A novel hybrid Harris hawk optimization-sine cosine algorithm for the influence of electric vehicle and renewable energy sources in power system distribution network

The stochastic behavior of renewable energy sources (RES) and electric vehicle (EV) loads can compromise power system reliability by affecting power quality, increasing losses, and causing voltage deviations. Mitigating these impacts requires optimal planning that accounts for RES variability and additional EV charging demand. This paper presents a novel approach for the simultaneous optimal allocation and sizing of RES and EV charging stations, along with coordinated management of the charging process. A multi-objective optimization problem is formulated to minimize power losses, voltage fluctuations, charging and supply costs, and EV battery costs. The model incorporates coefficients based on wind speed, solar radiation, and hourly peak demand to manage EV charging during off-peak hours. A hybrid Harris Hawk Optimization–Sine Cosine Algorithm (HHO-SCA) is proposed to develop a meta-heuristic-based RES and EVs charging system. By integrating SCA features into HHO, the algorithm enhances exploration and exploitation capabilities, thereby improving global search efficiency and optimizing energy usage. The performance of HHO-SCA has been validated on benchmark functions. The HHO-SCA is employed to solve the proposed optimization problem across five scenarios. Its effectiveness for simultaneous optimal siting and sizing of RES and EV charging stations is validated on the IEEE 33 and 69 bus system. Results indicate that HHO-SCA outperforms other methods by effectively avoiding local optima and exhibiting superior convergence behavior.