ANN-assisted optimization of a solar-to-X system for green hydrogen production, CO2 capture, and methanol-based energy storage

This study introduces a novel system to facilitate renewable energy usage, provide efficient energy storage, and guarantee consistent power and fuel generation by utilizing solar energy as the principal source, methane as a supplementary fuel, and methanol as an energy carrier. This study's primary innovation is using methanol synthesis as a storage method, wherein surplus solar-generated hydrogen is transformed into methanol during periods of high solar availability and subsequently employed as fuel when solar radiation diminishes. This method guarantees a consistent and controllable energy supply, enhancing system reliability and sustainability. The artificial neural network-based genetic algorithm method is applied to find optimal energy production/usage/storage. Then, the dynamic performance evaluation is comprehensively conducted for Xi'an, China. The findings underscore the trade-offs between efficiency, cost, and emissions, emphasizing the significance of optimization. The optimal configuration results in a notable enhancement in net power (↑475 kW) and exergy efficiency (↑6.6 %), accompanied by decreased costs (↓129 USD/h). Methanol production peaks in the summer, facilitating seasonal energy storage and reducing the effects of diminished solar availability in winter. The CO2 emissions are inversely correlated with solar intensity, highlighting the environmental benefits of solar-based methanol production and use. The findings underscore the effectiveness of the hybrid solar-methanol system, facilitating enhanced integration of renewable energy and reliable dispatchability.