Network planning for electric vehicle charging based on fuzzy multi-objective optimization
DOI:10.7667/PSPC170006
Key Words:charging network planning  service capacity maximization  energy losses minimization  fuzzy multi-objective optimization  genetic algorithm
Author NameAffiliationE-mail
DING Danjun Suzhou Power Supply Company, State Grid Jiangsu Electric Power Company, Suzhou 215004, China  
DAI Kang Suzhou Power Supply Company, State Grid Jiangsu Electric Power Company, Suzhou 215004, China  
ZHANG Xinsong School of Electrical Engineering, Nantong University, Nantong 226019, China prettypebble@163.com 
GU Juping School of Electrical Engineering, Nantong University, Nantong 226019, China  
ZHOU Hui School of Electrical Engineering, Nantong University, Nantong 226019, China  
QIAN Kejun Suzhou Power Supply Company, State Grid Jiangsu Electric Power Company, Suzhou 215004, China  
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Abstract:Network planning for Electric Vehicle (EV) charging is of importance to the development of EV and has direct impacts on the convenience of EV owners and the economic performance of distribution systems. An EV charging network planning model is proposed to maximize charging service capacity and to minimize energy losses in distribution systems. The model proposed is a typical multi-objective decision-making model, and two optimization objects of which are of different dimensions and might be inherent conflicting each other. As a result, they can not get their optimal results simultaneously. The original planning model is transformed into a single objective optimization problem based on maximum satisfaction degree by fuzzy processing two optimization objects through defining objective membership functions. The single objective optimization problem is then solved by Genetic Algorithm (GA). In the end, a 25-node traffic network and IEEE33 node distribution system are utilized to justify the formulation and solving technique presented here. This work is supported by National Natural Science Foundation of China (No. 51607098 and No. 61673226) Science and Technology Project of State Grid Corporation of China (No. SGJSSZ00FZWT1601138).
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