Transient multi-objective optimization of solar and fuel cell power generation systems with hydrogen storage for peak-shaving applications

Abstract

The present article introduces an innovative solution to improve performance efficiency while shaving the demand during peak hours. The idea focuses on efficient gas turbine and Rankine cycle hybridization for power and hydrogen production based on high-temperature heliostat solar towers. The proposed model is simulated via TRNSYS software to assess the performance transiently and then optimized through the artificial neural network in MATLAB program to minimize the calculation time. The key economic, environmental, and energy indicators are evaluated, compared, and optimized for this. According to the effect of main design parameters, including gas turbine inlet temperature, mass flow rate, and heliostat area, on the system's indicators, there is a conflictive trend among the power productivity, cost, and emission index, signifying the optimization significance. The optimization results show that the net power, efficiency, cost, and CO2 emissions are 298,300 GJ, 30.7%, 3.6 M$, and 20,760 tonnes under optimal conditions. This working condition is obtained by electing turbine inlet temperature, solar area, and airflow rate of 1087 °C, 91,200 m2, and 68,817, respectively. At this condition, while 71.18% of the yearly primary energy is provided by the sun, biomass meets the rest, showing the importance of renewable combination to achieve the highest independence from the grid. Finally, the system can generate an additional 585 kg/day of hydrogen that could be used in relevant industries.