Publication

Abstract : Distribution systems heavily employ Distributed Generators (DG) to decrease losses. Some writers have explored this crucial optimization problem with a constant power load model. Consumers in distribution network load centres utilize varied loads based on voltage. A few authors have investigated this optimization problem with voltage-dependent load models. Some studies used a single load model independent of voltage or depending on bus system voltage. Few studies have employed a single load model on the whole feeder, lateral or sub-lateral, to analyze all voltage-dependent load models. A mixture of voltage-dependent load models calculates consumer load on a bus or all buses in actual distribution networks. Combining all voltage-dependent load models at one or more buses has yet to be fully researched. This study examines mixed load model effects in radial distribution networks with DG. DG position and capacity must be optimized to decrease power losses, a tough, nondifferentiable, non-convex combinatorial task. A quantum-inspired evolutionary metaheuristic algorithm solves this combinatorial optimisation challenge. A quantum-inspired evolutionary algorithm (AQiEA) determines DG placement and capacity. The suggested technique is evaluated on two IEEE benchmark test bus systems. Tabulated findings show that AQiEA reduces power loss the most.