具有高度可再生贯穿件的电动汽车充电站和配电系统的规划

IF 1.9 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Jingqi Zhang, Shu Wang, Cuo Zhang, Fengji Luo, Zhao Yang Dong, Yingliang Li
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引用次数: 11

摘要

陕西省自然科学基础研究计划,资助/奖号:2020JM‐542摘要随着电动汽车的日益普及,电动汽车充电站和配电系统已经成为一个耦合的物理系统。本文开发了一个多目标规划模型,用于EVCS的规模和选址以及高风电渗透率配电网的扩展。规划模型的目标是最大限度地降低配电系统的总投资成本和能源损失,同时最大限度地提高总捕获交通流量。考虑了与风电源相关的不确定性。此外,电动汽车日常充电负载的不确定性也是规划模型优化中的重要问题。为了对电动汽车负载的不确定性进行建模,采用了最近的情景生成(SG)方法。此外,引入了一种多目标优化工具,即多目标自然聚合算法(MONAA),以获得规划模型的最终解。基于耦合的54节点配电网和25节点交通网络系统进行了仿真,以验证所提出模型的效率和基于SG的MONAA的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Planning of electric vehicle charging stations and distribution system with highly renewable penetrations

Planning of electric vehicle charging stations and distribution system with highly renewable penetrations

With the increasing prevalence of electric vehicles (EVs), the EV charging station (EVCS) and power distribution have become a coupled physical system. A multi-objective planning model is developed herein for the sizing and siting of EVCSs and the expansion of a power distribution network with high wind power penetration. The objectives of the planning model are to minimise the total cost of investment and energy losses of the distribution system while maximising the total captured traffic flow. The uncertainties associated with wind power sources are considered. Additionally, the uncertainties in EV daily charging loads are also important concerns in the optimisation of the planning model. To model the EV load uncertainties, a recent scenario generation (SG) method is adopted. Further, a multi-objective optimisation tool, Multi-Objective Natural Aggregation Algorithm (MONAA), is introduced to obtain the final solutions of the planning model. The simulations based on coupled 54-node distribution network and 25-node traffic network systems are conducted to verify the efficiency of the proposed model and the effectiveness of SG-based MONAA.

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来源期刊
CiteScore
5.80
自引率
4.30%
发文量
18
审稿时长
29 weeks
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