Exergoeconomic and multi-objective optimization of a solar system for Hydrogen production by the Particle Swarm Algorithm

Due to the rapid depletion of fossil energy resources and the environmental pollutions caused by consumption of fossil fuels, many countries have started developing renewable energy systems and finding alternative energy resources. One of the best resources of renewable energy is solar energy, which is clean and carbon-free. Solar-based energy systems can be designed in a way to produce hydrogen energy in order to supply the global energy demand, and reducing the environmental effects caused by global warming. In this study, a solar-based integrated hybrid system is considered to generate hydrogen. The system takes advantage of a flat plate collector, an organic Rankine cycle (ORC) and a PEM electrolyzer to convert renewable solar energy into electricity and hydrolyze water to hydrogen gas. To determine the optimum parameters and evaluate their effects on performance of the system, a parametric study is conducted. Outlet temperature of generator, inlet temperature of ORC turbine, irradiation intensity, water mass flow rate of the collector, and collector surface area are considered as the five decision variables. To optimize the design parameters, a multiobjective optimization is performed through the multi-objective particle swarm algorithm. The optimization results indicate that exergy efficiency of the system can increase from 1 to 3.5% meanwhile the total cost of the system can increase from 21 to 28 $/h, at optimum conditions. According to the findings, extending the collector’s surface area can lead to increasing the overall cost of the system, whilst reducing the exergy efficiency. It can also be stated that the collector component contributes to the total cost of the system noticeably.