Reliability assessment of distribution system: A case study of 33 kV Chandragiri utility feeders integrated with PV and electric vehicle

The growing integration of electric vehicles (EVs), renewable energy sources, and variable load profiles has introduced significant uncertainty and operational complexity into modern electrical distribution networks (EDNs). To address these challenges, this paper presents an optimal network reconfiguration (NR) framework that incorporates photovoltaic (PV) generation under EV loading and contingency scenarios. The optimization employs an Improved Mayfly Optimization Algorithm (IMOA), enhanced with a median-based position update mechanism and a nonlinear gravity coefficient to improve exploration–exploitation balance and convergence speed. The proposed approach is initially validated on the IEEE 33-bus test system, where the IMOA-optimized switching configuration achieves substantial reductions in power losses, minimizes voltage deviations, and improves system reliability indices relative to conventional configurations. To demonstrate real-world applicability, the method is further evaluated using 7 years (2017–2023) of actual outage data from two operational 33 kV feeders—Chandragiri and Agarala, located in Chandragiri, Andhra Pradesh, India. Reconfiguration of these feeders under both normal and fault conditions yields measurable improvements in reliability metrics and voltage profiles, confirming the robustness of the strategy in practical operating environments. The findings demonstrate that IMOA-based NR, integrated with PV and EV systems, provides a scalable and effective solution for enhancing the reliability, resilience, and operational efficiency of contemporary EDNs.