A multi-objective optimization framework for EV-integrated distribution grids using the hiking optimization algorithm

Electric vehicle (EV) integration into distribution grids introduces significant challenges in maintaining grid stability, minimizing operational costs, and ensuring overall system efficiency. In response to these challenges, a novel multi-objective optimization model is proposed that concurrently minimizes energy losses, energy procurement costs, load shedding, and voltage deviations over a 24-hour period, while also accounting for the operational costs associated with EV and battery management. The model is optimized using the Hiking Optimization Algorithm (HOA), which leverages an adaptive search mechanism based on Tobler's Hiking Function. This mechanism enhances the exploration of the solution space and effectively avoids local optima, resulting in superior performance compared to conventional methods. Simulation results on a 33-bus distribution grid demonstrated that, with EV integration, operational costs were reduced by 19.3%, energy losses decreased by 59.7%, load shedding was minimized by 75.4%, and voltage deviations improved by 43.5% relative to a scenario without EVs. Additionally, the model eliminated photovoltaic (PV) curtailment, thereby ensuring optimal utilization of renewable energy resources. When benchmarked against alternative optimization techniques, the HOA achieved a 4.4% lower total cost than the Komodo Mlipir Algorithm (KMA) and reduced energy losses by 24.5% compared to Particle Swarm Optimization (PSO). These results clearly demonstrate the model's effectiveness in enhancing grid stability, optimizing costs, and improving operational efficiency. The proposed approach offers a scalable and reliable solution for modern grid management in the context of increasing EV penetration and renewable energy integration.