Utilizing the multi-objective particle swarm optimization for designing a renewable multiple energy system on the basis of the parabolic trough solar collector

Today, to preserve fossil resources, mankind has to search for new ways to respond to its ever-increasing energy needs. In this study, a hybrid system of energy and the use of a parabolic trough solar collector to attract solar radiation was investigated to produce clean electricity, cooling, and hydrogen from thermodynamic and economic aspects. The designed system consisted of a parabolic trough solar collector, organic Rankine cycle, lithium-bromide absorption refrigeration cycle, and proton exchange membrane electrolysis system. The evaporator input temperature, turbine inlet temperature, solar radiation intensity, mass flow rate of collector and parabolic trough collector surface area were set as decision variables and the effect of these parameters on system performance and system exergy loss were investigated. The objective functions of this research were exergy efficiency and cost rate. In order to optimize this system, multi-objective particle swarm optimization algorithm was employed. Optimization results with particle swarm optimization indicated that the best rate of exergy efficiency is 3.12% and the system cost rate is 16.367 US$ per hour, at the same time. The system is capable of producing 15.385 kW power, 0.189 kg/day hydrogen and 56.145 kW cooling in its optimum condition. The results of sensitivity analysis showed that increasing mass flow rate at the collector, temperature at the evaporator inlet, and temperature at the turbine inlet have positive effect on the performance of the proposed system. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study investigated a hybrid energy system utilizing a parabolic trough solar collector to produce clean energy, focusing on thermodynamic and economic aspects. The system was optimized using a multi-objective particle swarm optimization algorithm, resulting in the identification of optimal performance parameters and outcomes.