OPTIMAL SITING AND SIZING OF DISTRIBUTED RENEWABLE GENERATORS IN IEEE-69 BUS RADIAL DISTRIBUTION SYSTEM: A STRATEGIC APPROACH

Abstract

Growing global concern for sustainable and reliable electricity supply has intensified the deployment of renewable distributed generation (RDG) resources - particularly solar photovoltaic (PV) and wind-based systems - within radial distribution networks. Determining the most suitable locations and capacities of these generators, however, presents a challenging multi-objective optimization task. This research study develops a Quantum-Inspired Particle Swarm Optimization–Gravitational Search Algorithm (QPSOGSA) framework to minimize active power losses, operational cost, and voltage instability simultaneously in the standard IEEE-69 bus radial distribution system. The proposed strategy proceeds in two stages: first, potential RDG installation buses are identified using loss-sensitivity analysis; second, the QPSOGSA algorithm determines the optimal sizing and placement of RDG units at those sites. System losses and voltage performance are evaluated through a Backward/Forward Sweep power-flow model, and the algorithm’s outcomes are compared with other metaheuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFOA), and Simulated Annealing (SA).Simulation studies in MATLAB demonstrate that the hybrid QPSOGSA significantly improves network efficiency - achieving lower real-power losses, superior voltage profiles, and reduced operating costs - than the benchmark algorithms. The results indicate that this quantum-inspired hybrid approach offers a robust and scalable tool for DG planning, enhancing both the sustainability and reliability of contemporary power-distribution networks.