| 引用本文: | 何国彬,杨金新,施铭涛,等.基于多目标蜉蝣算法的电动汽车充电站联合储能系统最优规划方法[J].电力系统保护与控制,2025,53(15):95-102.[点击复制] |
| HE Guobin,YANG Jinxin,SHI Mingtao,et al.Optimal planning of electric vehicle charging stations integrated with energy storage systems based on multi-objective mayfly algorithm[J].Power System Protection and Control,2025,53(15):95-102[点击复制] |
|
| 摘要: |
| 电动汽车(electric vehicle, EV)保有量的不断提升带来了电动汽车充电站(electric vehicle charging station, EVCS)的大量铺设。EV充电具有极高的随机性，这使得EVCS的建设使用会给配电网的负荷稳定性带来一定的冲击。为了缓解EV充电负荷导致的配电网稳定性下降问题，提出了一种基于电动汽车充电需求的EVCS联合电池储能系统(battery energy storage system, BESS)的多目标优化规划模型。该模型以最小化EVCS联合BESS综合成本(包括系统网损经济损失)、用户等待时间成本和系统电压波动为目标优化EVCS的规划方案，以达到EVCS建设者、用户及电网公司的多方共赢。此外，为了验证所提规划模型的有效性，设计了基于扩展的IEEE33节点测试系统的仿真实验。实验结果表明，采用多目标蜉蝣算法(multi-objective mayfly algorithm, MOMA)对目标进行配置，能够有效提高配电网的稳定性与经济性。 |
| 关键词: 电动汽车充电桩 最优规划 储能系统 网损 负荷波动 |
| DOI：10.19783/j.cnki.pspc.241053 |
| 投稿时间：2024-08-07修订日期：2024-09-08 |
| 基金项目:南方电网科技项目资助(YNKJXM20230337)“多能互补低碳园区综合能源系统关键技术研究与应用” |
|
| Optimal planning of electric vehicle charging stations integrated with energy storage systems based on multi-objective mayfly algorithm |
| HE Guobin,YANG Jinxin,SHI Mingtao,SU Rui,HUANG Yuanping,LI Jianyun,YANG Jin |
| (Dali Power Supply Bureau, Yunnan Power Grid Co., Ltd., Dali 671000, China) |
| Abstract: |
| The increasing ownership of electric vehicles (EVs) has led to the widespread deployment of electric vehicles charging stations (EVCS). However, the high randomness of EV charging behavior pose significant challenges to the load stability of distribution network. To alleviate the deterioration of distribution network stability caused by EV charging load, this paper proposes a multi-objective optimization planning model of EVCS integrated with battery energy storage systems (BESS) based on EV charging demand. The model aims to minimize the overall cost of the EVCS-BESS system, including economic losses due to network losses, user waiting time costs, and system voltage fluctuation, thus optimizing the installation of EVCS in distribution networks to achieve a win-win outcome for EVCS developers, users, and power grid operators. To verify the validity of the proposed planning model, a simulation experiment based on the extended IEEE33 node test system is designed. The experimental results show that using the multi-objective mayfly algorithm (MOMA) for optimization can effectively improve the stability and economic performance of the distribution network. |
| Key words: electric vehicle charging station optimal planning energy storage system network loss load fluctuation |
