摘 要
随着电动汽车产业的迅猛发展，充电设施规划与电网负荷管理成为亟待解决的关键问题。本文聚焦于电动汽车充电设施布局优化及其对电网负荷的影响分析，旨在构建科学合理的充电网络体系，确保电力系统的安全稳定运行。研究基于地理信息系统和大数据分析技术，综合考虑城市交通流量、人口分布、土地利用等因素，建立了多目标优化模型，以实现充电设施的空间合理配置。同时，引入分时电价机制，通过模拟不同场景下的充电行为模式，评估其对配电网峰谷差的影响。研究表明，在现有电网架构下，通过优化充电设施布局并配合智能调度策略，可有效降低高峰时段负荷压力，提高电网整体运行效率。特别是夜间低谷期充电比例显著增加，有助于平衡电力供需关系。此外，本研究首次提出基于用户行为特征的动态充电需求预测方法，为精准规划提供了理论依据和技术支持，具有重要的实际应用价值。研究成果不仅为城市交通电气化转型提供了决策参考，也为智能电网建设奠定了坚实基础。
关键词：电动汽车充电设施布局 电网负荷管理 多目标优化模型
Abstract
With the rapid development of the electric vehicle (EV) industry, the planning of charging infrastructure and grid load management have become critical issues that require immediate attention. This study focuses on optimizing the layout of EV charging facilities and analyzing its impact on grid load, aiming to establish a scientifically sound and rational charging network system to ensure the safe and stable operation of the power system. Based on Geographic Information System (GIS) and big data analysis technologies, this research integrates factors such as urban traffic flow, population distribution, and land use to develop a multi-ob jective optimization model for achieving spatially reasonable allocation of charging facilities. Additionally, time-of-use pricing mechanisms are introduced, and different scenario-based charging behavior patterns are simulated to evaluate their effects on the peak-to-valley difference in distribution networks. The findings indicate that under the existing grid architecture, optimizing the layout of charging facilities combined with intelligent scheduling strategies can effectively reduce peak load pressure and enhance the overall efficiency of grid operations. Notably, the proportion of nighttime off-peak charging has significantly increased, contributing to a more balanced power supply and demand relationship. Furthermore, this study proposes a novel dynamic charging demand prediction method based on user behavior characteristics, providing theoretical foundations and technical support for precise planning, which holds significant practical application value. The research outcomes not only offer decision-making references for the electrification transformation of urban transportation but also lay a solid foundation for the construction of smart grids.
Keyword:Electric Vehicle Charging Facility Layout Grid Load Management Multi-ob jective Optimization Model
目 录
引言 1
1电动汽车充电需求分析 1
1.1充电需求预测模型 1
1.2用户行为特征研究 2
1.3区域需求差异分析 2
2充电设施布局规划 3
2.1规划原则与目标 3
2.2关键影响因素分析 3
2.3布局优化算法研究 4
3电网负荷特性研究 5
3.1负荷曲线分析方法 5
3.2充电负荷时空分布 5
3.3负荷峰值缓解策略 6
4充电设施与电网协同 6
4.1协同规划框架构建 6
4.2智能调度技术应用 7
4.3经济效益评估分析 7
结论 8
参考文献 9
致谢 10
随着电动汽车产业的迅猛发展，充电设施规划与电网负荷管理成为亟待解决的关键问题。本文聚焦于电动汽车充电设施布局优化及其对电网负荷的影响分析，旨在构建科学合理的充电网络体系，确保电力系统的安全稳定运行。研究基于地理信息系统和大数据分析技术，综合考虑城市交通流量、人口分布、土地利用等因素，建立了多目标优化模型，以实现充电设施的空间合理配置。同时，引入分时电价机制，通过模拟不同场景下的充电行为模式，评估其对配电网峰谷差的影响。研究表明，在现有电网架构下，通过优化充电设施布局并配合智能调度策略，可有效降低高峰时段负荷压力，提高电网整体运行效率。特别是夜间低谷期充电比例显著增加，有助于平衡电力供需关系。此外，本研究首次提出基于用户行为特征的动态充电需求预测方法，为精准规划提供了理论依据和技术支持，具有重要的实际应用价值。研究成果不仅为城市交通电气化转型提供了决策参考，也为智能电网建设奠定了坚实基础。
关键词：电动汽车充电设施布局 电网负荷管理 多目标优化模型
Abstract
With the rapid development of the electric vehicle (EV) industry, the planning of charging infrastructure and grid load management have become critical issues that require immediate attention. This study focuses on optimizing the layout of EV charging facilities and analyzing its impact on grid load, aiming to establish a scientifically sound and rational charging network system to ensure the safe and stable operation of the power system. Based on Geographic Information System (GIS) and big data analysis technologies, this research integrates factors such as urban traffic flow, population distribution, and land use to develop a multi-ob jective optimization model for achieving spatially reasonable allocation of charging facilities. Additionally, time-of-use pricing mechanisms are introduced, and different scenario-based charging behavior patterns are simulated to evaluate their effects on the peak-to-valley difference in distribution networks. The findings indicate that under the existing grid architecture, optimizing the layout of charging facilities combined with intelligent scheduling strategies can effectively reduce peak load pressure and enhance the overall efficiency of grid operations. Notably, the proportion of nighttime off-peak charging has significantly increased, contributing to a more balanced power supply and demand relationship. Furthermore, this study proposes a novel dynamic charging demand prediction method based on user behavior characteristics, providing theoretical foundations and technical support for precise planning, which holds significant practical application value. The research outcomes not only offer decision-making references for the electrification transformation of urban transportation but also lay a solid foundation for the construction of smart grids.
Keyword:Electric Vehicle Charging Facility Layout Grid Load Management Multi-ob jective Optimization Model
目 录
引言 1
1电动汽车充电需求分析 1
1.1充电需求预测模型 1
1.2用户行为特征研究 2
1.3区域需求差异分析 2
2充电设施布局规划 3
2.1规划原则与目标 3
2.2关键影响因素分析 3
2.3布局优化算法研究 4
3电网负荷特性研究 5
3.1负荷曲线分析方法 5
3.2充电负荷时空分布 5
3.3负荷峰值缓解策略 6
4充电设施与电网协同 6
4.1协同规划框架构建 6
4.2智能调度技术应用 7
4.3经济效益评估分析 7
结论 8
参考文献 9
致谢 10
