Improved PICEA-g-based Multi-Objective Optimization Scheduling Method for Distribution Grid with Large-Scale Electric Vehicles

Aiming at the problem that large-scale electric vehicles(EVs) access to the distribution network charging will affect the safety and reliability of the power grid, this paper proposes an optimal scheduling method for large-scale access of EVs to the distribution network based on the improved Preference-inspired Coevolutionary Algorithm. First, a large-scale response scheduling model is developed based on EVs as flexible loads. Then, a multi-objective optimization model is established by considering five factors: grid load fluctuation, user cost, environmental governance, user flexible travel time, and charge state. Finally, a multi-scenario comparative analysis is performed with the help of an improved preference-inspired co-evolutionary algorithm, an optimization algorithm. It is verified that the scheduling method can realize the effective management of loads in the region, reduce the management cost of microgrids and the cost of environmental pollution control, and improve the users' flexible travel time and SOC.