Optimization of Renewable Energy Sharing for Electric Vehicle Integrated Energy Stations and High-Rise Buildings Considering Economic and Environmental Factors

Amid the rapid growth of the new energy vehicle industry and the accelerating global shift toward green and low-carbon energy alternatives, this paper develops a multi-objective optimization model for an Electric Vehicle Integrated Energy Station (EVIES) and a high-rise building wind-solar-storage sharing system. The model aims to maximize the daily economic revenue of the EVIES, minimize the load variance on the grid side of the building, and reduce overall carbon emissions. To solve this multi-objective optimization problem, a Multi-Objective Sand Cat Swarm Optimization Algorithm (MSCSO) based on a mutation-dominated selection strategy is proposed. Benchmark tests confirm the significant performance advantages of MSCSO in both solution quality and stability, achieving the optimal mean and minimum variance in 73% of the test cases. Further comparative analyses validate the effectiveness of the proposed system, showing that the optimized configuration increases daily economic revenue by 26.54% on average and reduces carbon emissions by 37.59%. Additionally, post-optimization analysis reveals a smoother load curve after grid integration, a significantly reduced peak-to-valley difference, and improved overall operational stability.