Net-zero Energy Management through Multi-criteria Optimizations of a Hybrid Solar-Hydrogen Energy Production System for an Outdoor Laboratory in Toronto

Hydrogen production and storage in hybrid systems is a promising solution for sustainable energy transition, decoupling the energy generation from its end use and boosting the deployment of renewable energy. Nonetheless, the optimal and cost-effective design of hybrid hydrogen-based systems is crucial to tackle existing limitations in diffusion of these systems. The present study explores net-zero energy management via a multi-objective optimization algorithm for an outdoor test facility equipped with a hydrogen-based hybrid energy production system. Aimed at enabling efficient integration of hydrogen fuel cell system, the proposed solution attempts to maximize the renewable factor (RF) and carbon mitigation in the hybrid system as well as to minimize the grid dependency and the life cycle cost (LCC) of the system. In this context, the techno-enviroeconomic optimization of the hybrid system is conducted by employing a statistical approach to identify optimal design variables and conflictive objective functions. To examine interactions in components of the hybrid system, a series of dynamic simulations are carried out by developing a TRNSYS code coupled with the OpenStudio/EnergyPlus plugin. The obtained results indicate a striking disparity in the monthly RF values as well as the hydrogen production rate, and therefore in the level of grid dependency. It is shown that the difference in LCC between optimization scenarios suggested by design of experiments could reach $15,780, corresponding to 57% of the mean initial cost. The LCOE value yielded for optimum scenarios varies between 0.389 and 0.537 $/kWh. The scenario with net-zero target demonstrates the lowest LCOE value and the highest carbon mitigation, i.e., 828 kg CO2/yr, with respect to the grid supply case. However, the LCC in this scenario exceeds $57,370, which is the highest among all optimum scenarios. Furthermore, it was revealed that the lowest RF in optimal scenarios is equal to 66.2% and belongs to the most economical solution.