Feasibility of optimum energy use and cost analyses by applying artificial intelligence and genetic optimization methods in geothermal and solar energy-assisted multigeneration systems

This study evaluates the potential for optimizing energy utilization and cost analysis in geothermal and solar energy-supported multigeneration systems using artificial intelligence (AI) and genetic algorithm (GA) optimization methods. Integrating renewable energy sources aims to promote sustainable energy solutions focusing on efficiency and cost-effectiveness. A cogeneration system that leverages geothermal and solar energy for electricity generation and space cooling is analyzed. The model incorporates an organic Rankine cycle (ORC) for power generation and a geothermal-solar absorption cooling cycle for space cooling, developed using Artificial Neural Networks (ANN) and optimized through thermoeconomic methods. Thermodynamic and thermoeconomic analyses guide the optimization process using an ANN-based GA method. The system, with parameters such as a 130 °C geothermal source temperature, an 85 kg/s mass flow rate, and a 600 W/m2 monthly average solar radiation intensity in Afyonkarahisar, achieves overall energy and exergy efficiencies of 19.5 % and 43.5 %, respectively. Optimization via the GA method significantly improves performance, increasing net power output from 2240 kW to 2760 kW and cooling capacity from 2720 kW to 3061 kW. Additionally, the cost of electricity decreases by 12.1 %, from 0.0165 $/kWh to 0.0145 $/kWh, while the cooling cost is reduced by 41.9 %, from 0.0745 $/kWh to 0.0525 $/kWh.