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<p>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.</p>

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A fuzzy logic based energy management model for solar PV-wind standalone with battery storage system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月9日

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  • Nayebare Alfred,
  • Venkataramana Guntreddi,
  • Aliyu Nuhu Shuaibu,
  • Mutiu Shola Bakare

摘要

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.