Optimization based on the cost, energy, and environmental approaches of a solar-geo system: using real solar data of ParsaAbad-e-Moghan

This study addresses a key knowledge gap in renewable energy research: the limited optimization of hybrid solar-geothermal systems using comprehensive decision variables. Novel aspects of this work include the use of seven independent variables, such as vapor generator inlet temperature, geothermal well temperature, separator pressure, incident angle, water-to-air mass ratio, evaporator temperature, and inlet pressure of vapor turbine, enabling a nuanced optimization that surpasses the scope of previous studies. To achieve this, the study employs a combination of response surface methodology, artificial neural networks, and genetic algorithms to predict and optimize system performance across thermal efficiency, released carbon dioxide rate, and levelized cost of plant. Numerically, the optimized system achieves a thermal efficiency of 37.53% and an exergy efficiency reduction of 13.06%, while the levelized cost of the plant rises to 20.86 $/GJ. As a practical case study, real solar data from ParsaAbad-e-Moghan demonstrate the model’s viability and environmental impact reduction potential in high solar-radiance areas. Therefore, carbon dioxide emission rate is minimized in August, reaching 197 tons/year due to ideal solar conditions. Finally, June records the highest thermal efficiency of 37.75%, coinciding with the peak solar irradiance and favorable summer weather conditions.