Multi-objective optimization control of uncertain electric vehicle clusters participating in distribution substation supply assurance

To address the widening peak-to-valley difference and imbalanced charging station utilization caused by disorderly charging of large-scale electric vehicles, this paper proposes an energy-optimized charging station allocation method based on the Ant Colony Optimization (ACO) algorithm. First, a multi-objective optimization model incorporating SOC, power constraints, and time-of-use electricity pricing is constructed in the Python environment. This model aims to minimize daily operational costs of charging stations, user waiting time penalties, and the mean square deviation of grid load. Second, an adaptive pheromone decay factor and a heuristic dynamic adjustment strategy are introduced into the classical ant colony algorithm. This enhances global search capabilities and convergence speed, effectively achieving spatiotemporal balance of charging loads and economical system operation.