Open AccessOptimal planning of charging station based on discrete distribution of charging demand
Author(s) -
Liang Yanchang,
Guo Chunlin,
Yang Jingjing,
Ding Zhaohao
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.0292
Subject(s) - sizing , beijing , particle swarm optimization , computer science , charging station , population , distribution (mathematics) , mathematical optimization , electric vehicle , automotive engineering , engineering , algorithm , mathematics , power (physics) , mathematical analysis , art , physics , demography , quantum mechanics , sociology , political science , law , china , visual arts
The continuous growth in the number of electric vehicles (EVs) places higher demands on public charging station (PCS) planning. This study proposes a PCS locating and sizing method based on the discrete distribution of EV charging demand. First, the distribution of EV charging demand is predicted by the distribution of gas station sales, functional areas, and population density. Next, a method of determining the PCS capacity based on the distribution of the charging demand is given. Then, the PCS locating and sizing model with the goal of minimising the total social cost is established, and the particle swarm optimisation algorithm is used to solve the problem. Finally, the method is verified by a case in Beijing. The results show that after obtaining several convenient parameters and using a simple algorithm calculation, this method can effectively reduce the construction and operation costs of PCSs and give full play to the social benefits of PCSs.