Design of a Smart Grid Load Management System in MATLAB Using Hybrid Optimization Methods

A. S. Adaira, M. J. E. Evbogbai, H. E. Amhenrior

Abstract


The increasing mismatch between electricity demand and available supply in modern distribution networks has intensified the need for intelligent and automated load management strategies. This study presents the development of a MATLAB App Designer-based Smart Grid Load Management System that integrates hybrid optimization techniques to ensure efficient, fair, and priority-sensitive power allocation across multiple load centers. The system combines Genetic Algorithm (GA) optimization with auxiliary heuristic and rule-based repair operators to handle conflicting constraints, maintain critical-load floors, and minimize supply and demand imbalance. A graphical user interface (GUI) was developed to provide operators with real-time capabilities, including adjustable priority indices, algorithm selection (GA, PSO, hybrid GA-greedy), demand specification, and automated report generation. The framework was validated using an 18-load-center dataset representing a typical urban 33 kV distribution feeder, with total demand exceeding available supply under several stress scenarios. Results show that the hybrid optimization approach achieved faster convergence, improved load satisfaction levels, and superior critical-load preservation compared with standalone GA or conventional load-shedding methods. The system’s performance was further verified through ETAP-based feasibility checks, confirming its operational viability for real-time deployment. Overall, the developed platform provides a scalable, operator-friendly, and optimization-driven solution for modern smart grid load management. 


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