Optimizing energy-efficient grid performance: integrating electric vehicles, DSTATCOM, and renewable sources using the Hippopotamus Optimization Algorithm

The rapid increase in renewable energy integration and electric vehicle (EV) adoption creates significant challenges for the stability and efficiency of power distribution networks. This study addresses the need for optimized placement and sizing of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) systems, and Distribution Static Compensators (DSTATCOMs) to enhance grid performance. The motivation for this work arises from the fluctuating nature of renewable energy generation and the unpredictable demands of EV charging, which strain existing infrastructure. To address these challenges, we propose a novel optimization framework that introduces the Renewable Distributed Generation Hosting Factor (RDG-HF) and Electric Vehicle Hosting Factor (EV-HF) as key metrics. These metrics, combined with the Hippopotamus Optimization Algorithm (HO), enable strategic planning within the IEEE 69-bus system. Simulation results demonstrate that the integrated placement of EVCSs, PVs, and DSTATCOMs reduces power losses by up to 31.5% and reactive power losses by up to 29.2%. An economic analysis further reveals payback periods ranging from 2.7 to 10.4 years and potential profits of up to $1,052,365 over 25 years. These findings highlight the importance of optimized integration in improving both technical performance and long-term economic benefits for distribution networks.