Optimization Strategy for Orderly Charging of Electric Vehicles Based on LC-ASSA

LI Na; ZHANG Jianwen; MA Chao; OUYANG Puyan; LU Wentao; YUAN Peisen

doi:10.20204/j.sp.2026.05005

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Optimization Strategy for Orderly Charging of Electric Vehicles Based on LC-ASSA

更新时间：2026-05-20

- Optimization Strategy for Orderly Charging of Electric Vehicles Based on LC-ASSA
- Vol. 54, Issue 5, Pages: 38-45(2026)
- 作者机构：
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- DOI：10.20204/j.sp.2026.05005
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- Published：2026
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李娜,张建文,马超,欧阳朴妍,陆文韬,袁培森.基于LC-ASSA搜索算法的电动汽车有序充电优化策略[J].智慧电力,2026,54(5):38-45.

doi:10.20204/j.sp.2026.05005
李娜,张建文,马超,欧阳朴妍,陆文韬,袁培森.基于LC-ASSA搜索算法的电动汽车有序充电优化策略[J].智慧电力,2026,54(5):38-45. DOI： 10.20204/j.sp.2026.05005.

doi:10.20204/j.sp.2026.05005 DOI：

摘要

针对大规模电动汽车无序充电引发的配电网负荷波动加剧与运行压力增大的问题，提出一种基于多机制改进麻雀搜索算法（LC-ASSA）的电动汽车有序充电优化策略。首先，以最小化负荷峰谷差和用户充电费用为双目标，综合考虑电网安全约束与用户充电需求，建立了电动汽车有序充电的数学优化模型。针对标准麻雀搜索算法（SSA）在大规模调度中易陷入局部最优、收敛速度慢的不足，提出LC-ASSA，其在SSA基础上融合了Logistic混沌初始化、自适应发现者比例调整与Lévy飞行机制，有效提升了种群多样性、全局搜索能力与收敛速度。最后，通过仿真实验对所提方法进行了验证，结果表明：与原始SSA相比，LC-ASSA在保持解质量的同时，运行时间缩短了62.87%，用户充电费用降低了1.03%，综合适应度优化幅度约为0.92%，显著优于对比算法，充分证明了LC-ASSA在电动汽车智能有序充电调度中的有效性与优越性。

Abstract

To address the issues of intensified distribution network load fluctuations and increased operational pressure caused by large-scale uncoordinated charging of electric vehicles（EVs）

this paper proposes a multi-mechanism improved sparrow search algorithm

namely the logistic-chaos enhanced adaptive sparrow search algorithm (LC-ASSA). Firstly

a mathematical optimization model for orderly EV charging is established with dual objectives of minimizing load peak-valley difference and user charging costs

while comprehensively considering grid security constraints and user charging demands. Aiming at the shortcomings of the standard sparrow search algorithm（SSA） in large-scale scheduling

such as susceptibility to local optima and slow convergence

LC-ASSA is proposed. This algorithm integrates Logistic chaotic initialization

adaptive adjustment of the proportion of discoverers

and Lévy flight mechanisms into SSA

effectively enhancing population diversity

global search capability

and convergence speed. Finally

simulation experiments validate the proposed method. The results show that

compared with the original SSA

LC-ASSA reduces execution time by 62.87%

lowers user charging costs by 1.03%

and optimizes the comprehensive fitness function value by approximately 0.92%

significantly outperforming comparison algorithms. This fully demonstrates the effectiveness and superiority of LC-ASSA in intelligent orderly charging scheduling of electric vehicles.

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