Reliability evaluation of solar integrated power distribution systems using an Evolutionary Swarm Algorithm

The reliability of solar-integrated power distribution systems is significantly affected by intermittent solar generation and its impact on feeder voltages. While existing adequacy studies account for intermittency, they frequently overlook feeder voltages due to the computational burden of the Alternating Current Optimal Power Flow (AC-OPF) analysis. Addressing this gap, we propose an efficient framework based on an Evolutionary Swarm Algorithm (ESA) to integrate AC-OPF analysis into the reliability evaluation of power distribution systems. The sampling mechanism of ESA reduces the application of time-consuming AC-OPF and allows the fast estimation of reliability indices. The performance of the proposed framework is compared with Sequential Monte Carlo Simulation (SMCS), classical meta-heuristics, and three state-of-the-art meta-heuristics. Results demonstrate that our proposed framework can estimate the reliability indices approximately 34 times faster than SMCS without sacrificing accuracy. Furthermore, the ESA outperforms classical and state-of-the-art methods by over 23% in event sampling efficiency. Friedman and Nemenyi post-hoc tests conclude that ESA's results significantly differ from others. We utilize the proposed framework in a case study to analyze the influence of solar photovoltaic integration on distribution system reliability. Another case study investigates the impact of dynamic tap changing of power transformers on the reliability improvement of distribution systems.