Multi-Objective Optimization of RES and EV Charging Station Placement for Loss Minimization and Voltage Profile Enhancement in Distribution Systems

This paper addresses the challenges posed by the unpredictable nature of renewable energy sources (RES) and electric vehicle (EV) loads, which can impact power system reliability through issues like power quality degradation, increased losses, and voltage instability. To mitigate these effects, it proposes an innovative method for the combined optimal placement and sizing of RES and EV charging stations, along with a coordinated charging management strategy. The approach uses a multi-objective optimization framework aimed at minimizing power losses, voltage fluctuations, costs related to charging and energy supply, as well as EV battery expenses. The model incorporates factors such as wind speed, solar radiation, and hourly peak demand, to encourage EV charging during off-peak periods, thereby improving system efficiency and stability. The paper proposes a hybrid metaheuristic algorithm called Harris Hawk Optimization–Sine Cosine Algorithm (HHO-SCA) for optimizing renewable energy sources (RES) and electric vehicle (EV) charging systems. By integrating features of the Sine Cosine Algorithm (SCA) into Harris Hawk Optimization (HHO), the hybrid algorithm enhances both exploration and exploitation capabilities, leading to improved global search efficiency and optimized energy use. The HHO-SCA's performance was validated using benchmark functions and then applied to solve the proposed optimization problem under five different scenarios. Its effectiveness for the simultaneous optimal siting and sizing of RES and EV charging stations was demonstrated on the IEEE 33-bus system. Results show that HHO-SCA outperforms other methods by effectively avoiding local optima and achieving superior convergence behavior.