The project has received funding from the European Partnership on Metrology, co-financed by European Union Horizon Europe Research and Innovation Programme and from the Participating States.
About the project
Partial replacement of natural gas with renewable alternatives is essential to mitigate climate change. There is an urgent need to fast forward the clean transition by upscaling biomethane and hydrogen utilisation within the European infrastructure. However, the introduction of these renewable gases in gas grids gives rise to increasing fluctuations in gas flow rates and properties.
Priorities in this transition include dealing with safety aspects, gas allocation and fair billing. Gas grids will have more points where gas is fed in, leading to greater dynamics in supply and demand, as well as in gas properties.
Knowing measurement uncertainty in gas grids is key to assessing and managing risks regarding safety, economy and the supply of gas within the applicable specifications. Met4H2 (see website and Zenodo repository) started to investigate the dependency of fiscal metering measurements, since these are not mutually independent, due to changes in the grid. MetroPEMS also revealed that quantification of totalised volume or mass in the presence of dynamic flow changes is challenging. Therefore, it is essential to adapt the current evaluation of measurement uncertainty to the conditions of the future gas network.
The SmartGasNet project will provide the necessary methods, algorithms, models, datasets and good practice guides to enable gas grid operators to adapt their data processing for fiscal metering, custody transfer and billing. This measurement infrastructure for gas supply will support the envisaged increase in the use of renewable energy gases.
The objectives
The specific objectives of the project are:
To create datasets for time-resolved gas flow measurements, including temperature, pressure and gas composition using state-of-the-art methods and techniques, mimicking changes in gas grid flow rate, pressure, temperature and gas composition typically seen in real world scenarios in order to enable studying and modelling of time-correlation effects. (WP1).
To develop methods for the evaluation of time-correlations in gas metering data, as well as uncertainty evaluation for time averages of gas quantity and calorific value, and link these with models used to operate gas grids. The methods should be applicable to grids for hydrogen, natural gas and hydrogen-enriched natural gas (enriched to the most common level), and fully validated (WP2).
To assess the possibility of integrating machine learning (ML), as a type of artificial intelligence (AI), in smart metering and monitoring systems used in gas grids with respect to its applicability to produce measurement results that meet the requirements of ISO/IEC 17025, OIML R140 and ISO 15112, and ISO/IEC Guide 98 (WP3).
To develop and validate an integrated package of methods for the evaluation of measurement data to support gas allocation and the fiscal metering of the most commonly used blends of natural gas with renewable gases (e.g., hydrogen and biomethane) as well as renewable hydrogen (WP4).
To facilitate the take up of the technology and measurement infrastructure developed in the project by the measurement supply chain (GERG and MARCOGAZ), standards developing organisations (ISO/TC 193, ISO/TC 197, CEN/TC 234, EURAMET TC-F and TC-MC), legal metrology organisations (WELMEC, OIML) and end users (e.g., gas network operators) (WP5).