标题： 基于聚类方法和实际配电馈线的智能充电影响分析 显示英文标题

标题： Smart Charging Impact Analysis using Clustering Methods and Real-world Distribution Feeders

摘要： 电动汽车（EV）的预期广泛应用需要对现有的配电基础设施进行关键评估，因为电动汽车的接入会对配电网络施加额外的压力，可能导致组件过载和电能质量下降。 实施智能充电机制可以缓解这些不利影响，并推迟或避免升级。 本研究评估了两种智能充电策略——分时电价（TOU）定价和负载平衡（LB）在七个通过k均值聚类识别的代表性实际馈线上的性能。 对这些馈线进行了基于时间序列的稳态潮流分析，以模拟在四种不同的电动汽车注册场景下，两种策略对电动汽车充电的影响，并选择了三个具有代表性的日子来捕捉季节性负荷特征。 提出了一种电网升级策略，以以最低成本加强电网，支持电动汽车的集成。 结果表明，与没有智能充电策略的情况相比，TOU和LB策略都能有效管理额外的电动汽车负荷，减少对现有基础设施的升级需求和成本。当客户注册水平较高时，LB优于TOU。 这些发现支持了智能充电在促进电动汽车集成方面的可行性，同时保持配电网络的可靠性并降低投资成本。 显示英文摘要

摘要： The anticipated widespread adoption of electric vehicles (EVs) necessitates a critical evaluation of existing power distribution infrastructures, as EV integration imposes additional stress on distribution networks that can lead to component overloading and power quality degradation. Implementing smart charging mechanisms can mitigate these adverse effects and defer or even avoid upgrades. This study assesses the performance of two smart charging strategies - Time of Use (TOU) pricing and Load Balancing (LB) on seven representative real-world feeders identified using k-means clustering. A time series-based steady-state load flow analysis was conducted on these feeders to simulate the impact of EV charging under both strategies across four different EV enrollment scenarios and three representative days to capture seasonal load characteristics. A grid upgrade strategy has been proposed to strengthen the power grid to support EV integration with minimal cost. Results demonstrate that both TOU and LB strategies effectively manage the additional EV load with reduced upgrade requirement and cost to existing infrastructure compared to the case without smart charging strategies and LB outperforms TOU when the customer enrollment levels are high. These findings support the viability of smart charging in facilitating EV integration while maintaining distribution network reliability and reducing investment cost.