Data-driven optimization and economic study of a biomass-fueled power plant combined with vanadium-chloride thermochemical cycle, electrochemical hydrogen compressor, and thermal desalination for efficient hydrogen production/storage

Integrating renewable energy sources into energy markets depends on effectively aligning production with demand, mainly by deploying large-scale energy storage programs. Among the available options, hydrogen storage stands out as a robust alternative. Focusing on challenges related to hydrogen production and storage, this research suggests a multigeneration system, utilizing an innovative energy storage/conversion process planned to simultaneously provide electric power, freshwater, and hydrogen. This arrangement integrates several vital subsystems, encompassing a gasification unit, an externally-fired gas turbine cycle, a thermochemical cycle, a humidification-dehumidification desalination unit, and an electrochemical hydrogen compressor. Comprehensive parametric studies analyze the system's applicability from thermodynamic, environmental, and cost perspectives. Besides, a data-driven multi-objective optimization relying on artificial neural networks and a genetic algorithm is employed to optimize three critical goals associated with the system: exergetic efficiency, CO2 emissions, and levelized cost of energy. The results indicate their optimal values at 44.5 %, 40.98 $/MWh, and 0.5656 kg/kWh, correspondingly. Subsequently, the optimal operational conditions also exhibit the net electrical power of 314.9 kW, freshwater output 5142 L/day, and hydrogen output of 102.38 kg/day. Further analysis exposes that the gasifier and combustion chamber are the primary sources of exergy destruction, accounting for more than 69.1 % of total exergy destruction.