Open AccessPlanning of electric vehicle charging stations and distribution system with highly renewable penetrations
Author(s) -
Zhang Jingqi,
Wang Shu,
Zhang Cuo,
Luo Fengji,
Dong Zhao Yang,
Li Yingliang
Publication year - 2021
Publication title -
iet electrical systems in transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 26
eISSN - 2042-9746
pISSN - 2042-9738
DOI - 10.1049/els2.12022
Subject(s) - sizing , renewable energy , automotive engineering , engineering , electric vehicle , electric power system , wind power , power (physics) , reliability engineering , simulation , electrical engineering , art , physics , quantum mechanics , visual arts
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.