EV Charging Scheduling for Flattening Load Profile Based on Improved Particle Swarm

The load profile can reflect the change of the grid load over time. By connecting electric vehicles to the grid for charging and discharging, the goal of peak shaving and valley filling is realized, thus flattening the load profile and improving the stability of grid operation. Aiming at this characteristic, a method for optimal scheduling of electric vehicle charging based on an improved particle swarm algorithm is proposed in this paper. First, the proposed method constructs the electric vehicle charging scenario by Monte Carlo. Then, the improved particle swarm algorithm is used to solve the optimal charging and discharging quantity of electric vehicles in each period to minimize the load variance. By comparing the improved particle swarm algorithm with other intelligent algorithms on the benchmark function test set, the improved particle swarm algorithm is verified that it has superiority in convergence ability and optimization accuracy. Simulation results show that the scheduling of electric vehicle charging using the improved particle swarm algorithm reduces the peak valley difference of the power grid by 6.31% and the load variance by 74432kW
²
compared with the classical particle swarm algorithm.