Optimal reconfiguration of photovoltaic/wind-based distributed generation systems: A techno-economic analysis using AHP–GA

Abstract

Renewable distributed generation (RDG), notably solar and wind energy, is increasingly integrated into distribution networks (DNs) to enhance sustainability, meet rising demand, and improve grid reliability. This study proposes a coordinated planning framework that jointly optimizes RDG siting/sizing and DN expansion using a hybrid Analytic Hierarchy Process–genetic algorithm (AHP–GA). Multi-objective goals—minimizing active power losses, reliability-related costs, and annual equipment investment—are scalarized using AHP-derived weights and solved through a chromosome-segmentation GA that reduces the search space and accelerates convergence. The method was validated on a modified IEEE 37-node radial distribution system, demonstrating substantial techno-economic benefits: total cost reductions exceeding 25%, approximately 75% reduction in active power losses, and about 40% improvement in reliability indices. A comparative assessment of the IEEE 69-bus system further underscored the approach’s efficiency, achieving an active power loss of 16.88 kW (a 92.50% reduction) and 112.67 s of CPU time, outperforming alternative metaheuristics. After the integration of DGs, both reliability indices improved significantly, resulting in reductions of 40.27% in System Average Interruption Duration Index (SAIDI) and 58.84% in Energy Not Served (ENS) annually. The results indicate that coordinated RDG–DN planning yields DNs that are more economical, reliable, and operationally robust than those from sequential or uncoordinated strategies.