Optimization framework for energy management systems in renewable energy-integrated EV charging stations

The rapid adoption of electric vehicles (EVs) is accelerating the shift to clean energy and sustainable transport. But high and often unpredictable energy consumption is a challenge for the stability of power grids due to the growing demand for EV charging. An intelligent Energy Management System (EMS) is required to solve this problem, and it should be able to optimize the energy flow along with the integration of renewable energy sources. This paper proposes an advanced EMS framework for a renewable energy-based Electric Vehicle Charging Station (EVCS) to minimize the operational cost and improve the energy efficiency. The proposed system evaluates the EVCS performance considering seasonal variability (Solar and wind fluctuations) and non-seasonal conditions for robust and adaptive functioning. The Modified Competitive Swarm Optimization (MCSO) algorithm is used to optimally schedule the EV arrival and charging pattern, and its performance is compared with the existing algorithms, such as Particle Swarm Optimization (PSO) and Competitive Swarm Optimization (CSO). Economic feasibility is assessed by analysing key metrics such as Levelized Cost of Energy (LCOE) and payback period. The results demonstrate that the proposed EMS can effectively decrease the operating cost, enhance the utilization of renewable energy, and reduce the dependence on the conventional grid under various environmental conditions. Overall, this work offers a comprehensive and scalable solution for next-generation EV charging infrastructure, enabling cost-effective, reliable, and sustainable energy management while supporting long-term resilience in modern power systems.