The ongoing digitalization of energy infrastructure is a crucial enabler for improving efficiency, reliability, and sustainability in gas distribution networks, especially in the context of decarbonization and the integration of alternative energy carriers (e.g., renewable gases including biogas, green hydrogen). This study presents the development and application of a Digital Twin framework for a real-world gas distribution network developed using open-source tools. The proposed methodology covers the entire digital lifecycle: from data acquisition through smart meters and GIS mapping, to 3D modelling and simulation using tools such as QGIS, FreeCAD, and GasNetSim. Consumption data are collected, processed, and harmonized via Python-based workflows, hourly simulations of network operation, including pressure, flow rate, and gas quality indicators like the Wobbe Index. Results demonstrate the effectiveness of the Digital Twin in accurately replicating real network behavior and supporting scenario analyses for the introduction of greener energy vectors such as hydrogen or biomethane. The case study highlights the flexibility and transparency of the workflow, as well as the critical importance of data quality and availability. The framework provides a robust basis for advanced network management, optimization, and planning, offering practical tools to support the energy transition in the gas sector.
Digital Twin Framework for Energy Transition in Gas Networks Based on Open-Source Tools: Methodology and Case Study in Southern Italy
Baby B. A.;Ferraro M.;
2025
Abstract
The ongoing digitalization of energy infrastructure is a crucial enabler for improving efficiency, reliability, and sustainability in gas distribution networks, especially in the context of decarbonization and the integration of alternative energy carriers (e.g., renewable gases including biogas, green hydrogen). This study presents the development and application of a Digital Twin framework for a real-world gas distribution network developed using open-source tools. The proposed methodology covers the entire digital lifecycle: from data acquisition through smart meters and GIS mapping, to 3D modelling and simulation using tools such as QGIS, FreeCAD, and GasNetSim. Consumption data are collected, processed, and harmonized via Python-based workflows, hourly simulations of network operation, including pressure, flow rate, and gas quality indicators like the Wobbe Index. Results demonstrate the effectiveness of the Digital Twin in accurately replicating real network behavior and supporting scenario analyses for the introduction of greener energy vectors such as hydrogen or biomethane. The case study highlights the flexibility and transparency of the workflow, as well as the critical importance of data quality and availability. The framework provides a robust basis for advanced network management, optimization, and planning, offering practical tools to support the energy transition in the gas sector.| File | Dimensione | Formato | |
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