摘要
随着电动汽车的快速发展，充电设施规划与配电网协同优化成为亟待解决的关键问题。本文旨在研究电动汽车充电设施规划与配电网协同优化策略，以实现资源合理配置和系统高效运行。首先构建了考虑时空特性的电动汽车充电需求预测模型，基于大数据分析技术准确把握用户行为特征，为后续规划提供依据。然后提出了多目标优化模型，综合考虑经济性、可靠性等多方面因素，采用改进遗传算法求解，确定最优充电设施布局方案。研究结果表明，所提方法能够有效降低配电网损耗，提高电压稳定性，减少投资成本。创新点在于将电动汽车充电特性与配电网运行状态深度融合，从全局角度出发进行协同优化，突破传统单一规划模式局限。主要贡献是为电动汽车充电设施规划提供了新思路，有助于推动智能电网建设与发展，对相关政策制定具有重要参考价值。
关键词：电动汽车充电设施规划；配电网协同优化；多目标优化模型
Abstract
With the rapid development of electric vehicles (EVs), the协同 optimization of charging infrastructure planning and distribution network has become a critical issue that needs to be addressed. This study aims to investigate the co-optimization strategies for EV charging infrastructure planning and distribution network to achieve rational resource allocation and efficient system operation. A spatiotemporal characteristic-based EV charging demand forecasting model is first established, leveraging big data analytics technology to accurately capture user behavior characteristics, thereby providing a basis for subsequent planning. Subsequently, a multi-ob jective optimization model is proposed, which comprehensively considers multiple factors such as economic efficiency and reliability, and employs an improved genetic algorithm to solve for the optimal charging facility layout scheme. The research findings indicate that the proposed method can effectively reduce distribution network losses, enhance voltage stability, and decrease investment costs. The innovation lies in the deep integration of EV charging characteristics with the operational status of the distribution network, conducting co-optimization from a holistic perspective, thus breaking through the limitations of traditional single-planning models. The primary contribution is offering new insights into EV charging infrastructure planning, facilitating the development of smart grids, and providing significant reference value for relevant policy-making.
Keywords：Electric Vehicle Charging Infrastructure Planning; Distribution Network Co-Optimization; 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) 研究方法与技术路线 2
二、电动汽车充电需求分析 2
(一) 充电设施使用模式 2
(二) 充电负荷特性研究 3
(三) 充电需求预测方法 3
三、配电网适应性评估 4
(一) 配电网现状分析 4
(二) 充电设施接入影响 5
(三) 配电网承载能力评估 6
四、协同优化模型构建 6
(一) 目标函数设定 6
(二) 约束条件分析 7
(三) 模型求解算法 8
结 论 10
参考文献 11
随着电动汽车的快速发展，充电设施规划与配电网协同优化成为亟待解决的关键问题。本文旨在研究电动汽车充电设施规划与配电网协同优化策略，以实现资源合理配置和系统高效运行。首先构建了考虑时空特性的电动汽车充电需求预测模型，基于大数据分析技术准确把握用户行为特征，为后续规划提供依据。然后提出了多目标优化模型，综合考虑经济性、可靠性等多方面因素，采用改进遗传算法求解，确定最优充电设施布局方案。研究结果表明，所提方法能够有效降低配电网损耗，提高电压稳定性，减少投资成本。创新点在于将电动汽车充电特性与配电网运行状态深度融合，从全局角度出发进行协同优化，突破传统单一规划模式局限。主要贡献是为电动汽车充电设施规划提供了新思路，有助于推动智能电网建设与发展，对相关政策制定具有重要参考价值。
关键词：电动汽车充电设施规划；配电网协同优化；多目标优化模型
Abstract
With the rapid development of electric vehicles (EVs), the协同 optimization of charging infrastructure planning and distribution network has become a critical issue that needs to be addressed. This study aims to investigate the co-optimization strategies for EV charging infrastructure planning and distribution network to achieve rational resource allocation and efficient system operation. A spatiotemporal characteristic-based EV charging demand forecasting model is first established, leveraging big data analytics technology to accurately capture user behavior characteristics, thereby providing a basis for subsequent planning. Subsequently, a multi-ob jective optimization model is proposed, which comprehensively considers multiple factors such as economic efficiency and reliability, and employs an improved genetic algorithm to solve for the optimal charging facility layout scheme. The research findings indicate that the proposed method can effectively reduce distribution network losses, enhance voltage stability, and decrease investment costs. The innovation lies in the deep integration of EV charging characteristics with the operational status of the distribution network, conducting co-optimization from a holistic perspective, thus breaking through the limitations of traditional single-planning models. The primary contribution is offering new insights into EV charging infrastructure planning, facilitating the development of smart grids, and providing significant reference value for relevant policy-making.
Keywords：Electric Vehicle Charging Infrastructure Planning; Distribution Network Co-Optimization; 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) 研究方法与技术路线 2
二、电动汽车充电需求分析 2
(一) 充电设施使用模式 2
(二) 充电负荷特性研究 3
(三) 充电需求预测方法 3
三、配电网适应性评估 4
(一) 配电网现状分析 4
(二) 充电设施接入影响 5
(三) 配电网承载能力评估 6
四、协同优化模型构建 6
(一) 目标函数设定 6
(二) 约束条件分析 7
(三) 模型求解算法 8
结 论 10
参考文献 11
