Electric vehicle charging load spatial allocation optimization algorithm

As the movable power load large number of electric vehicles needing to be charged maybe lead to partial overload, line congestion and other issues by their disordered charging on the space. In order to reduce the effects of disordered charging behavior on the grid and complete electric vehicle charging load spatial allocation, the multi-objective optimization of minimizing system charging time, distance and dispatching charging load evenly is researched. The optimization is solved by PSO (particle swarm optimization) and GA (genetic algorithm) respectively. Constraints are handled by solution space conversion in PSO and by code in GA. The latter can effectively reduce the dimension of the problems and improve the calculation speed. The performances of both algorithms are compared in the same simulation example. The simulation results show that both algorithms can solve the electric vehicle charging load spatial allocation optimization in certain area. The effectiveness and feasibility of algorithm are shown by the results. When the vehicles are more the genetic algorithm's performance is much better than the PSO algorithm's and GA has higher practicality.