Techno-economic Feasibility of Integrating Hybrid-battery Hydrogen Energy Storage in Academic Buildings

Green hydrogen production and storage are vital in mitigating carbon emissions and sustainable transition. However, the high investment cost and management requirements are the bottleneck of utilizing hybrid hydrogen-based systems in microgrids. Given the necessity of cost-effective and optimal design of these systems, the present study examines techno-economic feasibility of integrating hybrid hydrogen-based systems into an outdoor test facility. With this perspective, several solar-driven hybrid scenarios are introduced at two energy storage levels, namely the battery and hydrogen energy storage systems, including the high-pressure gaseous hydrogen and metal hydride storage tanks. Dynamic simulations are carried out to address subtle interactions in components of the hybrid system by establishing a TRNSYS model coupled to a Fortran code simulating the metal hydride storage system. The OpenStudio-EnergyPlus plugin is used to simulate the building load, validate against experimental data, according to the measured data and monitored operating conditions. Aimed at enabling efficient integration of energy storage systems, a techno-enviro-economic optimization algorithm is developed to simultaneously minimize the levelized cost of the electricity and maximize the CO2 mitigation in each proposed hybrid scenario. The results indicate that integrating the gaseous hydrogen and metal hydride storages into the photovoltaic-alone scenario enhances 22.6% and 14.4% of the annual renewable factor. Accordingly, the inclusion of battery system to these hybrid scenarios gives a 30.4% and 20.3 % boost to the renewable factor value, respectively. Although the inclusion of battery energy storage into the hybrid systems increases the renewable factor, the results imply that it reduces the hydrogen production rate via electrolysis. The optimized values of the levelized cost of electricity and CO2 emission for different scenarios vary in the range of 0.376–0.789 $/kWh and 6.57–9.75 ton, respectively. The multi-criteria optimizations improve the levelized cost of electricity and CO2 emission by up to 46.2% and 11.3%, with respect to their preliminary design.