Thermoeconomic analysis and artificial neural network based genetic algorithm optimization of geothermal and solar energy assisted hydrogen and power generation

The study aims to optimize the geothermal and solar-assisted sustainable energy and hydrogen production system by considering the genetic algorithm. The study will be useful by integrating hydrogen as an energy storage unit to bring sustainability to smart grid systems. Using the Artificial Neural Network (ANN) based Genetic Algorithm (GA) optimization technique in the study will ensure that the system is constantly studied in the most suitable under different climatic and operating conditions, including unit product cost and the plant's power output. The water temperature of the Afyon Geothermal Power Plant varies between 70 and 130 degrees C, and its mass flow rate varies between 70 and 150 kg/s. In addition, the solar radiation varies between 300 and 1000 W/m(2) for different periods. The net power generated from the region's geothermal and solar energy-supported system is calculated as 2900 kW. If all of this produced power is used for hydrogen production in the electrolysis unit, 0.0185 kg/s hydrogen can be produced. The results indicated that the overall energy and exergy efficiencies of the integrated system are 4.97% and 16.0%, respectively. The cost of electricity generated in the combined geothermal and solar power plant is 0.027 $/kWh if the electricity is directly supplied to the grid and used. The optimized cost of hydrogen produced using the electricity produced in geothermal and solar power plants in the electrolysis unit is calculated as 1.576 $/kg H-2. The optimized unit cost of electricity produced due to hydrogen in the fuel cell is calculated as 0.091 $/kWh. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study aims to optimize geothermal and solar-assisted sustainable energy and hydrogen production system using genetic algorithm. The integration of hydrogen as an energy storage unit brings sustainability to smart grid systems. The study uses artificial neural network (ANN) based genetic algorithm (GA) optimization technique to constantly study the system in the most suitable conditions, including unit product cost and power output. The overall energy and exergy efficiencies of the integrated system are found to be 4.97% and 16.0% respectively.