Integrated solar energy-energy storage system for an electricity-freshwater multigeneration configuration: Exergo-economic assessment with multi-objective particle swarm optimization
Abstract
This study evaluates an integrated solar energy-energy storage system comprising organic Rankine cycle with open feed heater (ORC-OFH), ejector refrigeration cycle with ORC (ERC-ORC), and reverse osmosis (RO) subsystems aimed at enhancing energy efficiency and freshwater production. Utilizing energy and mass balance equations for system modeling, an exergo-economic assessment was performed to analyze payback period and net present value (NPV). Multi-objective particle swarm optimization (MOPSO) was utilized to optimize exergy efficiency and minimize payback time, focusing on the effects of vapor generator temperatures, mass fractions, and ejector primary pressure ratio on system performance. Key findings indicate that the solar framework experienced the maximum exergy destruction (86 %), with ERC-ORC (8 %), ORC-OFH (4 %), and RO (2 %) contributing less. Increasing vapor generator temperatures improved total power generation and exergy efficiency while reducing cooling loads, resulting in a reduced payback period from 5.97 to 5.68 years. Moreover, variations in working fluid mass fractions revealed complex interdependencies affecting exergetic performance and payback period. The implementation of the MOPSO algorithm optimized system parameters, achieving 6.58 % exergetic efficiency and a 5.11-year payback period. The NPV analysis across three pricing scenarios demonstrated that price fluctuations significantly impacted system viability, with a 50 % price reduction resulting in a 57.41 % decrease in NPV and a 49.27 % increase in payback period, while increased prices declined the payback period to 4.79 years. This research underscores the importance of optimizing integrated solar energy systems for enhanced performance and economic feasibility.
Author
Rebwar Nasir Dara
DOI
https://doi.org/10.1016/j.est.2025.116750
ISSN
2352-152X
Publish Date: 2025-04-22