Optimizing solar PV systems using fuzzy logic for Climate-Resilient Healthcare infrastructure in Kyrgyzstan

Stable power infrastructure and access to electricity for hospital and clinic infrastructures remains a challenge in most rural and climatically sensitive areas. Though photovoltaic (PV) modules are commonly used for renewable energy generation, conventional methods are generally based on fixed tilt angles or high mathematical modeling techniques. They often do not consider varying weather conditions as well as uncertainties in the surrounding environment and therefore have poor energy capture efficiency and higher operational nonlinearities. To fill this gap, this study develops an intelligent MPPT algorithm that applies the FLC. FLC was chosen because of its ability to control systems having nonlinearities and adverse operating environment without necessarily requiring robust computational power. These tilt angles are proposed for seasonal adjustment to ensure high efficiency, more importantly for the healthcare facilities in Naryn area in Kyrgyzstan that strongly depends on stable power sources. Simulation data also show that the FLC-based model has 20% more power compared with fixed-angle system and approximately 15% compared with traditional MPPT technique. Also, the proposed scheme showed 3% prediction error when checked with the PVWatts calculator. Moreover, the proposed system avoids large computational complexity and miniaturization, which makes it more realistic in practice. Besides contributing to the MPPT optimization field, this research also helps in meeting the energy requirement of healthcare facilities present in remote locations. The results fall under SDG 3 — Good Health and Well-being and SDG 13 — Climate Action, highlighting the benefits of using intelligent solar PV systems to create climate adaptive health facilities.