Federica Gugole

Dynamic effects in gas grids influence the measurement of the quantity and energy supplied to and withdrawn from the grid. These dynamics arise in gas grids due to the supply and withdrawal of gas from the grid. The magnitude of these effects increase due to the injection of, e.g., biomethane and hydrogen. Said dynamic effects add to measurement uncertainty associated with the quantities used in the metering of gas. In this work, it is argued that generally, the measurements in gas grids can be modelled as time series. The data in such time series may or may not show autocorrelation, which can be assessed by time series analysis. In this work, it is shown how the outputs from a time series analysis can be combined with the uncertainty budgets of the measured quantities to comprehensively describe the dependencies between the measurement results used for obtaining the mass, volume or energy supplied to or withdrawn from the grid. Enriching the measurement model used for propagating the measurement uncertainty is the key to integrating the various uncertainty contributions. For temperature and volume measurement, it is demonstrated how measurement models can be constructed to deal with autocorrelation and instrumental uncertainty. From the model development, it follows that the dependencies between the data in the time series have a different cause than the dependencies arising from, e.g. using the same instrumentation, so they should be combined rather than perceived as double counting.

The fiscal metering of natural gas is often performed using a flow meter and a gas chromatograph. The flow meter measures the volume flow rate of the gas and the gas chromatograph measures the composition, from which the calorific value is calculated. The energy is then calculated as the product of the volume and the calorific value. The uncertainty of the energy delivered (or received) is an important parameter for operating the gas grid. Currently, the energy values used to compute the total energy are considered mutually independent. However, the underlying processes affecting the gas flow through a pipe are continuous. We analysed the correlation between successive measurements due to temporal variations in the physical process by applying autoregressive moving average (ARMA) models to a set of measurement data taken at a metering station in the Dutch gas grid. The analysis showed that the volume time series can be modelled as an autoregressive model of order 1, while the time series of the calorific value can be described by an autoregressive model of order 2. These correlations lead to an increase of the instrumental measurement uncertainty associated to the total energy of about 50%.

The total quantity and energy delivered through a gas grid is calculated using simple formulæ that sum the increments measured at regular time intervals. These calculations are described in international standards (e.g., ISO 15112 and EN 1776) and guidelines (e.g., OIML R140). These guidelines recommend that the associated measurement uncertainty is evaluated assuming the measurement results to be mutually independent. This assumption leads to the underestimation of the measurement uncertainty. To address the growing concern among transmission and distribution system operators, the underlying assumptions of these uncertainty evaluations are revisited and reworked to be more adequate. The dependence of measurement results coming from, e.g., the same flow meter and gas chromatograph will be assessed for correlations, as well as other effects, such as the effect of the chosen mathematical approximation of the totalisation integral and fluctuations in the flow rate and gas quality. In this paper, an outline is given for improvements that can be implemented in the measurement models to render them more responsive to the error structure of the measurement data, temporal effects in these data, and the fluctuations in the gas quality and gas quantity. By impact assessment using a simple scenario involving the injection of (renewable) hydrogen into a natural gas grid, it is shown that these improvements lead to a substantive difference. This preliminary work demonstrates that correlations occur both in the instrumental measurement uncertainty and due to temporal effects in the gas grid. To obtain a fit-for-purpose uncertainty budget for custody transfer and grid balancing, it is key to enhance the current models and standards accordingly.

The total quantity and energy delivered through a gas grid is calculated using simple formulas that sum the increments measured at regular time intervals. These calculations are described in international standards (e.g., ISO 15112 and EN 1776) and guidelines (e.g., OIML R140). Currently, in the evaluation of the associated measurement uncertainty, the measurement results that enter into the calculation are assumed to be mutually independent. This assumption leads to underrating of the measurement uncertainty. There is a growing concern among transmission and distribution system operators that this assumption and the obtained values for the measurement uncertainty are not fit for purpose when fluctuations in gas quantity and quality increase, which occurs when injecting renewable energy gases such as hydrogen and biomethane. In a project in the European Partnership for Metrology programme, "Metrology for the hydrogen supply chain", the underlying assumptions of these uncertainty evaluations are revisited and reworked to be more adequate. The dependence of measurement results coming from, e.g., the same flow meter and gas chromatograph will be assessed for correlations, as well as other effects, such as the effect of the chosen mathematical approximation of the totalisation integral, and fluctuations in flow rate and gas quality. % The poster presentation gives an impression of the models being developed, the first findings and the magnitudes of the effects concerned. In this paper, an outline is given for the improvements that can be made in the measurement models to make them more responsive to the error structure of the measurement data, temporal effects in these data, and the fluctuations in gas quality and gas quantity.

Multi-scale systems, such as the climate system, the atmosphere, and the ocean, are hard to understand and predict due to their intrinsic nonlinearities and chaotic behavior. Here, we apply a physics-consistent machine learning method, the multi-resolution dynamic mode decomposition (mrDMD), to oceanographic data. mrDMD allows a systematic decomposition of high-dimensional data sets into time-scale dependent modes of variability. We find that mrDMD is able to systematically decompose sea surface temperature and sea surface height fields into dynamically meaningful patterns on different time scales. In particular, we find that mrDMD is able to identify varying annual cycle modes and is able to extract El Nino–Southern Oscillation events as transient phenomena. mrDMD is also able to extract propagating meanders related to the intensity and position of the Gulf Stream and Kuroshio currents. While mrDMD systematically identifies mean state changes similarly well compared to other methods, such as empirical orthogonal function decomposition, it also provides information about the dynamically propagating eddy component of the flow. Furthermore, these dynamical modes can also become progressively less important as time progresses in a specific time period, making them also state dependent.

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies. Author summary Many countries are currently dealing with the COVID-19 epidemic and are looking for an exit strategy such that life in society can return to normal. For that purpose computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before putting them into practice. These models are subject to uncertainties (due to, for instance, limited knowledge of the parameter values), which can lead to a large variability in model predictions. It is therefore fundamental to assess which range of values a model produces for key indicators of the severity of the epidemic. We present here the results of the uncertainty and sensitivity analysis of four exit strategies simulated with an individual-based model of the COVID-19 transmission. As key indicators of the severity of the pandemic we consider the maximum number of cases in intensive care and the total number of intensive care patient-days in excess. Our results show the crucial role of the intervention uptake by the population, of the reduction in the level of transmission by intervention and of the capability to trace infected individuals.