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ON the PHENOMENON of UNACCOUNTED for GAS Baseline Formulation and Error Detection Techniques

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ON the PHENOMENON of UNACCOUNTED for GAS Baseline Formulation and Error Detection Techniques ON THE PHENOMENON OF UNACCOUNTED FOR GAS Baseline formulation and error detection techniques Thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences 2021 Lubomir Botev Supervisor: Dr. Paul Johnson School of Mathematics Contents List of Tables8 List of Figures 11 Abstract 16 Declaration 17 Copyright Statement 18 Acknowledgements 19 1 Introduction 20 1.1 Natural Gas Transportation........................... 22 1.2 The NTS...................................... 24 1.2.1 Operating Conditions........................... 25 1.2.2 Nodes................................... 27 1.2.3 Linepack.................................. 29 1.3 Grid Balancing and Introducing UAG...................... 29 1.4 Defining UAG................................... 32 1.5 Literature Review................................. 34 1.6 Scope....................................... 36 1.7 Contributions................................... 38 2 Sources of Uncertainty 40 2.1 Classifying Errors................................. 42 2.1.1 Random Error............................... 42 2 2.1.2 Systematic Error............................. 42 2.1.3 Decomposing UAG............................ 43 2.2 Atmospheric Emissions.............................. 43 2.3 Metering Errors.................................. 45 2.3.1 The Measurement Chain......................... 45 2.3.2 Random Error............................... 47 2.3.3 Systematic Error............................. 49 2.3.4 A Note on Error Cases.......................... 50 2.3.5 Uncertainty and Bias Drift........................ 53 2.3.6 Estimating Total Measurement Uncertainty.............. 53 2.4 Attributed Measurements............................ 63 2.4.1 Failure Modes............................... 64 2.4.2 Error Estimation at Attributable Sites................. 65 2.5 Linepack...................................... 71 2.5.1 Calculation................................ 71 2.5.2 Temperature................................ 72 2.5.3 Multiphase Flow............................. 76 2.5.4 Linepack Uncertainty........................... 76 2.6 Non-integrated Energy.............................. 78 2.7 Accounting Errors................................. 79 2.8 Closeout Period.................................. 80 2.8.1 The Dataset................................ 81 2.8.2 Analysis.................................. 81 2.8.3 Mitigating Closeout Period Errors.................... 82 2.9 Additional Factors................................ 84 2.9.1 Non-technical Losses (NTL)....................... 84 2.9.2 Conflicting Interests........................... 85 2.9.3 Billing Cycle Discrepancies........................ 85 2.10 Analysis...................................... 86 2.11 Conclusion..................................... 88 3 3 Daily Baseline 89 3.1 Measures of UAG................................. 90 3.1.1 Expressing UAG............................. 90 3.1.2 Aggregation Frequency.......................... 91 3.1.3 Aggregation Function........................... 91 3.2 NTS and UAG Statistical Analysis....................... 94 3.2.1 UAG.................................... 94 3.2.2 UAG Predictors.............................. 101 3.2.3 Exploratory Regression.......................... 106 3.2.4 Nodes................................... 112 3.3 Baseline Model.................................. 116 3.3.1 Uncertainty Based Approach....................... 116 3.3.2 Statistical Approach........................... 118 3.3.3 Aggregate Baseline Model........................ 123 3.3.4 Decision Intervals............................. 124 3.4 Model Performance Results........................... 125 3.4.1 Additional Diagnostics.......................... 127 3.4.2 Baseline Calculation Methodology.................... 130 3.5 Efficacy of Baseline Method........................... 130 3.5.1 Nature of Error Identification...................... 131 3.5.2 Historic Errors.............................. 131 3.5.3 Results................................... 135 3.5.4 Extreme Values.............................. 136 3.5.5 Analytic Performance Estimation.................... 136 3.6 Conclusion..................................... 142 4 Systematic Error Detection 143 4.1 Motivation and Case Study........................... 143 4.2 Methodology................................... 146 4.2.1 Algorithms................................ 148 4.2.2 Metrics.................................. 149 4.3 Detection Problem................................ 151 4 4.3.1 Offline................................... 152 4.3.2 Online................................... 152 4.3.3 Reconciled UAG............................. 153 4.4 Effectiveness and Limitations.......................... 155 4.4.1 Simulation Procedure........................... 156 4.4.2 Results................................... 157 4.5 Discussion..................................... 159 4.5.1 Conclusion................................. 161 5 UAG-led Node Error Detection 162 5.1 Joint Energy Balancing.............................. 162 5.1.1 Distribution Grids............................ 163 5.1.2 Power Stations.............................. 168 5.1.3 Industrial Customers........................... 184 5.1.4 LNG Terminals.............................. 184 5.1.5 Compressor Station Metering...................... 185 5.1.6 Interpretation............................... 185 5.2 Predicted vs Actual Flow Analysis....................... 187 5.2.1 Methods of Prediction.......................... 187 5.2.2 Classification............................... 189 5.2.3 Statistical Prediction in Literature................... 190 5.2.4 NG Load Forecast............................ 191 5.2.5 Interpretation............................... 192 5.2.6 Predicted UAG.............................. 192 5.3 Comprehensive Error-reducing Process..................... 193 5.3.1 Baseline Contingent Error Minimisation................ 193 5.3.2 Independent Error Minimisation..................... 194 5.3.3 Investigative threshold.......................... 195 5.3.4 Discussion................................. 196 5.4 Conclusion..................................... 197 6 UAGMS { Industrial Integration 198 6.1 Solution Architecture............................... 198 5 6.2 UAGMS Backend................................. 199 6.2.1 Data Provision.............................. 199 6.2.2 Hosting.................................. 201 6.3 Features...................................... 202 6.3.1 UAG Monitor Tab............................ 202 6.3.2 Causality Detection Tab......................... 203 6.3.3 LDZ Weather Model Tab......................... 204 6.3.4 Changepoint Analysis Tab........................ 205 6.3.5 Reporting Tab............................... 205 6.3.6 Data Configuration Tab......................... 205 6.3.7 Help Page................................. 206 6.4 Summary and Future Development....................... 206 7 Summary and Recommendations 215 7.1 Recommendations................................. 215 7.1.1 Monitoring and Statistical Control................... 215 7.1.2 Increasing UAG Calculation Frequency................. 216 7.1.3 Data Confidence............................. 216 7.1.4 Standardisation of Reporting...................... 217 7.1.5 Data Sharing............................... 217 7.1.6 Cross-departmental approach to UAG management.......... 218 7.1.7 Documentation of Sources of Uncertainty................ 218 7.1.8 Volumetric Balancing........................... 218 7.1.9 Adoption of an Independent Error Reducing Process......... 219 7.1.10 Development of an Action Plan..................... 219 7.1.11 Linepack Modelling Evaluation..................... 219 7.1.12 Automation of the UAG Calculation.................. 219 7.2 Summary..................................... 220 7.2.1 Limitations................................ 221 7.2.2 Future Research Direction........................ 221 7.2.3 Closing Remarks............................. 222 A Glossary 223 6 B CWV calculation 225 Bibliography 226 7 List of Tables 1.1 Typical gas composition............................. 26 1.2 Node type and aggregate daily flows by group, 2019. Flows are shown in terms as pure energy and percentage throughput, in terms of the mean and standard deviation................................. 28 1.3 Yearly UAG and OUG.............................. 32 2.1 Relevant expanded uncertainties at the 95% confidence level in key measure- ment chain components.............................. 47 2.2 Mean daily CV, mean daily CV standard deviation for select offtakes, 2017. 66 2.3 Select UK weather station soil data for 2017, from MIDAS database. Subscript denotes soil depth in cm, for the indicated function: µ, σ; min; max denote the mean, standard deviation, minimum and maximum temperature respectively. idsrc indicates the MIDAS id........................... 75 2.4 Meter vs Data error statistics, 2011-2016.................... 79 2.5 Summary statistics by correction group. All energy values are in GWh. Mean and Sum are based on absolute values...................... 81 2.6 UAG error sources in the NTS. M.Cap refers to the overall capacity of the error type to be modelled, and accounted for in a composite model. Impact refers to the potential impact on UAG. UAG ± indicates whether the error has a strictly positive, negative or mixed impact on UAG........... 87 3.1 Shapiro-Wilks normality test for UAG by year, 2012-2020........... 99 3.2 Multivariate regression model, 2015-2020 for predictors in Section 3.2.2. Es- timates,
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