Optimization of Hybrid Geothermal–Solar Power Plant Based on Advanced Exergy Analysis Using Genetic and Water Cycle Algorithm

In recent years, human endeavors have been increased to optimally produce clean energy from renewable sources to preserve non-renewable resources and reduce environmental pollution. Economic and environmental analysis based on advanced exergy is a good way to examine the strengths and weaknesses of power generation systems. This paper used advanced exergy analysis to optimize the exergy efficiency of two systems, i.e. standalone geothermal and a hybrid geothermal-solar system. Three-objective optimization was performed by considering twelve decision variables of genetic algorithm and water cycle algorithm. The results of advanced exergy analysis showed that the condenser had the highest avoidable exergy degradation. In the hybrid geothermal-solar cycle, the solar collector became unavoidable in terms of exergy degradation. Exergy degradation of the standalone geothermal cycle was mostly endogenous (78.53%), the maximum avoidable exergy in this cycle was for the ORC evaporator (91.68%). Advanced economic exergy analysis in the hybrid cycle showed that the steam evaporator had the main cost of purchasing equipment in the system. For all components studied, the endogenous cost rate was higher than the exogenous part, indicating a weak relationship between them. The results of genetic algorithms and the water cycle algorithm are very close to each other. In optimization by genetic algorithm, the exergy efficiency of the system has been increased by 1.22%. System costs dropped by 22.49%. The system's environmental impact rate has been dropped from 204.53 mPh to 142.87 mPh. Also, optimization by the water cycle algorithm has increased the exergy efficiency by 1.13% and reduced costs by 21.97%.