Electric Vehicles Charging and Discharging Optimization Based on Sand Cat Swarm Optimization (SCSO) Algorithm

This paper proposes a strategy for optimizing the charging and discharging of electric vehicles (EVs) using the Sand Cat Swarm Optimization (SCSO) algorithm, aiming to address the peak pressure on the grid caused by the increasing adoption of EVs. First, a Monte Carlo method is employed to construct a travel probability model for EVs, capturing key data such as charging duration, driving distance, and remaining battery power. Next, an optimization scheduling model for EV charging and discharging is established, incorporating a dynamic pricing mechanism. During the development stage of the SCSO algorithm, a roulette wheel strategy is introduced to update search angles, reducing the likelihood of local optima and enhancing optimization performance. Finally, the improved SCSO algorithm is applied to solve the multi-objective function, which aims to minimize grid load fluctuations, reduce user charging costs, and maximize user charging volume. The results demonstrate that the proposed strategy exhibits superior convergence speed and optimization capability, effectively mitigating peak-valley disparities in grid load and reducing residential charging costs, thereby significantly improving the grid's peak-shaving capacity.