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

Ke Liu, Hui He, Xiang Liao (), Fuyi Zou, Wei Huang and Chaoshun Li
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Ke Liu: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Hui He: Changjiang Engineering Group, Wuhan 430010, China
Xiang Liao: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Fuyi Zou: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Wei Huang: Hubei Energy Group New Energy Development Co., Wuhan 430077, China
Chaoshun Li: School of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan 430074, China

Sustainability, 2025, vol. 17, issue 7, 1-33

Abstract: 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.

Keywords: EVIES; MSCSO; mutation-dominated selection strategy; multi-objective optimization model; Renewable energy integration (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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