Neighborhood electric vehicle charging scheduling using particle swarm optimization

Chargeable electric vehicles are projected to gain increasing market share becoming a significant load in distribution systems. An un-controlled charging of a large number of electric vehicles can potentially lead to problems in distribution circuits including low voltage levels and component overloads. These problems can be avoided by implementing a vehicle charging control scheme. This paper proposes a particle-swarm optimization-based method to centrally control vehicle charging on a neighborhood level. Vehicle charging is scheduled day-ahead for a given distribution system area while minimizing the total charging cost subject to grid and vehicle constraints. The proposed computationally efficient algorithm reduces the charging cost while enforcing voltage or line flow limits applying linear sensitivities. We demonstrate the method in a model of a real meshed 121-bus, 57-vehicle European low voltage distribution system.