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Green hydrogen technologies are crucial for decarbonization purposes, while cogeneration offers efficient heat and power generation. Integrating green hydrogen and cogeneration brings numerous benefits, optimizing energy utilization, reducing emissions, and supporting the transition to a sustainable future. While there are numerous studies examining the integration of combined heat and power with Power to Gas, certain aspects still requires a more detailed analysis, especially for internal combustion engines fuelled by natural gas, due to their widespread adoption as one of the primary technologies in use. Therefore, this paper presents a comprehensive numerical 0-D dynamic simulation model, implemented within the TRNSYS environment, considering internal combustion engines fuelled by natural gas. Specifically, the study focuses on capturing CO2 from exhaust gases and producing green hydrogen from electrolysis. Based on these considerations, two configurations are proposed: the first involves the methanation reaction, while the second entails the production of a hydromethane mixture. The aim is to evaluate the technical and economic feasibility of these configurations and compare their performance within the Power to Gas framework. Self-sufficiency from the national electricity grid has been almost achieved for the two configurations, considering an industrial case. The production of hydromethane allows smaller photovoltaic plant (81 kWp) compared to the production of synthetic methane (670 kWp) where a high quantity of hydrogen is required, especially if all the carbon dioxide captured is used in the methanation process. Encouraging economic results with payback times below ten years have been obtained with the use of hydromethane. Moreover, hydromethane shows potential residential applications, with small required photovoltaic sizes.

In this paper the implementation and application of a novel methodology for the estimation of the energy demand of the railway building stock is presented. To this aim, a bottom-up modelling approach implemented in a simulation tool is developed to assess the energy footprint and potential savings of railway buildings. The tool is intended to support operators and decision-makers in the planning of systematic energy retrofit necessary to up to date the railway infrastructure.The developed methodology is applied to the Italian railway building stock with a bottom-up approach, identifying several groups of similar stations (archetypes) that are clustered according to real data collected. Afterwards, a data-driven model is derived from the detailed dynamic simulations of physic-based models representing the whole building heritage. As a demonstration of the validity of the proposed methodology and its capability to be exploited in real applications, some energy-saving strategies are simulated, and a comprehensive analysis is conducted on the considered stations.The surrogate data-driven model shows R2 coefficients always above 0.93 compared to physic-based model in predicting heating, cooling and electricity demand. Depending on the size of the stations, the mean relative error is in the range 5.9–15.0%. Furthermore, the surrogate model turns out to be an easy-to-use tool to analyse retrofit scenarios and take informed decisions, while the methodology is easily extensible and scalable to other contexts.As demonstrated, the most impactful measure among the ones investigated is the adoption of high-performance lighting systems which entail an overall primary energy saving up to 26%, with very low pay back periods (∼1 year).