The transition from fossil fuels to renewable fuels is reflected in an increase in the production and use of biogas, biomethane and hydrogen from renewable sources. To increase the production of these renewable energy gases, their use should increase too, which calls for transmission and distribution systems that are ready to receive and supply them. To an extent, current natural gas infrastructure is used for this purpose alongside the construction of distribution systems for hydrogen only. The transmission system operators (TSOs) and distribution system operators (DSOs) also see the geometry of their grids change, in particular with respect to the number of points where gas enters their grids.
The use of a greater diversity of gaseous fuels also creates larger fluctuations in the properties measured for billing and custody transfer. These larger fluctuations are caused by a greater diversity of gaseous fuels transmitted and distributed through gas grids. Another cause lies in the fact that the production of biomethane and hydrogen from renewable sources usually takes place at a smaller scale than the production of natural gas, which leads to a more distributed supply of these renewable energy gases, leading to a larger number of entry points.
A concern from TSOs and DSOs is that current practices, such as those described in ISO 15112, EN 1776 and OIML R140 no longer suffice to evaluate in a reliable manner fiscal metering data, including the evaluation of measurement uncertainty. Said documents consider the measurement results that enter into the calculation of total volume, mass or energy as mutually independent. Failing to address these dependencies severely underestimates the uncertainty calculated for total quantity and total energy.
This best practice guide describes novel methods for the evaluation of measurement uncertainty along the supply chain, namely, the measurement of total quantity, energy and impurity content of hydrogen and hydrogen blends. These methods address the correlations arising due to the use of the same instrumentation for the measurement result, temporal (serial correlation) effects, and effects arising from the use of time averages. These novel methods find their root in the methods of the Guide to the Expression of Uncertainty in Measurement (GUM).
Whereas the framework of the GUM is deemed to be generally applicable, it is only so for measurement models where there are no constraints between the input quantities. Thus, for measurement models that have as input the composition of the fuel gas, the framework was extended to provide appropriate sensitivity coefficients for using the law of propagation of uncertainty (LPU). The Monte Carlo method (MCM), which can also be used for propagating measurement uncertainty, is less affected by this issue if the constraint on the amount fractions
forming the composition is duly respected. The guidance is aimed at specialists in gas metering, members of standardisation committees and developers of software to support fiscal metering, billing, custody transfer and gas allocation.
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