Orderly Charging Strategy for Electric Vehicles Based on Hippopotamus Optimization Algorithm

TANG Xuanyi; LIU Ping; LAN Zheng; TANG Mingbin; HU Siyang

doi:10.3969/j.issn.1008-0198.2026.01.010

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Orderly Charging Strategy for Electric Vehicles Based on Hippopotamus Optimization Algorithm

更新时间：2026-03-11

- Orderly Charging Strategy for Electric Vehicles Based on Hippopotamus Optimization Algorithm
- Vol. 46, Issue 1, Pages: 76-83(2026)
- 作者机构：
  
  1. 湖南工业大学交通与电气工程学院,湖南,株洲,412007
  2. 湖南大学电气与信息工程学院,湖南,长沙,410082
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2026.01.010
  CLC： TM715
- Online First：10 March 2026，
  
  Published：10 March 2026
- 稿件说明：
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唐轩逸, 刘平, 兰征, 唐明斌, 胡思阳. 基于河马优化算法的电动汽车有序充电策略[J]. 湖南电力, 2026, 46(1): 76-83.

TANG Xuanyi, LIU Ping, LAN Zheng, et al. Orderly Charging Strategy for Electric Vehicles Based on Hippopotamus Optimization Algorithm[J]. 2026, 46(1): 76-83.
唐轩逸, 刘平, 兰征, 唐明斌, 胡思阳. 基于河马优化算法的电动汽车有序充电策略[J]. 湖南电力, 2026, 46(1): 76-83. DOI： 10.3969/j.issn.1008-0198.2026.01.010.

TANG Xuanyi, LIU Ping, LAN Zheng, et al. Orderly Charging Strategy for Electric Vehicles Based on Hippopotamus Optimization Algorithm[J]. 2026, 46(1): 76-83. DOI： 10.3969/j.issn.1008-0198.2026.01.010.

摘要

为缓解电动汽车无序充电对电网的影响

提高用户充电体验

提出一种基于河马优化算法的有序充电策略。首先

通过蒙特卡洛法模拟电动汽车的无序充电负荷;其次

以电网负荷峰谷差率与用户充电成本最小化为优化目标

建立电动汽车有序充电模型;最后

通过河马优化算法对模型进行求解。仿真结果表明

与无序充电相比

电网负荷峰谷差率降低了23个百分点

用户充电成本减少了38%

所提策略在降低电网负荷峰谷差率与用户充电成本方面均具有显著效果。

Abstract

To mitigate the impact of disorderly EV charging on the power grid and enhance user charging experiences

an orderly charging strategy based on the hippopotamus optimization algorithm is proposed. Firstly

the disorderly charging load of electric vehicles is simulated using the Monte Carlo method. Subsequently

an orderly charging model is established with the dual objectives of minimizing the grid load peak-to-valley difference rate and user charging costs. Finally

the hippopotamus optimization algorithm is employed to solve this model. Simulation results demonstrate that compared with disorderly charging

the grid load peak-to-valley difference rate is reduced by 23%

while user charging costs is decreased by 38%. Consequently

the proposed strategy exhibits significant effectiveness in both reducing the grid load peak-to-valley difference rate and user charging costs.

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references

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