Computational Intelligence Techniques for Secured Unit Commitment in Microgrid Systems in the Presence of Renewable Energy Sources
Authors: *Gwaivangmin, B.I., Bakare, G.A., Haruna, Y.S. And Amoo, A.L.
DOI Info: http://doi.org/10.5281/zenodo.15778224
ABSTRACT
This study addresses the critical challenge of optimizing unit commitment in a university microgrid, located in a region with unreliable power infrastructure, by employing computational intelligence techniques. The research compares the performance of Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and hybrid approaches (HPSO-SA and HPSO-GA) to minimize the operational costs of a microgrid incorporating solar, pumped hydro energy storage (PHESS), diesel generators, and public grid supply. By considering real-world data and addressing uncertainties in renewable energy generation and demand, this study aims to develop cost-effective and reliable power generation schedules. The results demonstrate that the hybrid PSO-SA algorithm achieved the lowest total operational cost (N1,246,765.58), indicating its superior performance in optimizing unit commitment for the university microgrid.
Affiliations: Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria.
Keywords: Computational Intelligence Genetic Algorithm Hybrid Secured Simulated Annealing Unit Commitment
Published date: 2025/06/30
