Coordinated Charging Strategy for Electric Vehicles Based on Hierarchical Optimization

doi:10.11985/2021.03.019

Abstract

Abstract:

An orderly charging strategy is proposed for electric vehicles based on hierarchical optimization to reduce calculation and communication requirements of electric vehicles centralized control. Firstly, a two level optimization model for electric vehicles charging is established with the minimum total charging cost of users and the minimum variance for the total load of the distribution network. Secondly, aiming at the problem that the particle swarm algorithm is easy to fall into local optimality, the Tent chaotic map is used to initialize the particle population, the dynamic weight is introduced to change the updating mode of particle velocity, and Levy flight strategy is added to disturb the particle population. Finally, the improved particle swarm algorithm is used to optimize the charging start time of electric vehicles. Combined with an example, the influence of different user responsiveness and different charging quantities on the optimization result is compared to verify the effectiveness of the proposed strategy.

Key words: Electric vehicle, hierarchical optimization, charging cost, load variance, improved particle swarm algorithm

CLC Number:

TM73

FENG Shijie, LIU Tao, PAN Sa, CHEN Zhenghao, WANG Qing. Coordinated Charging Strategy for Electric Vehicles Based on Hierarchical Optimization[J]. Journal of Electrical Engineering, 2021, 16(3): 137-144.

Figures/Tables 8

References 17

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时段	电价/[元/(kW·h)]
00:00—08:00	0.36
08:00—21:00	0.56
21:00—24:00	0.36

参与模式	峰谷差/kW	方差/kW²	总充电费用/元
常规负荷	1 068.5	105 194.1	—
r=0%	1 296.5	137 995.0	919.1
r=50%	944.8	85 028.6	787.1
r=75%	859.6	70 896.1	729.2
r=100%	748.2	55 651.6	666.8

充电数量/辆	峰谷差/kW	方差/kW²	总充电费用/元
200	916.6	82 612.3	478.0
300	859.6	70 896.1	729.2
400	805.5	60 204.0	1 020.8