Improved PICEA-g-based multi-objective optimization scheduling method for distribution network with large-scale electric vehicles

Large-scale electric vehicle access to the distribution grid for charging can affect the security and economic operation of the grid. In this paper, an optimal scheduling method for large-scale EV access to the distribution grid based on the improved preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) is proposed. 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, multi-scenario simulation analysis is performed to verify the effectiveness of the proposed control strategy and optimization algorithm. The experimental results show that the improved PICEA-g algorithm outperforms the remaining several algorithms when the size of electric vehicles is larger than 50. And based on this method, it realizes the effective management of loads in the region, and reduces the management cost of microgrids and the cost of environmental pollution control, and ithe users' flexible travel time and state of charge.