Performance analysis of solar-assisted-geothermal combined cooling, heating, and power (CCHP) systems incorporated with a hydrogen generation subsystem
In this study, the response surface method (RSM) and transient assessment was used to evaluate the energy and economic performance of a solar-assisted-geothermal combined cooling, heating, and power system (SG-CCHP). The proposed SG-CCHP process consisted of two steam turbines (STs), photovoltaic/thermal (PV/T) collectors, a fuel cell circuit, an absorption chiller, and a heat pump (HP), with battery cells and a hydrogen storage container as the power storage modules.
The system’s performance was investigated using the TRNSYS modeling tool. The design of experiments (DOE) approach was used to determine the optimal arrangement of the SG-CCHP scheme by controlling the key design factors. A number of simulation scenarios were generated using DOE, and their outcomes were analyzed using RSM. The transient interactions of the controlling design factors on the techno-economic metrics were given after RSM identified the optimal SG-CCHP scheme. The number of PV/T panels, steam turbine capacity, fuel cell power, HP heating capacity and absorption chiller cooling capacity were considered as decision variables. Total electricity consumption (TEU) and auxiliary boiler fuel consumption (ABFU) as indicators of primary energy consumption of the system and predicted average vote (PMV) as an index of thermal comfort of the system and life cycle cost (LCC) as an economic measure to 4 objective functions were selected for optimization. The results indicated that the optimal system
significantly reduced its annual life cycle costs, thermal comfort score, total power usage, and auxiliary boiler natural gas usage. The findings also demonstrated that the SG-CCHP system’s integration of battery and hydrogen storage components achieved the maximum efficiencies of 90%, 60%, 23%, and 18% for the electrolyzer, fuel cell, PV/T solar collector, and electrical generator, respectively over a year. The optimization results showed that the system cycle cost.