ArticleScientific reports2025
A fuzzy logic based energy management model for solar PV-wind standalone with battery storage system.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Innovative fuzzy reinforcement learning based energy management for smart homes through optimization of renewable energy resources with starfish optimization algorithm.Scientific reports · 2026Article
- Optimal sizing and rule-based management of hybrid microgrids using SSA for rural electrification.Scientific reports · 2025Article
- Intelligent demand-side energy management via optimized ANFIS-gene expression programming in hybrid renewable-grid systems.Scientific reports · 2025Article
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Authors and funding
4 authors.
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Abstract
Access to reliable electricity is essential for delivering quality healthcare. However, off-grid health facilities in rural regions like Kalangala, Uganda, often face persistent power outages and high operational costs due to dependence on diesel generators. This study proposes a fuzzy logic-based energy management system (FLC-EMS) to optimize power flow in a hybrid renewable energy system (HRES) combining solar photovoltaics (PV), wind turbines (WT), and battery storage. The system was modeled in MATLAB/Simulink, using 27 fuzzy IF-THEN rules and triangular membership functions to manage four switching ports that prioritize renewable energy based on real-time load demand, renewable availability, and battery state-of-charge (SOC). Simulation results showed that the FLC-EMS ensured continuous power supply during peak demand periods (e.g., 9:00 AM and 7:00 PM) by dynamically balancing solar, wind, and battery inputs. The optimized PV-WT-BAT configuration achieved a Levelized Cost of Electricity (LCOE) of $0.281 and a Net Present Cost (NPC) of $269,246 over a 20-year period. Compared to diesel-based systems, it reduced operational costs by 11.87-18.7% and significantly lowered carbon emissions. The proposed FLC-EMS demonstrates strong potential to improve energy reliability and cost-effectiveness for off-grid healthcare facilities. Its adaptability to variable loads and intermittent renewable sources makes it a scalable solution for sustainable rural electrification. Future research will focus on real-world implementation and enhancing predictive control through machine learning.
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