Dynamic Model User Guide - Joint Office of Gas Transporters
Contents
1. The likely AQ following an AQ correction is 3 times the size of the current AQ this is because of the level of change needed to fix validation where the tolerances are of this size The model uses this to determine the 1 in 20 worst case scenario The maximum number of AQ corrections has been determined as the number of meter read failures that cannot be corrected and resubmitted Updating the Data Xoserve may be able to provide updated information on actual meter read rejection percentages and average AQ following project Nexus go live Xoserve may also be able to provide updated information on meter read frequency on an ad hoc basis but this should be discussed between Xoserve and the PAW R13 Lack of Winter Annual Ratio Band calculation for Sites in Product 4 The model assesses the risk of not completing a site specific winter consumption profile on sites in product 4 Currently a Winter Annual Ratio WAR is used to determine a site specific winter consumption for a monthly read site with an AQ gt 293 000kWh It is calculated as the December to March consumption divided by the AQ If the meter readings are not available to complete a site specific WAR the default EUC profile is used Following Nexus go live product 3 sites will be reconciled monthly so the effect will be minimal The main effect will be a profiling effect to initial allocation of all sites within product 4 Engage Consulting Limited Page 19 of 24 www engage consul2. Average Error per fault day kWh A eae ees aiiate inp oian comes from measurement error register to be over written by the PAW Probability of an error occuring per day Probability of ameter having a fault on any day Number of offtake meters per LDZ 14 38 Average LDZ meters er a a Using a binomial distribution with n no of Off take meters A e ere Net and p probability of an error occuring per day Energy error for VAR Daily kWh error The model s user should update Risk 01 data with a refreshed version of the Measurement Errors Register following the identification and evaluation of an offtake meter error Records with errors that do not have a start and end date must be removed 4 2 R2 LDZ Offtake Measurement Errors that remain undetected Where an offtake measurement error is not detected then the error will never be corrected Where an error occurs and remains undetected the proportion of NTS Shrinkage will remain inaccurate The model assess the risk to initial allocation and final reconciliation following the end of the settlement window This risk affects all products 1 4 4 2 1 Data Used Risk 2 should also be updated with information from the Measurement Errors Register We have used the same data as risk 1 and added the probability of an error remaining undetected 4 2 2 Determining the 95 Worst case Scenario In each case we are assessing the probability of the number of offtake meter errors occurring on our average LDZ on any given day and no
3. the impact to one shipper is evaluated 2 4 Compatibility We have built the model in Microsoft Excel 2013 It does not contain SQL or macros This model requires a minimum of Excel 2007 and earlier versions of MS Excel will not display the model correctly Engage Consulting Limited Page 6 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 WENSAge Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 3 How to Navigate Through the Model 3 1 Menu The menu tab is to navigate around the model The model only assesses one risk at a time The user should select the risk to assess using the drop down on the menu tab The selected risk will then be highlighted green Selected Risk 4 LDZAllocation Error Corrected Click the banner above to return here Common Data Risks Model Shipper Matrix 1 LDZ Allocation Error Corrected LDZ Volume Common Data 2 LDZ Allocation Error no correction Allocation 3 Meter Read Validation Failure Reconciliation 4 Failure to Obtain a Meter Reading 5 Use of Estimated Read for Product 1 and 2 VAR Report 6 Read Submission Frequency for Product 4 7 Insufficient Maintenance of the Supply Point Register 8 Change of Shipper Documents 9 Late Check Reads Admin 10 Shipperless Sites Paramter Control Risk Assessment report 11 Theft of Gas About 1 Dynamic Model
4. 15 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wencgage onsulting limited Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 4 8 3 Updating the Data Xoserve can provide ad hoc reports on the following e Number of supply point confirmations per month e Percentage of estimated transfer reads e Number of SARs that have been accepted e Latest meter read date which can be used to determine meter read frequency and e Yearly AQ change 4 9 R9 Late or Incomplete Check Reads Nexus rules transfer the check read obligation from transporters to shippers where equipment is in place that derives meter readings If shippers do not fulfil their obligation there is a risk that metering drift will not be correctly assigned to the right shippers It may be that all AMR devices derive reads which has been used as the base case for assessing this risk Shippers are required to complete check reads for all metering equipment that derives a read within the 12 months for MPRNs in products 1 3 and monthly read sites in product 4 and every 24 months for annually read sites within product 4 This affects product 1 4 which could be all AMR devices The risk of not completing these check reads is that drift is not identified 4 9 1 Data Used We have used data from Mod 81 report 10 to determine the average AQ for
5. Daily kWh error No Assets attached to confirmed sites Source Xoserve s data cleanse reporting Decemb er 2014 Percentage of MPRNs potentially with an incorrect read factor A From Xoserve data 3 Average AQ from Mod 81report kWh Yea 6 Estimated correctness of the AQ 20 error Average AQ lin 20 worst case AQ accuracy Total energy errorfor VAR 31 180 Daily kWh error Error split by product meter count P1 6 Daily kWh error P2 Daily kWh error P3 Daily kWh error P4 Daily kWh error 4 7 2 Determining the 95 Worst case Scenario To demonstrate the 95 worst case scenario we have used the following scenarios e Correction factors being incorrect by a factor of 10 e Read factors being incorrect by a factor of 35 3 e The AQ of sites with unconfirmed assets being 20 incorrect 4 7 3 Updating the Data On request Xoserve may be able to provide information around the percentage of MPRNs which have potentially incorrect correction factors incorrect read factors and unconfirmed Engage Consulting Limited Page 14 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London ECiN 8JR VAT Registration 754 7463 04 Wencgage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk assets Xoserve currently complete reporting to maintain data quality we anticipate that this will continue however there is not
6. EUC 03 09 however we have reduced the estimate to 250 000 to account for I amp C sites that are in EUC 01 and 02 The PAW should update this data if a better estimate becomes available The percentage of sites requiring a check read is determined as the total percentage of I amp C MPRNs within the market We have estimated the number of sites requiring a check reads and the impact of not completing these check reads We have also estimated the energy consumed by meters in each products Engage Consulting Limited Page 16 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 WENSASge Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Percentage of sites requiring a check read Average AQ for I amp C sites Average difference between check read and actual lin 20 event percentage of late check reads 1 in 20 event percentage of uncompleted check reads within the settlement window Energy errorforinitial allocation 135 317 Daily kWh error Energy errorforfinal allocation Daily kWh error Percentage of Product 3which is I amp C energy volume Percentage of Product 4which is I amp C energy volume I amp C Energy by Product Proportion of all energy that is I amp Cin product P1 P2 P3 P4 Total I amp C Energy Split Split of I amp C between products P1 P2 P3 P4 Total Daily kWh error D
7. Specification 12 Use of AQ Correction Process Model user Guide 13 Use of WAR for EUC 3 and Above 14 Approach to Retrospective Updates 15 Risk 15 Not used 16 Risk 16 Not used gt Splash Menu Common Data Shipper_Matrix Parameter Control LDZ Volume Allocation Reconciliation Checks Var Report RiskO1 Risk 4 3 2 Using the model The model simulates an average LDZ for an average settlement day The model is currently configured to default specifications determined by Engage Consulting and can be run without any configuration by the PAW if desired The default values are shown in Section 5 The model characteristics are sensitive to the size of shippers within the simulated LDZ and their product uptake The common data also determines the volume of gas and number of customers The current breakdown of the market has been determined to reflect reality with shippers having a mixed portfolio of customers The common data has been determined to be an average LDZ using average data as published on the National Grid website Some parameters used for probability distributions should be updated with newer information when available however in cases where data from the Mod 81 and AUGE reports have been used model users will need to find alternative data as these reports will not be published in their current format from 2016 onwards Section 4 describes how to update each risk Throughout the model input cells are formatted consistently in in li
8. a o E e 7 3 2 USA Me Moderar doit econ 7 4 Using the Model to Assess Performance RISKS sseseseseseneneceeeseeseeenenenseeeeeeeeeusnensusneeeaeeeeeesesenensneneses 8 4 1 R1 Identified LDZ Offtake Measurement ErrOrs ccccceccsccseeeeeeseeeeeaeeeeeeeeseeeeeeeseeeeesaeeeeseeaeeeeeeeeaeeaesansaenansas 8 4 2 R2 LDZ Offtake Measurement Errors that remain UNdeteCted ccccccceceeeeeeeeeeeeeeseeeeeeeeseeaeeeeeneeaeeeeaeseenaneas 9 4 3 R3 Meter Reading Validation FallUre xccsincsvecccdaccmnnssantccuseansasincimensgesencecevnceinsdeemsecceneansccvesecancaanneiieemiiedcwncds 10 4 4 R4 Failure to Obtain a Meter Reading cccccecceessceececeeeeesecnecncconeoecneanseecnsaesneansneonseeseecesnesaesssneenensaaees 11 4 5 R5 Estimated Reads used for daily read SIteS oocconconconconconconcancanocnoninnnoncnnnononnnnnrnnonronnnnnnnrnnrnnrorennenaraninss 11 4 6 R6 Meter Read Submission Frequency for Product 4 ccoccoccocccccncconoccnnnnncnnconcnnonnnnnnnnnnronronnnnnnnrnnrnrennonnranenss 12 4 7 R7 Insufficient Maintenance of the Supply Point Register coocoococcoccccconconcocionnoncnncanonnnnnonnnnonnnorennnnncanenss 13 4 8 o Oleo Or MPO PR no o o OO oO A 15 4 9 R9 Late or Incomplete Check Reads ccccccsccecceeseceececneeeeaecneonsaeeneceensanseecneaesneesneonseeseeoesnesaeensnsensansages 16 ELO RIO Shipperless SIGS geccnoeccesantentesintciaieneerdechanaceacadiadeniendexndoarsacananettedauddnndatdenscnnctorsaudsaesanscuec
9. co uk product 2 which can be updated separately as transporters are responsible for check reads on sites in product 1 and shippers are responsible for updating sites in product 2 Data from the Mod 81 report has been used to determine the average AQ for EUC 07 09 1 100 From shipper matrix tab From shipper matrix tab Percentage that f ail outer tolerance _ To be updated by the PAW with more accurate information Percentage that f ail outer tolerance To be updated by the PAW with more accurate inf ormation Estimates per day Reads by f ailure rate Estimates per day 2014 Mod 81 Report 10 EUC 07 08 and 09 from Risk 05 data sheet 2014 Mod 81 Report 10 EUC 07 08 and 09 from Risk 05 data sheet 2014 Mod 81 Report 10 EUC 07 08 and 09 from Risk 05 data sheet 2014 Mod 81 Report 10 EUC 07 08 and 09 from Risk 05 data sheet E 25302 k Wh per day 1in 20 Scenario annual estimated difference for product 1 39 380 Daily diff erence in kWh using anormal distribution Average energy between estimate and actual Product 2 OA kWh per day lin 20 Scenario annual estimated difference Product 2 39 380 Daily dif f erence in kWh using anormal distribution Percentage of check reads completed by transporters Percentage of check reads completed by shippers Product 1 only Product 2 only Initial Energy errorforP1 693 089 77 Daily kWh error Initial Energy error for P2 1 386 180 Daily kWh error Final Energy errorforP1 1
10. of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage consulting limited 4 10 2 4 10 3 4 11 4 11 1 4 11 2 4 11 3 Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 73 473 Xosere Report Novemb er 2014 Number of Isolations per year 0 05 Engage Estimate Xosere Report Novemb er 2014 18 698 From Mod 81 Report 10 Risk 03 Data Probability still consuming gas Isolations per day Average AQ Length of time remains undetected years im 0 75 Average time until GSR visit 1in 20 worst case no of shipperless sites created per day Taking a binomial distrib ution Determining the 95 Worst case Scenario A binomial distribution has been used with parameters n number of isolations per day and p probability an isolation has been completed but the site continues to consume gas Updating the Data The PAW can update e The number of isolations per year e The probability of a site continuing to consume gas and e The detection rate of the shipperless sites which have been created The model can be run using different parameters as the effects of UNC Modification 424 425 are more widely understood and further data becomes available R11 Theft of Gas Theft of gas creates a risk to shipper allocation as unidentified gas is inflated The model evaluates the val
11. the read factor is not correct and where no assets are attached to confirmed sites This risk affects products 1 4 4 7 1 Data Used We have used the data provided by Xoserve to track their data cleanse work in December 2014 Engage Consulting Limited Page 13 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Potentially incorrect correction factors Source Xoserve s data cleanse reporting Decemb er 2014 Percentage of MPRNs potentially with an incorrect correction factor From Xoserve data 8 Average AQ from Mod 81report kWh Yea Factor Energy is incorrect by Average AQ lin 20 worst case Energy factor is out by afactor of 10 Total energy errorfor VAR 854 791 Daily kWh error Error split by product meter count P1 Incorrect read factors Source Xoserve s data cleanse reporting Decemb er 2014 Percentage of MPRNs potentially with an incorrect read factor 0 0076 From Xoserve data 3 Average AQ from Mod 81report kWh Yea Average AQ 3 Factor Energy is incorrect by lin 20 worst case Energy factoris out by afactor of 10 Total energy errorfor VAR 293 439 Daily kWh error Error split by product meter count P1 Daily kWh error P2 lo 23 Daily kWh error P3 52 819 Daily kWh error P4
12. 386 18 Daily kWh error Final Energy error for P2 2 772 Daily kWh error Over time the average AQ may reduce as smaller MPRNs are elected into product 2 4 5 2 Determining the 95 Worst case Scenario A normal distribution has been used to determine the 95 worst case scenario with uy 840 154 kWh the average AQ change for EUC 07 09 and standard deviation of 8 227 834 kWh The 95 worst case scenario has been determined as a daily difference between estimate and actual as 39 452kWh per day The final settlement error has been determined as 0 2 of reads not being updated within the settlement window This matches the total average from data in the East Midlands provided by Xoserve 4 5 3 Updating the Data The risk needs to be updated with the mean yearly AQ change of products 1 and 2 percentage read failure by product 1 and 2 as well as the percentage check read not completed by product 1 and 2 4 6 R6 Meter Read Submission Frequency for Product 4 This assesses the risk created by infrequent meter read submissions for sites in product 4 Where read frequency is lower there is a higher chance that the AQ will not reflect true consumption The risk has been set up to consider all MPRNs in product 4 4 6 1 Data Used This risk uses similar data to risk three as shown below Engage Consulting Limited Page 12 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N
13. 8JR VAT Registration 754 7463 04 from Xoserves data provided to the Nexus Workgroup on 24th November 2014 from Xoserve s data provided to the Nexus Workgroup on 24th November 2014 WENSASge Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Average AQ N 1 a d 81 Rep a Average AQ change between 13 14 2 16 Mod 81 Report EUC 01 08 Standard Deviation 2 97 Spread of Change in AQ values between Shippers Average Decrease in AQ 538 Standard Deviation 556 1 in 20 Scenario Change in consumption 377 Derived using a normal distribution Average number of days between meter reads Data provided using Xoserve s sample data Energy error for VAR 117 695 Daily kWh error 4 6 2 Determining the 95 Worst case Scenario The 95 worst case scenario is determined using a normal distribution that assesses the kWh difference between actual consumption and AQ for all MPRNs from in product 4 4 6 3 Updating the Data This risk should be updated periodically with the average days between meter readings and average AQ 4 7 R7 Insufficient Maintenance of the Supply Point Register The model assesses the risk created due to the inaccurate data in the supply point register Some errors will cause settlement to process a significant consumption error that validation will not ordinarily block We have used three scenarios to quantify that risk Where the correction factor is not be correct where
14. Page 8 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wencgage consulting limited Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk e pis the probability of 1 meter having an error on any given day This is determined as number of meter errors number of meters X average length per error total period measured Using this distribution X Binomial 14 38 0 0641 where the number of meters is 14 38 and the probability of each one having an error is 6 41 The 95 worst case scenario has been determined as 3 errors within the same LDZ on the same day 4 1 3 Updating the Data When the Measurement Error Register has new data the model s user can use this to determine new inputs for this risk The inputs derived from the Measurement Error Register are the probability of an LDZ measurement error occurring on a given day the average length of an LDZ meter error the overall time that the sample covers and the total number of errors over the sample period Number of LDZ Off take Errors Measurement Error reports total Total Number Offtake Meters All the off take meters on the system a armain comes from measurement error register to be over Average Days per Error Britten by the PAW GWh Information comes from measurement error register to be over Total Metering errors written by the PAW J
15. aily kWh error 71 807 Daily kWh error Initial Energy error for VAR P1 Initial Energy error for VAR P2 Initial Energy error for VAR P3 Initial Energy error for VAR P4 Daily kWh error Final Energy error for VAR P1 9 Daily kWh error Final Energy error for VAR P2 OS Doily kWh error Final Energy errorfor VAR P3 96 Daily kWh error Final Energy errorfor VAR P4 34 Daily kWh error 4 9 2 Determining the 95 Worst case Scenario The 95 worst case has been estimated as 5 of qualifying sites have not had a check read Currently there is no data available to enable this estimate to be updated 4 9 3 Updating the Data The average AQ of I amp C sites and the percentage of check reads completed would need to be updated A report from Xoserve could be used to determine a more accurate AQ for meters that derive reads 4 10 R10 Shipperless Sites The model evaluates the performance risk created because of shippers erroneously withdrawing from sites that continue to consume gas All energy consumed by shipperless sites is allocated to unidentified energy This risk affects product 1 4 4 10 1 Data Used Xoserve has provided a report on the number of isolations per year The probability of a site which has been withdrawn from still consuming gas has been determined as 0 05 5 The average AQ has been determined using the Mod 81 report Engage has determined the detection rate 1 year per site Engage Consulting Limited Page 17
16. ation will be completed It would be possible for a shipper to use the retrospective updates process only where they are advantaged financially This is a risk to product 3 and 4 allocation 4 14 1 Data Used We have used the number of RFA and CDQ query in Conquest as an approximation for the number of adjustments The average AQ has been taken from the Mod 81 report Engage have estimated the percentage impact on reconciled energy Number of MPRNsin LDZ 2 200 000 Core data Forecast percentage of MPRNs which require Percentage of Request for Adjustment amp retrospective update affecting reconcilation 0 04 Consumption Dispute Queries in 2012 Average impact on reconcilation 1 00 Engage estimate Average AQ 18 698 Mod 81 Report 10 Average impact on reconcilation lin 20impact on reconciliation 4 14 2 Determining the 95 Worst case Scenario Engage have estimated a 1 difference on energy allocation 4 14 3 Updating the Data Xoserve may be able to provide the number of retrospective updates that are process when Nexus has be implemented Engage Consulting Limited Page 20 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage consulting limited 4 15 4 15 1 4 15 2 4 15 3 Engage Consulting Limited Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk R15 Unregistered Si
17. d 15 Unregistered sites This document and the dynamic model assumes the reader has an understanding of the current and future settlement arrangements Engage Consulting Limited Page 4 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 engage ZA 2 2 Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Description of the Model Model Construction The model uses common data to convert the gas volume entering into an LDZ into identified and unidentified gas These two categories are then split by product category to derive the initial allocation and reconciliation volume The market shares are used to divide the energy allocation between the 7 shippers in the market The reconciliation process simulates individual meter point reconciliation and the redistribution of energy through unidentified gas The energy will be redistributed to Shippers based on an approximation of consumption over the last 12 months Market share data is used as an approximate value The model has market share data at the start and end of a model year and uses the average of the two values this assumes a linear change from start to end The basic model structure is shown below Each of the risks vary the initial allocation or final reconciliation or both as they are fed through the model The parameter control
18. d for customers changing shipper where only a whole number of change of shipper events can occur Normal distributions have been used for continuous probability such as energy consumption Al Binomial Distribution The binomial distribution is for x discrete events it has parameters n and p and is characterised as follows e nis the number of independent events n must be a whole number e pis the probability of a success occurring where p must be between 0 1 e qis 1 p e Xis number of success that occurring from a total of n trials P Y n A etan g 1 m p e a Probabi a A2 Normal Distribution To be used for continuous probability distributions taking a symmetrical distribution Normal distribution take parameters u and o with the following characteristics e u mean and o standard deviation of a set of data e In diagram A below z is the number of successes this shows a 95 score of less than Z e Diagram B shows how to find the 5 probability where there is a negative mean where AQs are decreasing Engage Consulting Limited Page 24 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04
19. dsasabitenteacnres 17 FIL RL CWE GIS iia 18 4 12 R12 Fair Use of the AQ Correction Process ccsccsssececcesececsececceesececeeeeecuseesesseseeeeesesesaeseeseneesecausetsenensenas 19 4 13 R13 Lack of Winter Annual Ratio Band calculation for Sites in Product 4 ccccccecseeseeeeeeeeeeeeeeseeeeeseesaeeeees 19 4 14 R14 Bias approach to retrospective updates occoccccocconocconocconnoncnnonnnnononnoncnnonrononrnnrnnnnrnnrnrnnrnrrnrannnrninnnnin 20 ES RS Unregistered Ses od 21 Engage Consulting Limited Page 2 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 WENSASge Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 5 Updating Reference Data and RUNNING Scenarios cccscencnseseeeeseenenensnseeeeeeenensusnsnseneeeeeeeeenensnensneses 22 5 1 q A A a 22 5 2 FOU AE a daa aioas 22 5 3 Bea O neon EEE 23 Engage Consulting Limited Page 3 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 1 Introduction 1 1 Background This user guide describes how to use the dynamic model built in Excel which simulates the Project Nexus settlements processes The model will be us
20. ed by the PAW to assess each performance risk identified within the Gas Market Settlement Risk Assessment report The model simulates the post Nexus settlement arrangements for an averaged sized LDZ with seven shippers operating in a competitive market The core model is set up to replicate gas settlements without any risk to allocation or reconciliation volume for one day To assess each risk the model uses an error distribution to identify the 1 in 20 worst case event and quantify the inaccuracy that it would create if it materialised Each risk can affect products 1 4 differently The risk in KWh per day is run through the model to determine the value at risk and how it is distributed among shippers in the LDZ The PAW will be able to update key reference data and run different scenarios to find the most appropriate value at risk The model assesses the following risks 1 Identified LDZ offtake measurement errors 2 Undetected LDZ measurement errors 3 Meter read validation failure 4 Failure to obtain meter readings 5 Estimated reads used on daily read sites 6 Meter read submission frequency for product 4 7 Insufficient maintenance of the supply point register 8 Estimates used at change of shipper 9 Late or incomplete check reads 10 Shipperless sites 11 Theft of Gas 12 Fair Use of the AQ correction process 13 Lack of WAR Band calculation for qualifying sites in product 4 14 Fair use of retrospective updates an
21. element of each risk determines which product category the risk impacts Model Engine LDZ Initial Volume Allocation Reconciliation Risk Evaluation Parameter Contd VAR Report Value at Risk Report The Value at Risk VAR is determined as the difference between the cost incurred between the reference scenario where there are no risks and the scenario where the cumulative probability is 95 The 95 worst case scenario uses normal or binomial distributions shown in appendix A The relevant distribution is determined dependant on whether the data is discrete or continuous The graph below shows the probability distribution of a normal and binomial function Engage Consulting Limited Page 5 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London ECiN 8JR VAT Registration 754 7463 04 Wengage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Orange Pink Black Scenario Scenario Scenario Likel ihoo d Most likely 1 in 20 event scenario High O Average metric for each risk 95 percentile 9 scenario 2 3 Net Effect of Settlement Risks Where a risk has equal and opposite effects only half of the risk is assessed LDZ meter errors initially effect NTS shrinkage and this cost is then moved to energy allocation The model does not assess the impact on NTS shrinkage Similarly where an estimated transfer read is used
22. gage Contact Details 44 7827973224 naomi anderson engage consulting co uk Updating Reference Data and Running Scenarios This data should be updated by the PAW to assess the value at risk in different market conditions The uptake of different settlement product categories is likely to change as shippers develop their own strategies to optimise the new functionality created through Project Nexus Shipper Matrix This contains static data that defines characteristics of the modelled market Unidentified Gas is allocated based on consumption for the last 12 months so the current day s market shares and previous year s market share of energy consumption are required These shares must be broken down by product category for each shipper The PAW can update the overall product split of the settlement market The PAW can also update the product split by overall meters aS some risks such as change of supply is dependent on both energy consumption and number of meters The cells that are light brown in the shipper matrix tab should be agreed and updated manually by the PAW Year before model Y 1 Model Day Product1 Product2 Product3 Product 4 P P2 Shipper Shipper Name o 5 14 0 o 5 5 Residual Polluted Year before model Y 1 Model Day Product Split Energy 1 5 15 79 1 5 15 79 iff e2H 3H aw Product Split Meters 1100 2 200 396 000 1 800 700 Shipper 1 3 are coloured
23. ght brown to indicate where the model user can make updates Engage Consulting Limited Page 7 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London ECiN 8JR VAT Registration 754 7463 04 Wengage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk al Using the Model to Assess Performance Risks The model documents which data it has used to determine the 95 worst case scenario for a risk and where the user can get current data for the model In some instances where no data is available Engage has estimated the impact and probability When using the model to assess each risk use the menu tab to move between risks The table embedded within each risk shows the value at risk to shippers 1 7 This VAR is driven by product uptake and shipper market share If this table is red then risk is not the currently selected risk see the menu tab for how to select a risk 000s Year Allocation Reconciliation Supplier Reference Risk Variance Reference Risk Variance Small Polluter Medium Polluter 43 415 42 717 43 472 43 472 Large Polluter 158 531 155 983 A 158 432 158 432 Small Polluted 59 860 58 898 59 801 59 801 Medium Polluted 52 888 52 037 52 831 52 831 Large Polluted 185 238 182 260 A 185 359 185 359 Residual Polluted 779 764 767 228 ll 779 863 779 863 4 1 R1 Identified LDZ Offtake Measurement Errors T
24. he model assesses the risk offtake measurement errors create to accurate allocation The settlement process initially allocates the error to NTS shrinkage With the correct LDZ throughput volume adjustments will fall into unidentified gas reconciliation and it will allocate energy to shippers according to their consumption share over the previous 12 months This error affects all products 1 4 according to their consumption 4 1 1 Data Used Data from the Measurements Errors Registered which is kept on the Joint Office website is used to evaluate the probability and impact of an offtake error occurring on an LDZ The register can be found here http www gasgovernance co uk MER It is updated periodically when measurement errors are identified We have used the following data e Number of meter errors 127 errors e Total number of offtake meters 187 offtake meters e Period 8 years e Average error which has been reported 96 781 kWh per day e Average length per error 297 days and e Total length period 3650 days 4 1 2 Determining the 95 Worst case Scenario In each case we are assessing the probability of the number of offtake meter errors occurring in our average LDZ on any given day This can be approximated by a binomial probability distribution The parameters are as follows e nis the average number of offtake meters in an LDZ This has been determined to be number of meters number of LDZs and Engage Consulting Limited
25. hem for individual meter point reconciliation We have assessed the risk of shippers using the AQ correction process in a biased way i e only correcting certain AQ changes AQ corrections are likely to be required on increasing AQs as zero consumption is permitted within the Nexus rules AQ corrections will only affect MPRNs in product 4 Data Used An AQ correction will be required if the AQ is increasing by 3 times the current size for EUC 01 We have used three times the average AQ for the worst case Xoserve has provided a report of the latest meter readings for all MPRNs in the East Midlands and we have used this to determine that the average time between meter readings is 136 days The number of meter reads which will fail tolerances as a result of AQ SOQ being incorrect was provided by Xoserve to the Project Nexus workgroup on 24 November 2014 18 698 2014 Mod 81report Average AQ 300 average AQ 56 095 Maximum acceptible tolerance for EUCO1 according to the BRDs lin 20 worst case 56 095 Maximum acceptible tolerance for EUCO1 according to the BRDs Average days between meter readsin product 4 Number of Reads submitted annually Derived using days between meter reads and MPRNs in product 4 Number of Meter Read Failure 1 0 A Number of AQ corrections daily Approximated by number of daily f ailed reads lin 20 change in consumption Energy error for VAR 2 849 094 Daily kWh error Determining the 95 Worst case Scenario
26. hhi Dynamic Model User Guide Engage Consulting Limited 23 January 2015 y e A a mu a yO y m a se l an ee ee et Ld ie ee ee ee a 2 ses 2 aa LLL Tina os a 9 s ii os 7 pie n l i lt q TIE rl jaa E eer www engage consulting co uk Wencgage consulting limited Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Document Control Authorities Version Issue Date Author Comments 0 1 19 December 2014 Naomi Anderson First draft for review by the PAW and 2 0 23 January 2015 Naomi Anderson Ofgem Includes changes to the model and comments from PAW members Table of Contents DOCUMENE A aeaee aaae EEN ae aE AEE Ee aa Ea Ae Ea E Ea aa e a a 2 AUtNONItIES aeee aae ae EEEa e ee e aE EE Ea ae EE EEE aE Ea 2 EUA o 2 1 Introduction cicccccseuscecscecseceecssansecesededcsececusasacienaacsecdcssuesenedesesessssesensaccscesssscesssanentacreresaiatsesswssdsenuseseuecede 4 1 1 e 4 2 DeSCHIPtIOn OF CHE Model iccccscscacusceccscsveacacscscecdssccreneveecccrvswacsiciencusacnustsusenceredeatsnicanararuiecestusauacebeensuag 5 2 1 Model Cons Gl CROW eric sete EAE A E eden eos 5 2 2 Vale EI FISK RODO anclado rotacion EE EAE 5 2 3 Net Effect of Settlement RISKS ir A AAN 6 2 4 nn 6 3 How to Navigate Through the Model isccccccccnceiscacrededsstisdenccscawserededcceedecsssecet ssnvessesescsecnccsvessvacrescecrerdces 7 3 1 io re q
27. hing in UNC code to mandate shippers to maintain problem sites as they are identified 4 8 R8 Change of Shipper The model assesses the risk created by estimated reads at change of supply on product 4 only Where the shipper fails to provide any reading during a change of shipper the transporter will provide an estimate 16 days following the transfer date Change of shipper reads can be replaced with a shipper agreed within 12 months of the change of supply date Where a change of shipper is completed using an estimate transfer read and not replaced with an actual read the closed reconciliation period of the previous shipper will end on an estimate and the new reconciliation period will begin on the same estimate An estimated meter reading is used when e No actual reading was obtained e No actual is available because the actual transfer read was rejected due to data discrepancies and e Because a reading failed validation tolerances due to an incorrect AQ We have assessed the risk of estimated transfer reads on accurate reconciliation This risk affects product 4 sites as the length of time between reads provides a higher probability that the estimate will be inaccurate Where the transfer read does not reflect reality the final allocation of energy to each shipper may be incorrect Any misallocation in energy affects the two shippers who have been responsible for the meter point 4 8 1 Data Used Xoserve has provided data for the number of supply
28. onsumes and this will adjust unidentified gas accordingly A risk to other shippers is created when the shipper pays for less gas than their customers consume The principle risk because of meter read failure are inaccurate AQs and delayed reconciliations There is a corresponding impact of late reconciliation on the unidentified gas reconciliation energy This risk affects product 4 only 4 3 1 Data Used The Mod 81 report 10 provides the following data e Average AQ e Percentage change in AQ between 2013 and 2014 and e Standard deviation of the percentage change Xoserve s data of latest meter reading date for all MPRNs in the East Midlands has been used to the number of MPRNs within the model that have not had a meter read accepted by Xoserve within the last 12 months Average AQ 18698 Mod 81 Report EUC 01 08 Mod 81 Report EUC 01 08 Spread of Changein AQ values between Shippers Average AQ change between 13 14 Standard Deviation Average Decrease inAQ Standard Deviation MES lin 20 Scenario Change in consumption Derived using anormal distribution 5 Data provided using Xoserve s sample data 125 476 Daily kWh error Percentage of MPRN not readin a year Energy errorfor VAR 4 3 2 Determining the 95 Worst case Scenario The model uses a normal distribution and average AQ change to determine the 95 percentile scenario We have derived the average AQ change using the Mod 81 report 4 3 3 Updating the Data U
29. point confirmations by month and the percentage of estimated transfer reads used The percentage of MPRNs that change shipper is deduced using the data provided by Xoserve and the total number of MPRNs on the Mod 81 Xoserve can provide an updated ad hoc report detailing the change of supply percentage percentage of estimated reads used and the total number of supply points To determine the average difference in AQ we have used the average reduction from the latest Mod 81 report 10 The average number of days between meter reads for product 4 have been taken from an extract of showing all of the meter readings in the East Midlands MPRN Sample Size 21 714 664 Mod 81 Report Total MPRNs Number of supply point confirmations per year 2 782 040 Report from X Approximate CoS per day within the LDZ modelled 772 Using the model LDZ size Probability of Estimated Reading Used and not replaced 0 343 dani p nt i ie eis esi AR 1 in 20 number of estimated CoS reads per day 287 Average AQ 18 698 Mod 81 Report EUC 01 09 Average AQ change between 13 14 2 16 Mod 81 Report EUC 01 05 Average Difference in AQ compared to consumption 404 Average time between meter reads in product 4 136 Report from Xoserve 3rd December 2014 012 Determining the 95 Worst case Scenario A binomial distribution has been used with parameters n number of change of shipper events and p probability the transfer read remains as an estimate Engage Consulting Limited Page
30. red to represent polluting shippers These shippers realise the risks and their allocation and reconciliation will have corresponding errors simulated The risks pollute the other shippers and their allocation and reconciliation where appropriate Shippers 4 6 are polluted and will not negatively affect settlement allocation Shipper 7 is the residual market Common Data This common data determines the key characteristic of the LDZ The model looks at one settlement day and extrapolates this risk to a year Calorific value is the average forecast obtained from Distribution Network Operators shrinkage statements MPRNs is an approximate size of an average LDZ The LDZ size is the total gas usage in m for 23 November divided by 13 The Unidentified Gas is 1 of throughput as approximated by the AUGE The system average price is the average price for gas year 2013 2014 Data can be used to refresh the AUGE percentage following publication of their yearly statement Average CVs are published in the DN shrinkage statements National Grid s data Item Explorer Website can be used to determine the most appropriate system price to be used in the settlement calculations Engage Consulting Limited Page 22 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage Engage Contact Details 44 7827973224 naomi anderson engage consulting co
31. sers of the model will need to find an alternative source for the average AQ and the rate of AQ change following Nexus go live when the currents reports are no longer available Engage Consulting Limited Page 10 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wencgage consulting limited Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 4 4 R4 Failure to Obtain a Meter Reading The model assesses the risk of shippers failing to obtain meter reads within the settlement window of 36 48 months For ease of assessment we have determined the settlement window to be 42 months the average of 36 and 48 Where reads are not obtained the current AQ will be out of date and the MPRN will have incomplete reconciliation This risk affects product 4 only 4 4 1 Data Used Where the current AQ is historical the true consumption is more likely to differ from the AQ To assess the risk we have used data from Xoserve to determine the percentage of sites that do not have a read accepted on the UK Link system within the last 42 months We have used the last 4 years Mod 81 reports to determine average yearly reduction in AQ Sample of latest meter read gt 42 months 4 811 Sample provided by Xoserve of East Midlands Total 2 191 244 Sarmples provided by Xoserve of East Midlands Percentage of MPRNs wi
32. t being detected This can be approximated by a Binomial probability distribution X Binomial n p Where n 14 38 number of meters on an average LDZ and number of meter errors number of meters X average length per error total period measured X probability of remaining undetected We have determined the detection probability as 10 If the probability of detection falls below 5 this risk diminishes Engage Consulting Limited Page 9 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 _ engage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 4 2 3 Updating the Data The PAW should update the probability of a meter error occurring and the probability of it remaining undetected 4 3 R3 Meter Reading Validation Failure The model assesses the risk caused by meter read validation failure Validation failure will occur when a comparison of the reading and advance against an expected value falls outside either of the two tolerance levels derived from the current AQ and or SOQ When meter read validation failure occurs individual meter point reconciliation is suppressed and the historical AQ remains live It is likely that as consumption trends are falling this AQ will be on average higher than actual consumption The responsible shipper will pay for more gas than the supply point c
33. ted reads will only materially affect settlement if there is no replacement read within gas flow day 5 MPRNs with significant usage can have volatile consumption Only when a check read is completed will the correct consumption for a site be determined The model assesses the impact of estimated reads being used for daily metered sites at initial allocation and evaluates where check reads are not completed 4 5 1 Data Used The number of daily read estimates is derived using the total number of daily read sites and applying the percentage read failure falling outside both tolerance levels Xoserve published this for the Project Nexus meeting on 24 November 2014 The risks is split into product 1 and Engage Consulting Limited Page 11 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 wengage consulting limited Total number of readsfor product 1 Total number of readsfor product 2 Percentage read failure for product 1 Percentage read failure for product 2 Number of estimated readsfor product 1 Number of estimated readsfor product 2 Average yearly AQ change by MPRN for Product 1 Standard Deviation Average yearly AQ change by MPRN for Product 2 Standard Deviation in AQ change for Product 2 Average energy between estimate and actual product 1 Engage Contact Details 44 7827973224 naomi anderson engage consulting
34. tes The model evaluates the performance risk created because of unregistered sites that have never been on the supply point register All energy consumed by these sites are allocated to unidentified energy This risk affects products 1 4 Data Used Xoserve have provided a report on the number of MPRN creations per year The probability of a site which has been withdrawn from still consuming gas has been determined as 0 05 5 The average AQ has been determined using the Mod 81 report cory Average Number of New MPRNs created per day Probability that these will become unregistered 1 in 20 worst case scenario for the number unregistered sites Average AQ Detection Rate Length of time remains undetected years Determining the 95 Worst case Scenario A binomial distribution has been used with parameters n number of MPRNs created per day and p probability is the probability of these MPRNs being created and not consuming gas This can be updated as more information becomes available Updating the Data The PAW can update the following parameters e The number of MPRN creations per year e The probability of a site not being registered and consuming gas and e The detection rate of the unregistered sites Page 21 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage 5 5 1 sP En
35. th latest read outside the settlement window 0 22 sarnples provided by Xoserve of East Midlands Average AQ 2011 20 451 Mod 81 Report 10 1in 20 consumption change in 2011 Normal Distrib ution Average AQ 2012 Mod 81 Report 10 lin 20 consumption change in 2012 BS Normal Distrib ution Average AQ 2013 Mod 81 Report 10 lin 20 consumption change in 2013 Normal Distrib ution Average AQ 2014 Mod 81 Report 10 lin 20 consumption change in 2014 Normal Distrib ution Total change over 42 months 1 817 41 k Wh error per MPRN 4 4 2 Determining the 95 Worst case Scenario We have applied a normal distribution to the last 4 years Mod 81 data to determine the compound 95 worst case difference between the AQ and the true consumption of the MPRNs that have not had a meter reads accepted by Xoserve In each case the worst case is determined to be where the AQ is understated 4 4 3 Updating the Data The model s user will need to find a new source of average AQ and the rate of AQ change following Nexus go live It may be possible to request from Xoserve an ad hoc report showing the latest meter read acceptance date 4 5 R5 Estimated Reads used for daily read sites The model assesses the risk of estimated reads being used to settle daily read sites Daily read estimates for product 1 and 2 are generated to repeat the consumption from a week ago 7 days previously and where there is no consumption history an estimate of AQ 365 will be used The use of estima
36. ting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wengage consulting limited Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk 4 13 1 Data Used The Mod 81 report 10 has been used to determine the percentage of sites that should have a site specific WAR band The average AQ of these MPRNs is also determined from the Mod 81 report Total MPRNson Mod 81Report 21 714 664 Data from Report 10 Mod 81 Number of MPRNs elligible for a site specific WAR 123 945 Provided by Fiona Cottam 31st Decemb er 2014 Percentage elligible for a site specific WAR Average AQ for EUC 03 and above lin 20 worst case difference in AQ during the winter Percentage site specific WAR completed 4 13 2 Determining the 95 Worst case Scenario By comparing load factors of the four different profiles Engage have estimated that the worst case scenario would be the winter profile is incorrect by 10 4 13 3 Updating the Data Currently there is no way of updating this data without an ad hoc report request to Xoserve 4 14 R14 Bias approach to retrospective updates Following Nexus go live shippers will be able to update historic data items more readily The model assesses the risk of shippers not completing retrospective updates in a fair and even way Where retrospective updates have an impact on consumption a reconciliation or a re reconcili
37. ue at risk created misallocation of gas volume to the market The AUGE report evaluates the suspected amount of theft We have used a range from the latest AUGE statement to evaluate the worst case scenario This risk affects products 1 4 Data Used The latest AUGE statement provides a range of percentages which we have used to evaluate risk Minimum estimated amount of theft Maximum estimated amount of theft 1 in 20 Determining the 95 Worst case Scenario The 1 in 20 worst case scenario has been determined using information from page 13 of the AUGE statement which identifies that theft may be as much as 10 of throughput Updating the Data There will no longer be an AUGE statement following Nexus go live There may be data that can be used from the theft of gas risk assessment service that can be used to update this risk Engage Consulting Limited Page 18 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 Wencgage 4 12 4 12 1 4 12 2 4 12 3 4 13 Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk R12 Fair Use of the AQ Correction Process When an AQ or SOQ prevents Xoserve from accepting a correct meter reading then the shipper can submit an AQ correction Following the correction an updated AQ or SOQ would allow Xoserve to accept future meter reads and use t
38. uk Calorific Value 39 3 MJ m Must be between 37 5 MJ m3 to 43 0 MJ m3 10 92 kWh m Divide by 3 6 to get kWh m3 1kWh 3 6MJ MPRNs in LDZ 2 200 000 Number Total MPRNS in LOZ for all products LDZ Size 18 000 000 m day LDZ Daily Input Quantit U IG 180 000 m day Approximate Unidentified Gas on the model LDZ Indentified Gas 17 820 000 m day LDZ Daily Quantity Offtaken via meters System Average Price e 0 02 kwh 513 Updating Risk Parameters The PAW should adjust the parameter control tab if it is felt that a risk has moved from one product category to another or from allocation to reconciliation This can be done by un hiding the parameter control section between column A and E within each risk tab The parameter can then be changed from false to true or vice versa for each product It can also be updated to move the risk from allocation to reconciliation or vice versa Engage Consulting Limited Page 23 of 24 www engage consulting co uk Registered in England number 3923081 Registered Office 1st Floor Rear 85 Hatton Garden London EC1N 8JR VAT Registration 754 7463 04 engage Engage Contact Details 44 7827973224 naomi anderson engage consulting co uk Appendix A Probability Distributions We have used two cumulative probability distributions to determine the 95 worst case scenario Binomial distributions have been used for discrete events i e these distributions have been use
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