Techno-economic analysis and optimization of green hydrogen production and liquefaction with metal hydride storage

A techno-economic analysis and three-objective optimization are reported for a system that uses a renewable energy source (RES) for hydrogen production and liquefaction in which all the produced hydrogen enters the liquefaction cycle. The system works in three phases; hydrogen liquefaction, hydrogen storage and fuel cell power generation. Every second, the mass of produced hydrogen from the electrolyzer is calculated and immediately injected into the liquefaction cycle. This happens for 3600 s during each hour and for 12 h during the day. To reduce the execution time and increase the accuracy, six different artificial neural network (ANN) models have been used, which are optimized using genetic algorithm (GA). Decision variables for optimization include solar irradiation intensity, ambient temperature, heat exchanger efficiency, and isothermal compressor output pressure. The objective functions are chosen to be total exergy efficiency, liquefaction efficiency, and total cost rate, and the optimal values of the objective functions are found to be 19.3%, 19.8%, and 10.6 $/hr, respectively. The objective functions are illustrated in terms of daily time, as well as decision variables. Also, the effect of liquefaction efficiency and specific work consumption on liquefaction cycle parameters and electrolyzer temperature have been investigated. The results show that in the range of compressor output pressure between 5 and 15 MPa, for every n % increase in heat exchanger efficiency, the liquefaction efficiency increases by (n/2) % and the amount of liquid hydrogen production at steady-state becomes 0.162 kg/h. It is also found that if the storage system is turned off and all the produced power is given to the liquefaction system, the mass of liquefied hydrogen will increase by 17% at the end of the day.