Multi-objective optimal charging of plug-in electric vehicles in unbalanced distribution networks

Abstract Plug-in electric vehicles (PEVs) as new generations of transportation systems have recently become a promising solution to mitigate emissions of greenhouse gases produced by petroleum-based vehicles. Existing power systems may face serious reliability and power quality problems in supplying emerging PEV charging loads unless the charging task is coordinated. In addition, in real world applications, most PEVs are single-phase loads supposed to be charged from residential or commercial outlets. In this paper, a multi-objective optimization framework is proposed to optimally coordinate the charging of single-phase PEVs with dynamic behavior in unbalanced three-phase distribution systems employing smart grid facilities. Objective functions include total cost of purchasing energy in a multi-tariff pricing environment as well as grid total energy losses over charging span. The objective functions are optimized subject to network security, power quality, and PEV constraints. Fuzzy memberships are used to transform differently-scaled objective functions into a same range in order to ensure the Pareto optimality of the multi-objective solution. The proposed method is tested on an unbalanced three-phase distribution system and obtained results, which are discussed in detail, confirm its efficiency in getting a solution satisfying both objective functions as well as in the speed.