Grid-connected desalination plant economic management powered by renewable resources utilizing Niching Chimp Optimization and hunger game search algorithms

This study presents a novel Hunger Game Search and Niching Chimp Optimization Algorithms (HGS-NChOA) for optimizing grid-connected desalination plants powered by renewable energy. The primary innovation of this study is the significant advantages provided by the HGS-NChOA method, particularly in terms of reducing the cost of freshwater production and mitigating greenhouse gas emissions. Key outcomes reveal the HGS-NChOA's superiority in reducing freshwater production costs and greenhouse gas emissions. Notably, the desalination unit capacity decreased from 10.4 m³ to 8.5 m³, with a cost reduction of 0.223 $/m³ in the PV-battery storage-wind turbine system. Experimental results show a 59% and 49% decrease in computation time for the PV-battery and PV-hydrogen systems, respectively. Sensitivity analysis highlights the significant impact of solar irradiation on investment costs. Overall, HGS-NChOA demonstrates enhanced efficiency and economic viability in managing grid-connected, renewable energy-powered desalination facilities. Sensitivity analysis showed that solar radiation has a more significant impact on investment costs compared to wind speed, with hourly solar radiation fluctuations affecting water production costs by 17.09% to 19.56%. Additionally, the study indicates that integrating a diesel generator into the system can further reduce costs and greenhouse gas emissions, proving HGS-NChOA's versatility in optimizing hybrid energy systems. Statistical analysis using metrics like Inverted Generational Distance (IGD) and Maximum Spread (MaxS) demonstrated the proposed method's superior convergence and diversity compared to well-known multi-objective algorithms like MOPSO, MOEA/D, and MOGWO-PSO.