Adaptive Energy Management System for Reverse Osmosis Desalination: Modelling Fuzzy Logic Control for Hybrid Solar PV, Battery and Grid Systems

The demand for fresh water in coastal regions has increased the energy demand for desalination systems. This study models a hybrid solar PV, battery, and grid energy system for reverse osmosis desalination under realistic solar conditions in Mombasa, Kenya. An adaptive system that responds to real-time irradiance data is modelled in MATLAB R2025a using a five-year hourly solar irradiance dataset from NASA POWER, an internet database with renewable energy datasets. The analysis done determines the control and economic efficiency of using fuzzy logic control that prioritizes solar PV power, battery, and grid and ensures seamless transitions between energy sources. A well-tuned PID controller is compared with the fuzzy controller. The results show that the fuzzy logic hybrid system outperformed the PID controller, achieving near-autonomous reliance on solar PV and battery energy. Solar PV power provided most of the energy to power reverse osmosis, while the stored charge was effectively used during periods of low solar irradiance. The attained specific energy consumption for the reverse osmosis desalination process was 5.82 kWh/m3, which substantially minimized grid energy use, hence reducing operational costs. The findings highlight the importance of intelligent control in optimizing hybrid energy in desalination applications, offering a viable pathway to low-carbon, sustainable freshwater production in regions with high solar irradiance.