RO desalination powered by renewable energy sources: Optimized sizing with pumped storage hydropower system

The fundamental human right to access clean water underscores the significance of technologies like reverse osmosis (RO) in rendering seawater potable. However, the substantial energy requirements associated with RO systems pose a significant operational cost challenge. Renewable energy sources (RES), such as wind and photovoltaic (PV), when combined with hybrid systems, have the potential to significantly reduce the energy costs of RO operations. This paper explores the optimal design of a hybrid power system combining solar panels, wind turbines, and pumped storage hydropower (PSH). The system aims to power a small-scale RO unit using the Differential Evolution (DE) algorithm, tailored to the local weather data of Dhahran, Saudi Arabia. To enhance the economic feasibility of the system, this study compares two scenarios for utilizing the surplus energy: selling the surplus energy to the grid and using it for additional desalination. The findings reveal that, for an RO load demand of 5m3/day, the optimal hybrid system configuration consists of 6.8 kW PV, 3 kW wind turbines, and an upper reservoir volume of 230.7 cubic meters at a 50-meter height, resulting in a required energy capacity of 26.4 kWh. This configuration yields a Levelized Cost of Energy (LCOE) of $0.428/kWh in Dhahran without unutilized surplus energy. Selling this surplus energy (SE) to the grid results in a cost of water (COW) of $2.26- $2.44/m3. However, utilizing the SE for more desalination lowers the COW to $1.85-$1.97/m3. For a larger RO unit (24 m3/day), the LCOE is $0.408/kWh, the COW ranges from $1.91 to $2.09/m3 when the SE is sold back to the grid and the COW ranges from $1.44/m3 to $1.62/m3 when the SE is utilized for more desalination. These findings underscore the cost-effectiveness of the proposed solutions when compared to existing literature. Furthermore, the study explores the impact of varying RO capacity on both LCOE and COW, shedding light on the scalability and efficiency of the proposed hybrid system.