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文章摘要
基于电动汽车有序充电策略的配电网调控优化
Control and optimization of distribution network based on orderly charging strategy of electric vehicles
Received:June 10, 2024  Revised:July 14, 2024
DOI:10.19753/j.issn1001-1390.2026.09.017
中文关键词: 电动汽车  蒙特卡洛模拟  麻雀搜索算法  多目标优化  配电网优化
英文关键词: electric vehicle, Monte Carlo simulation, sparrow search algorithm, multi-objective optimization, distribution network optimization
基金项目:国家电网有限公司科技项目(5108-202218280A-2-142-XG)
Author NameAffiliationE-mail
QIAO Shichao North China Electric Power University Gregory_Qiao@outlook.com 
HOU Langbo North China Electric Power University houlangbo1@163.com 
SUN Hao North China Electric Power University sunhao161113@163.com 
CHEN Heng* North China Electric Power University heng@ncepu.edu.cn 
LIU Tao Beijing Guo Dian Tong Network Technology Co Ltd 15210926258@163.com 
LIU Wenyi North China Electric Power University lwy@ncepu.edu.cn 
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中文摘要:
      随着我国电动汽车保有量的逐年攀升,大规模电动汽车充电负荷的无序接入给电网的安全高效运行带来了新的挑战。采用麻雀搜索算法,以用户充电费用最小、配电网负荷峰谷差和网损最小为目标建立优化模型以实现对电动车充电行为的优化调度。与传统的无序充电情景对比分析发现,采用智能优化算法的电动汽车充电策略能够有效降低用户充电费用,明显减轻电网负荷波动,降低电压偏移,减小有功网损,降低了电动汽车充电负荷对电力系统运行安全性和经济性的影响。
英文摘要:
      With the yearly rise of electric vehicle (EV) ownership in China, the disorderly access of large-scale electric vehicle charging loads brings new challenges to the safe and efficient operation of the power grid. The sparrow search algorithm is used to establish an optimization model with the objectives of minimizing user charging cost, minimizing peak-to-valley difference in distribution network loads and minimizing network loss to achieve optimal scheduling of EVs charging behavior. Compared with the traditional disorderly charging scenario, it is found that the electric vehicle charging strategy using intelligent optimization algorithm can effectively reduce the charging cost of users, significantly reduce the load fluctuation of the grid, lower the voltage offset, decrease the active network loss, and reduce the impact of the EV charging load on the operation security and economy of power system.
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