Optimizing power and energy loss reduction in distribution systems with RDGs, DSVCs and EVCS under different weather scenarios

Electric power grids are increasingly vulnerable to disruptions from extreme weather events, resulting in prolonged outages. The rise of electric vehicles (EVs) offers benefits like improved sustainability and reduced maintenance but also introduces challenges such as voltage instability and higher power losses when integrated into radial distribution systems (RDS). This study proposes an approach that integrates electric vehicle charging stations (EVCSs), distribution static VAR compensators (DSVCs), and renewable energy sources (RESs) like solar and wind into RDS, supporting both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes to enhance flexibility and resilience. The study aims to reduce power losses under normal conditions and minimize energy not delivered (END) during fault conditions, evaluated under different weather scenarios. The spotted hyena optimizer algorithm (SHOA) and genetic algorithm (GA) are employed to optimize RDG, DSVC, and EVCS locations and capacities. Tests on the IEEE 34-bus RDS show SHOA achieves a 25 % reduction in power losses, improving system resilience and outperforming GA in both power and energy loss reduction.