)	容量(p.u.)	功率(p.u.)	容量(p.u.)	功率(p.u.)
优化	2.08×10^-8	0	2.82×10^-8	2.35×10^-8	0	1.02×10^-8
分解			5.42×10^-8	0