Modeling and multiobjective optimization of a solar-powered reverse osmosis desalination system with hydrogen energy storage

Abstract

Remote communities often face challenges in accessing clean water, crucial for improving their quality of life and health. To address this issue, this work focuses on optimizing the design of a hybrid renewable energy system, integrating photovoltaic (PV) and hydrogen storage to power a reverse osmosis desalination (ROD) system. A novel multiobjective optimization model, implemented as a mixed-integer linear program, is proposed to minimize exergy losses and annual cycle costs, ensuring optimal system performance. The optimized decision variables include the sizing and power allocation of the hybrid energy system, and the operation of the ROD system based on water demand. Operational characteristics such as energy balance, system capacity, and daily water demand are incorporated into the model as constraints. The flexibility of the model allows for site-specific parameters, tailoring solutions to meet the needs of remote communities. The optimization model is tested in this work for two case studies, revealing significant cost-effectiveness disparities. In the first community, the system achieves a levelized cost of water (LCW) and exergy efficiency of 2.119 USD/m³ and 11.48 % for an 18 m³ daily water demand, compared to 3.757 USD/m³ and 8.79 % for a 3 m³ daily demand in the second community. This highlights the economic viability and higher efficiency of such a system for large-scale applications, achieving up to a 16.11 % lower LCW than other studies. Additionally, it demonstrates the flexibility of the proposed optimization model and provides a comprehensive evaluation of hybrid energy systems for remote communities.