Optimal charging of electric vehicles in smart stations and its effects on the distribution network using meerkat optimization algorithm

The proliferation of electric vehicles (EVs) presents a significant challenge and opportunity for the energy sector. This study proposes a novel approach for optimizing EV charging within smart stations, considering its impact on the distribution network. Leveraging the Meerkat Optimization Algorithm (MOA), we address the complex optimization problem of balancing EV charging demands with grid constraints. Navigating distribution grid energy management complexities, including renewable resources and dynamic demand, is challenging. We introduce a sophisticated optimization model tailored for grid operations, featuring meticulous formulations for energy management. The model optimizes battery usage, EV energy management, compensator utilization, and distributed generation dispatch. Through extensive simulations, we demonstrate the approach's effectiveness in minimizing charging costs, reducing grid congestion, and enhancing overall system performance. The multiobjective function minimizes energy losses, power purchases, load curtailment, distributed generation, and battery/EV expenses over 24 h. Simulations validate a significant reduction in the distribution grid's operating cost. This research highlights the potential of advanced optimization techniques in smart charging infrastructure to facilitate widespread EV adoption while ensuring grid reliability and efficiency. Incorporating electric vehicles (EVs) into the system yields significant improvements across performance indicators compared to scenarios without EVs. Results indicate a 19% reduction in the objective function value, with a notable 74% decrease in energy purchase and a 60 % reduction in energy losses. Additionally, load shedding decreases by approximately 75 %, while voltage deviation decreases by around 44 %. Importantly, no PV or WD curtailment is observed with EV integration, showcasing its compatibility with renewable energy generation profiles and emphasizing its potential to enhance system efficiency, reliability, and sustainability.