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This study aims to develop an electricity pricing and multi-objective optimization strategy that can be applied to integrated electric vehicle charging stations (IEVCS) that include photovoltaic (PV) systems and a range of multiple energy storage options. The strategy balances the interests of both commercial aggregators and end users, thereby enhancing the grid’s operational efficiency. The model incorporates temperature variations that affect the PV output, energy storage capacity, conversion efficiency, and EV charging demand, all of which improve numerical accuracy. A new pricing algorithm based on peak-valley differences is proposed that considers the impact of EV penetration and temperature fluctuations. By combining the effects of supercapacitors and lithium batteries, the approach therefore utilizes both power and energy densities. Using Multi-Objective Particle Swarm Optimization (MOPSO), the strategy thereby incorporates three main objectives; minimizing grid load fluctuations, maximizing aggregator profits, and reducing user costs. Simulation results show that the proposed model improves grid resilience, enhances economic benefits for both aggregators and users, and optimizes the system’s performance. The findings from this study provide valuable insights that lead to a better understanding of the sustainable development and the intelligent operation of PV-storage EV charging stations, and this then leads to more efficient and resilient power grids.
Yan et al. (Wed,) studied this question.