基于多目标粒子群的电动汽车优化充电策略

doi:10.16180/j.cnki.issn1007-7820.2024.03.007

Abstract

Abstract:

Household electric vehicle charging in residential areas has a strong centrality. Large-scale electric vehicle charging load causes large peak-valley load difference and other problems in the distribution network system. This study proposes a user charging selection control strategy based on Multi-Objective Particle Swarm Optimization(MPSO) algorithm. Through the analysis and prediction of electric vehicle charging load, a multi-objective optimization model is established with the minimum variance of the total system load and scheduling cost as the objective function. Meanwhile, considering the constraints of electric vehicle battery and system power, the MPSO algorithm is used to solve the optimal initial charging time of electric vehicles. The simulation results show that compared with unordered charging of EVs in residential areas, the EV charging strategy proposed in this study can effectively reduce load peak and dispatch cost.

Key words: electric vehicle, particle swarm optimization, orderly charging, load, charging power, peak-valley difference, power grid security, multi-objective optimization

CLC Number:

TP29

LI Tingting, LOU Ke, WANG Yuan, XU Huachao. Research on Optimal Charging Strategy of Electric Vehicle Based on Multi-Objective Particle Swarm Optimization[J].Electronic Science and Technology, 2024, 37(3): 51-56.

Figures/Tables 6

Figure 1.

Table 1.

Daily residential electricity price"

时段	电网电价/元·kWh^-1
08:00~12:00 17:00~21:00	0.869
12:00~17:00 21:00~24:00	0.687
0:00~8:00	0.365
EV放电电价/pric $e c o s t E V t$	0.100
并网补贴电价/pric $e c o m g r i d t$	0.300

Table 1.

Figure 2.

Figure 3.

Figure 4.

Table 2.

References 18

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EV数量	本文所提有序充电方案下系统负荷峰值/kW	无序充电下总系统负荷峰值/kW
10	89.69	122.50
30	95.21	167.20
50	108.60	222.60
70	137.60	275.80

Research on Optimal Charging Strategy of Electric Vehicle Based on Multi-Objective Particle Swarm Optimization

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