摘要
随着电动汽车产业的快速发展，充电基础设施建设成为制约其普及的关键因素之一。为解决这一问题并降低对电网的冲击，本文聚焦于电动汽车充电站布局优化及其对电网影响的分析。研究基于地理信息系统与电力系统仿真平台，构建了多目标优化模型，综合考虑交通流量、人口密度、电网承载能力等关键因素，提出了一种基于时空分布特征的充电站选址方法。通过引入分时电价机制和智能调度算法，实现了充电负荷的有效均衡。研究结果表明，优化后的充电站布局能够显著提高设施利用率，减少用户等待时间，同时有效缓解高峰时段电网压力。特别是在城市中心区域，优化方案使充电负荷波动降低了约30%，电网峰谷差减少了15%左右。此外，本研究首次提出了基于大数据分析的动态调整策略，可根据实际运行数据实时优化充电站配置，为未来城市交通电气化转型提供了重要参考。该成果不仅为充电基础设施规划提供了科学依据，也为实现智能电网与智慧交通融合发展奠定了理论基础。
关键词：电动汽车充电站布局；电网影响分析；多目标优化模型
Abstract
With the rapid development of the electric vehicle (EV) industry, the construction of charging infrastructure has become one of the critical factors constraining its widespread adoption. To address this issue and mitigate the impact on the power grid, this study focuses on the optimization of EV charging station layout and its effects on the power grid. By integrating Geographic Information Systems (GIS) and power system simulation platforms, a multi-ob jective optimization model is developed that comprehensively considers key factors such as traffic flow, population density, and grid capacity. A location selection method for charging stations based on spatiotemporal distribution characteristics is proposed. The introduction of time-of-use pricing mechanisms and intelligent scheduling algorithms achieves effective load balancing. The results indicate that the optimized layout of charging stations significantly improves facility utilization, reduces user waiting times, and effectively alleviates peak-hour grid pressure. Notably, in urban central areas, the optimized solution reduces charging load fluctuations by approximately 30% and decreases the peak-to-valley difference in the grid by around 15%. Additionally, this study introduces a dynamic adjustment strategy based on big data analysis for the first time, enabling real-time optimization of charging station configurations according to actual operational data. This provides crucial references for the future electrification transformation of urban transportation. The findings not only offer scientific evidence for the planning of charging infrastructure but also lay a theoretical foundation for the integration of smart grids and intelligent transportation systems.
Keywords：Electric Vehicle Charging Station Layout; Grid Impact Analysis; Multi-ob jective Optimization Model
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、电动汽车充电需求分析 2
(一) 用户行为模式研究 2
(二) 充电需求时空分布 3
(三) 不同场景需求特征 3
三、充电站布局优化模型 4
(一) 布局优化目标函数 4
(二) 约束条件设定 5
(三) 模型求解算法 5
四、对电网影响的量化分析 6
(一) 负荷特性分析 6
(二) 电压稳定性评估 7
(三) 电网损耗计算 7
结 论 9
参考文献 10
随着电动汽车产业的快速发展，充电基础设施建设成为制约其普及的关键因素之一。为解决这一问题并降低对电网的冲击，本文聚焦于电动汽车充电站布局优化及其对电网影响的分析。研究基于地理信息系统与电力系统仿真平台，构建了多目标优化模型，综合考虑交通流量、人口密度、电网承载能力等关键因素，提出了一种基于时空分布特征的充电站选址方法。通过引入分时电价机制和智能调度算法，实现了充电负荷的有效均衡。研究结果表明，优化后的充电站布局能够显著提高设施利用率，减少用户等待时间，同时有效缓解高峰时段电网压力。特别是在城市中心区域，优化方案使充电负荷波动降低了约30%，电网峰谷差减少了15%左右。此外，本研究首次提出了基于大数据分析的动态调整策略，可根据实际运行数据实时优化充电站配置，为未来城市交通电气化转型提供了重要参考。该成果不仅为充电基础设施规划提供了科学依据，也为实现智能电网与智慧交通融合发展奠定了理论基础。
关键词：电动汽车充电站布局；电网影响分析；多目标优化模型
Abstract
With the rapid development of the electric vehicle (EV) industry, the construction of charging infrastructure has become one of the critical factors constraining its widespread adoption. To address this issue and mitigate the impact on the power grid, this study focuses on the optimization of EV charging station layout and its effects on the power grid. By integrating Geographic Information Systems (GIS) and power system simulation platforms, a multi-ob jective optimization model is developed that comprehensively considers key factors such as traffic flow, population density, and grid capacity. A location selection method for charging stations based on spatiotemporal distribution characteristics is proposed. The introduction of time-of-use pricing mechanisms and intelligent scheduling algorithms achieves effective load balancing. The results indicate that the optimized layout of charging stations significantly improves facility utilization, reduces user waiting times, and effectively alleviates peak-hour grid pressure. Notably, in urban central areas, the optimized solution reduces charging load fluctuations by approximately 30% and decreases the peak-to-valley difference in the grid by around 15%. Additionally, this study introduces a dynamic adjustment strategy based on big data analysis for the first time, enabling real-time optimization of charging station configurations according to actual operational data. This provides crucial references for the future electrification transformation of urban transportation. The findings not only offer scientific evidence for the planning of charging infrastructure but also lay a theoretical foundation for the integration of smart grids and intelligent transportation systems.
Keywords：Electric Vehicle Charging Station Layout; Grid Impact Analysis; Multi-ob jective Optimization Model
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、电动汽车充电需求分析 2
(一) 用户行为模式研究 2
(二) 充电需求时空分布 3
(三) 不同场景需求特征 3
三、充电站布局优化模型 4
(一) 布局优化目标函数 4
(二) 约束条件设定 5
(三) 模型求解算法 5
四、对电网影响的量化分析 6
(一) 负荷特性分析 6
(二) 电压稳定性评估 7
(三) 电网损耗计算 7
结 论 9
参考文献 10
