Optimal placement of charging infrastructures for large-scale integration of pure electric vehicles into grid

• Published in: International journal of electrical power & energy systems 2013-12, Vol.53, p.159-165
• Main Authors: Xu, Hao, Miao, Shihong, Zhang, Chunyong, Shi, Dongyuan
• Format: Article
• Language: English
• Subjects: Applied sciences; Binary particle swarm optimization; Centralized charging station; Charging; Distribution discipline; Electric vehicles; Exact sciences and technology; Ground, air and sea transportation, marine construction; Infrastructure; Large scale integration; Mathematical models; Optimal placement; Optimization; Placement; Pure electric vehicle; Road transportation and traffic; Searching; Taboo mechanism
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2013.04.022

•A mathematical model for optimal CCS placement is established.•The distribution discipline of CCSs in the CCS placement is expounded.•Candidate CCS locations with high efficiency and more reliability is identified.•The optimum placement of CCSs is achieved partly via a modified BPSO.•The correctness and applicability of the proposed strategy is very high. The optimal placement of charging infrastructures owns fundamental importance to the popularization of pure electric vehicles (PEVs). This paper focuses on the optimal configuration of centralized charging stations (CCSs) under the condition of large-scale integration of PEVs into grid. A mathematical model to formulate the optimal CCS placement problem is firstly established. Then the distribution discipline of CCSs in the optimum CCS configuration with minimum total transportation distance (TTD) is shed light on according to the mathematical model, and it in turn helps to identify the candidate CCS locations which turn out to be discrete, finite, fit for numerical calculation and reliable. Finally a further optimization model within the searching space of these candidate CCS locations is proposed to identify the optimum CCS configuration, and solved by a modified binary particle swarm optimization (BPSO) based on Taboo mechanism (TM). The resultant optimization method, named TM-BPSO, can make up the defect of premature convergence of the original BPSO to a certain extent. A large number of numerical examples verify the correctness of the proposed strategy and the applicability of the modified BPSO in this study.