Quantum binary particle swarm optimization for optimal on-load tap changing and power loss reduction

Abstract

Over time, there has been a continuous surge in the demand for electrical energy, necessitating the development of larger and more intricate electrical power networks. These extensive networks pose a significant challenge, primarily in the form of considerable loss of electrical energy, which, if not effectively addressed, may lead to persistent and imperceptible losses. In response to this challenge, this research proposes the application of quantum binary particle swarm optimization (QBPSO) for the coordinated management of on-load tap changers (OLTC) in loaded transformers within a distribution network, with a specific emphasis on reducing power losses. The experimental results demonstrate that the implementation of QBPSO results in a reduction of power loss from 21.756107 kW to 19.157321 kW and an increase in the average voltage from 19.00467941 kV to 19.93068 kV in a 20 kV 34-bus distribution network. This has the potential to significantly enhance overall system efficiency.