Enhancing Energy Distribution Efficiency with Dynamic Transformer Rotation in 11kV Networks

Abstract

This study presents the optimisation of transformer-to-feeder load allocation within an 11kV distribution network to improve energy distribution efficiency, minimise transformer overload, and enhance system balance. The analysis utilised over five years of energy consumption data for three 11kV feeders: Auchi Town, Igbei Road, and GRA, supported by five transformers of varying capacities (30 MVA, 15 MVA, and 40 MVA units), with the largest introduced in 2021. A simulation framework using MATLAB and a Genetic Algorithm (GA) was developed to dynamically optimise transformer assignments based on feeder demand, transformer capacity, and operational constraints. Performance evaluation focused on the energy loss proxy, transformer overload occurrence, and maximum per-unit loading. The optimised configuration introduced reassignment, leading to balanced capacity utilisation. The GA reached an optimal solution zone rapidly, stabilising by the 61st generation with a consistent best fitness value of 1012.03, suggesting the methodology is both robust and practical. Transformer overloads were completely avoided in both baseline and optimised allocations, confirming assignments remained within rated limits. Loss proxy values under the optimised configuration increased slightly, reflecting broader transformer engagement, while maximum per-unit loading values remained safely below critical thresholds. Compared to the baseline's fixed pairings, the optimised structure provided improved asset utilisation and greater operational flexibility. The recommendations include integrating the model with existing SCADA systems for real-time deployment, incorporating operational switching costs into the optimisation function, and exploring scalability for larger networks to transition this research into a fully deployable power management solution.