Multi-Objective Optimization for Solar-Hydrogen-Battery-Integrated Electric Vehicle Charging Stations with Energy Exchange

The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach.

Automated summary

This paper presents an optimization methodology for direct energy exchange between multiple electric vehicle charging stations in London. The objective is to minimize costs and greenhouse gas emissions, and two multi-objective optimization algorithms are employed for evaluation. The findings indicate that the Non-dominated Sorting Genetic Algorithm achieves higher-quality solutions. The optimized charging stations result in substantial cost savings.