Intelligent energy-aware control of a single-stage photovoltaic-battery powered brackish water reverse osmosis desalination system

The integration of renewable energy into brackish water reverse osmosis (BWRO) desalination systems is essential for addressing water scarcity in off-grid and arid regions. However, the intermittent nature of solar energy poses significant challenges to continuous and efficient operation. This paper presents the design, modeling, and validation of an Energy-Aware Control (EAC) framework for a single-stage photovoltaic (PV)/battery-driven BWRO system, which regulates production according to available energy under fluctuating environmental condition. The proposed framework combines advanced power electronics with optimized control strategies to maximize energy utilization, enhance battery management, and improve desalination performance. A novel Maximum Power Point Tracking (MPPT) algorithm based on Harris Hawk Optimization enhanced with Directionally Adaptive Lévy Flight (DALF-HHO) is developed, achieving 99.9% tracking efficiency with superior accuracy, faster convergence, and greater robustness compared to conventional methods such as Grey Wolf Optimizer (GWO) and standard HHO. Complementing this, a Model-Free Adaptive Control (MFAC) strategy is implemented to regulate bidirectional energy flow through a Bidirectional Buck–Boost Quadratic Converter (BBQC), achieving precise DC-link voltage regulation with minimal ripple (±0.625%, ±0.05 V peak-to-peak) and consistently outperforming Fractional-Order PID (FOPID) and Sliding Mode Control (SMC) under dynamic conditions. By integrating DALF-HHO and MFAC within the EAC framework, the system maintains permeate salinity at a constant 5.2 g/m³ while optimizing energy efficiency and ensuring resilient desalination. The overall architecture is designed, modeled, and validated in MATLAB/Simulink, with further verification through Hardware-in-the-Loop (HIL) experiments on a dSPACE DS1104 platform, demonstrating robust real-time performance under variable environmental conditions.