Optimal Scheduling of EV Charging Stations with Integrated Renewable Resources Using the Mantis Search Algorithm

The transportation sector is rapidly transitioning toward carbon neutrality through the large-scale deployment of Electric Vehicles (EVs), which necessitates efficient and sustainable Charging Station (CS) infrastructures. This study develops a comprehensive CS model that integrates Renewable Energy Sources (RESs), Energy Storage Systems (ESSs), and the utility grid to meet EV demands while enhancing energy flexibility and sustainability. A novel Mantis Search Algorithm (MSA) is employed for operational scheduling with a profit-maximization objective. The MSA features a robust exploitation operator that improves convergence and effectively avoids local optima in unimodal optimization problems. Extensive evaluations were conducted across multiple scenarios: CSs without ESS, with ESS without export capability, and the proposed integrated model. The results demonstrate that the comprehensive configuration improves system performance by 10.35% and 21.99% compared to the two baseline cases. Furthermore, the proposed MSA demonstrates significantly better performance than other compared methods, highlighting its effectiveness in optimizing energy management in CSs. These results demonstrate the promising potential of the MSA-based day-ahead schedule to support resilient, cost-effective, and environmentally sustainable CSs.