[论文解读] Design and Implementation of Low-Cost Electric Vehicles (Evs) Supercharger: A Comprehensive Review

Md Khaledur Rahman, Faysal Amin Tanvir+3 more|arXiv (Cornell University)|Feb 24, 2024

Electric Vehicles and Infrastructure被引用 20

一句话总结

本论文提出一种利用智能表数据和基于智能体的仿真器进行概率建模的方法，以评估成本驱动的充电策略对配电网的影响，聚焦于 Frederiksberg 的低压网络，在 40% 的 EV adoption 下。

ABSTRACT

This article presents a probabilistic modeling method utilizing smart meter data and an innovative agent-based simulator for electric vehicles (EVs). The aim is to assess the effects of different cost-driven EV charging strategies on the power distribution network (PDN). We investigate the effects of a 40% EV adoption on three parts of Frederiksberg's low voltage distribution network (LVDN), a densely urbanized municipality in Denmark. Our findings indicate that cable and transformer overloading especially pose a challenge. However, the impact of EVs varies significantly between each LVDN area and charging scenario. Across scenarios and LVDNs, the share of cables facing congestion ranges between 5% and 60%. It is also revealed that time-of-use (ToU)-based and single-day cost-minimized charging could be beneficial for LVDNs with moderate EV adoption rates. In contrast, multiple-day optimization will likely lead to severe congestion, as such strategies concentrate demand on a single day that would otherwise be distributed over several days, thus raising concerns about how to prevent it. The broader implications of our research suggest that, despite initial worries primarily centered on congestion due to unregulated charging during peak hours, a transition to cost-based smart charging, propelled by an increasing awareness of time-dependent electricity prices, may lead to a significant rise in charging synchronization, bringing about undesirable consequences for the power distribution network (PDN).

研究动机与目标

• 评估不同成本驱动的 EV 充电策略对配电网（PDN）的影响。
• 评估在 Frederiksberg 的低压配电网（LVDN）上 40% EV adoption 的影响。
• 在不同充电场景下识别 LVDN 区域的拥堵风险。
• 为政策提供关于分时定价与充电优化的建议，以缓解 PDN 拥堵。

提出的方法

• 使用智能表数据开发一种概率模型。
• 为 EV 充电情景构建基于智能体的仿真器。
• 在 Frederiksberg 的多个充电策略下分析三个 LVDN 区域。
• 评估电缆与变压器的拥堵暴露。
• 比较基于 ToU 的策略与单日成本最小化策略。
• 评估多日优化集中在单日上对风险的影响。

实验结果

研究问题

• RQ1 成本驱动的 EV 充电策略在 40% EV adoption 情况下对 Frederiksberg 的 LVDN 拥堵与负载有何影响？
• RQ2 在中等采纳情景下，哪些充电策略（ToU 基于、单日成本最小化、多日优化）最能缓解 PDN 拥堵？
• RQ3 在不同情景与区域（5%–60%）下，有多少比例的电缆会出现拥堵？
• RQ4 转向基于成本的智能充电对同步性与 PDN 可靠性有何更广泛的影响？

主要发现

• 拥堵风险因 LVDN 区域与充电情景而异，在不同情景下5%–60% 的电缆可能会拥堵。
• 在中等 EV adoption 下，基于 ToU 的充电与单日成本最小化充电对 LVDN 有益。
• 多日优化可能通过将需求集中在单日来导致严重拥堵。
• 向基于成本的充电的更广泛转变可能增加充电同步性，提升对 PDN 的担忧。