COMPUTATIONAL INTELLIGENCE FOR COST- AND EMISSION-OPTIMIZED UNIT COMMITMENT IN HYBRID MICROGRIDS.
Abstract
This study addresses the challenge of efficient energy management in microgrids facing rising electricity costs and power shortages. The study tackles the limitations of traditional fossil fuels and the intermittency of renewables by developing an optimized unit commitment framework for a hybrid microgrid (Solar PV, PHESS, utility supply, diesel generator) at the University of Jos. Computational intelligence techniques (GA, PSO, SA, PSO-GA, PSO-SA) were implemented in Python, incorporating an electronic synchronizer for stability and aiming to minimize operational costs and emissions. The simulation results revealed that the hybrid PSO-SA algorithm achieved the lowest cost (₦1,246,765.58) and CO₂ emissions (N10695.87), demonstrating the effectiveness of hybrid optimization and the benefits of Solar PV and PHESS in reducing reliance on conventional sources. This study highlights the potential of computational intelligence to enhance microgrid efficiency and suggests future research on algorithm fine-tuning and hybrid approaches for further performance improvements.
Keywords:
Computational intelligence, Hybrid, Intermittency, Microgrids, Optimization, Synchronizer, Unit CommitmentDownloads
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Copyright (c) 2025 B.I Gwaivangmin, G.A Bakare, Y.S Haruna, A.L Amoo , Ali M.T
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