REAL-TIME LOAD ALLOCATION USING GENETIC ALGORITHM IN A SMART 33KV FEEDER: A NIGERIAN CASE STUDY
The Nigerian 33 kV distribution network continues to experience chronic transformer overloading, inequitable load allocation, and high technical losses due to weak-grid conditions and fluctuating power supply. This study aims to develop a real-time, adaptive load allocation framework capable of improving feeder-level performance under these constraints. A Genetic Algorithm (GA) driven optimization model was designed and integrated with feeder priority indices, demand-response logic, and real-time load profiling. The system was implemented in MATLAB and validated using operational data from an actual Nigerian 33 kV feeder. Results show that the GA framework significantly improves allocation fairness, transformer loading profiles, and loss minimization, outperforming conventional static allocation schemes. The approach also maintains higher load satisfaction levels under severe power deficits. It is recommended that distribution utilities adopt GA enabled intelligent optimization tools for real-time feeder management, as the validated framework presented here offers a scalable decision-support mechanism for smart-grid deployment in developing power systems.
Authors : Adaira, A.S., Evbogbai, M.J.E. and Amhenrior, H.E.
Category : Open Access Volume (Issue) : 11(2) Date Uploaded : 8th December 2025