Particle Swarm Optimization Based Scheduling Approach for Charging Electric Vehicle Population Addressing Factors Both Traffic and Power Grid

This paper proposes a particle swarm optimization (PSO) based optimal scheduling approach to achieve the minimum average charging time of electric vehicles (EVs) and the coupling minimum generation cost. To this end, an aggregated objective transformed from a bi-objective optimization problem is formulated taking into account the factors associated with the charging behavior of the electric vehicle population on the traffic side and the coupling factors on the power grid side. Further, an enhanced bilevel particle swarm optimization (EBPSO) algorithm is developed to solve the aforementioned aggregated objective. In order to avoid the particle swarm algorithm from falling into local optimums and round-trip motions between particles, learning particles are introduced into the EBPSO algorithm. Finally, the proposed approach is validated by using a coupled environment consisting of a 48-node traffic system and an IEEE 118-bus power system benchmark. The promising results encourage further work of this study in the next step.