SETTLEMENT ESTIMATION AND REVISIONS POLICY CONSULTATION PAPER. AeN 94 on Oh) 327 Wv'IW.oemo.oo m.ou infofffoemo.corr.ou VERSION: 1. DATE: 4 April 2012

Transcription

1 SETTLEMENT ESTIMATION AND REVISIONS POLICY CONSULTATION PAPER PREPARED BY: Market Performance VERSION: 1 DATE: 4 April 2012 FINAL Am,ttolion l:nergy 1\_.n,ketOpe rctor Ltd AeN 94 on Oh) 327 Wv'IW.oemo.oo m.ou infofffoemo.corr.ou NEW sou- H WAI.ES QU:ENSLAND SOUTH AUSTRALIA VCTORIA.. usrrauan CAPl"-L Tl'RRITORY TAS.'1/iA 'JIA

2 Contents 1 Introduction to Settlement Estimation and Revisions Policy Consultation Purpose of the Policy Estimates for Prudential Purposes Review of Prudential Estimates Proposed Changes Consultation Approach... 5 Current Prudential Estimation and Assessment Process Like-Day Estimation Estimation Performance Estimation Process Limitations Conclusion Proposed Changes to Prudential Estimation Hierarchy of Information to be used in Prudential Estimation Initial Bill Run Analysis of Initial Bill Run Initial Bill Run Accuracy Wholesale Connection Point Data Scaling Factors (TNISFs) Refine Like-Day Estimation Method Proposed Calculation Other Issues Appendix A Summary of Prudential Estimation Review A1.1 Refine Like-Day Estimation A1.2 Segregate Energy Consumption A1.3 Wholesale Connection Point Data Scaling Factors A1.4 Initial Bill Run A1.5 Immediate Data Provision Consultation Paper 4 April 2012 Page 2 of 16

3 GLOSSARY (a) (b) (c) In this document, a word or phrase in this style has the same meaning as given to that term in the NER In this document, capitalised words or phrases or acronyms have the meaning set out opposite those words, phrases, or acronyms in the table below. Unless the context otherwise requires, this document will be interpreted in accordance with Schedule 2 of the National Electricity Law. TERM MEANING AEMO EMS EPR FRMP LD LR MDP MLF EMMS MW MWh NEM MSATS NER NMI PD RRP SCADA SDA SF t TNI TNISF Australian Energy Market Operator Energy Management System Ex Post Reallocation Financially Responsible Market Participant Like-Day Local Retailer Metering Data Provider Marginal Loss Factor Electricity Market Management System MegaWatts MegaWatt Hours National Energy Market Market Settlement And Transfer Solution National Electricity Rules National Metering Identifier Previous Day Regional Reference Price Supervisory Control And Data Acquisition Security Deposit Amount Scaling Factor 30 minute trading interval Transmission Node Identifier Wholesale Connection Point Scaling Factor Consultation Paper 4 April 2012 Page 3 of 16

4 1 Introduction to Settlement Estimation and Revisions Policy Consultation 1.1 Purpose of the Policy The Settlement Estimation and Revisions Policy sets out the policy for the preparation and issue of revised settlement statements, and AEMO's position with regard to the calculation and use of estimated settlement amounts in the issue of settlement statements. In particular, this policy sets out AEMO's response to two of its settlement related obligations under the National Electricity Rules. The obligations relate to the development of and consultation for procedures for estimating settlement results when normal processing is not possible under NER clause (c); and policy for revised settlement statements under NER clause (e). The procedures and arrangements described in this policy complement the requirements of NER clause 3.15 of the National Electricity Rules, and in the event of any inconsistency between the two, the NER will prevail. In addition to estimated settlement amounts that are calculated after system or data failure, the policy also includes the provision of estimates for prudential purposes. NER clause permits AEMO to use a reasonable estimate of the settlement amounts for billing periods where final statements have not been issued. Although there is no Rules requirement to consult with regards to these prudential estimates, AEMO has chosen to include details of the calculation and use of these amounts in the Settlement Estimation and Revisions Policy. 1.2 Estimates for Prudential Purposes AEMO is responsible for monitoring and assessing the daily prudential position for each market participant in the National Electricity Market. This prudential assessment is in place to ensure that AEMO holds sufficient credit support to cover market participant liabilities and minimise credit risk to the NEM. AEMO monitors the prudential position for each market participant through their total outstandings which comprise all billable amounts yet to be paid up to, and including, the previous day. Where actual meter data is not available on a given day to calculate these total outstandings, AEMO is permitted to estimate the daily energy consumed and generated. In the current prudential assessment process, the period for which energy is estimated in the calculation of total outstandings can be more than a week. AEMO has established automated systems to facilitate the prudential estimates, and this data is published to participants through a number of mechanisms including the Prudential Dashboard and the EMMS Data Model. Under some failure scenarios, AEMO may be able to use the prudential estimates as a preference to other estimated settlement amounts, as described in the Policy. 1.3 Review of Prudential Estimates AEMO has reviewed the current prudential estimation process, focussing on the following areas: The absolute accuracy of the estimation when compared against actual metering data The impact of the estimation on the accuracy of prudential assessment, particularly in circumstances where a participant has exceeded their trading limit Assessing the improvement in absolute and prudential accuracy when using a number of alternative estimation methodologies The alternatives reviewed included a range of methodologies, which were also tested in combination to achieve the greatest level of improvement. Details of the review are provided in Appendix A. Consultation Paper 4 April 2012 Page 4 of 16

5 1.4 Proposed Changes To achieve improvements to prudential estimation AEMO is proposing to amend the current Settlement Estimation and Revisions Policy to allow a broader range of available data to be used. The changes are to include: Establishing a hierarchy of information to be use in prudential estimation which will include: o o o actual or SCADA data, where available, otherwise scaling factors that are derived at a wholesale connection point level where appropriate, otherwise regional scaling factors with a refinement to the current estimated energy calculation Utilising additional actual meter data in the calculation of total outstandings o Achieved by performing an additional billing run ( initial bill run) at the end of the second day after each settlement date. 1.5 Consultation Approach This paper sets out the proposed approach AEMO is consulting on to improve its prudential estimation processes and the analysis behind the approach. Submissions to the first stage of consultation, in response to the questions laid out in this document, will be used to inform an update to the Settlement Estimation and Revisions Policy document. The updated policy will be issued for stakeholder consideration at the draft determination stage of the consultation. Consultation Paper 4 April 2012 Page 5 of 16

6 2 Current Prudential Estimation and Assessment Process The current settlement process can be summarised in the following steps: 1. Each market participant s daily outstandings are calculated using a like-day estimation method for energy consumed and NEM dispatch data for energy generated. This data is available for the prudential assessment on the following day. 2. For the purposes of creating Preliminary settlement statements, a Preliminary billing run is performed on the third business day after the end of the billing week. Data from this Preliminary billing run is available for the prudential assessment on the fourth business day, and the Preliminary statements are issued on the fifth business day. 3. For the purposes of creating Final settlement statements, a Final billing run is performed on the fifteenth business day after the end of the billing week. Data from this Final billing run is available for the prudential assessment on the sixteenth business day. The Final statements are issued on the eighteenth business day and payment occurs two business days later. 4. Revision billing runs are performed twenty- and thirty-weeks after the end of the billing week. These are not included within the prudential assessment process. Figure 1 Current Prudential Assessment Process Figure 1 shows the data that is used to calculate total outstandings in the prudential assessment process. The length of the outstandings period varies with the occurrence of public holidays. Typically payment occurs four weeks after the end of the billing week in which electricity is consumed/generated, at which time that billing week would no longer be included in the prudential assessment process. As a consequence of the settlement process there can be more than one week of estimated data utilised in the calculation of total outstandings. The current prudential estimation process is executed daily to estimate the energy consumed and generated on the previous day in the following manner: Energy generated is estimated directly from the NEM dispatch process, with SCADA data being scaled by a static regional factor to account for differences in the point of measurement of SCADA compared to meter data. Energy consumed by generators is estimated by scaling energy from a like-day for which actual metering data is available. The scaling factor is derived from regional dispatch data. Energy consumed by customers is estimated by scaling the energy from a like-day for which actual metering data is available. The scaling factor is derived from regional dispatch data. Consultation Paper 4 April 2012 Page 6 of 16

7 2.1 Like-Day Estimation The like-day estimation method used for the calculation of energy consumed by customers is the area that has the greatest impact on prudential outcomes. The like-day estimation process is executed daily and estimates the value of customer energy for each market participant for the previous day. The like-day is defined as the same day from the most recent billing week for which data from a Preliminary billing run is available. In the case of a public holiday the most recent Sunday is chosen as the like-day. The like-day estimation requires the following information for each 30-minute trading interval: regional demands on the previous day regional demands on the like-day like-day actual customer energy for every market participant at each wholesale connection point This data is used to estimate the previous day customer energy and subsequently the total outstanding amounts for each market participant, using the following steps: 1. Calculate the regional scaling factor ( ) for each region for each 30-minute trading interval as the ratio of regional demand for the previous day (PD) to the chosen like-day (LD): where t represents the 30-minute trading interval 2. Calculate the estimated customer energy for the previous day ( ) for each market participant for each wholesale connection point for each 30-minute trading interval by multiplying the chosen like-day energy data ( ) by the relevant regional scaling factor ( ): where t represents the 30-minute trading interval 3. Calculate the value of the estimated customer energy ( ) by multiplying the previous day estimated energy by the regional reference price ( ) for the 30-minute trading interval and the transmission loss factor ( ) for the relevant wholesale connection point: where t represents the 30-minute trading interval 4. The total energy cost for the previous day ( ) for each market participant is the sum of all the estimated energy costs for each 30-minute trading interval plus other charges such as ancillary services and fees: where t represents the 30-minute trading interval 5. The total outstandings are calculated by adding any unsettled trading liabilities to the previous day s total energy cost and deducting any current security deposit amounts (SDA) and any registered ex post reallocations (EPR): Consultation Paper 4 April 2012 Page 7 of 16

8 01-Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec-09 Performance(%) Customer Energy Purchased ($m) SETTLEMENT ESTIMATION AND REVISIONS POLICY 2.2 Estimation Performance The performance of the current like-day estimation model has been assessed by analysing the difference between the estimate and actual meter data 1. The results of the analysis for dates in 2009 are illustrated in Figure 2 below. Figure Like-Day Settlement Estimation Method Performance LIKE-DAY SETTLEMENT ESTIMATION MODEL PERFORMANCE 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% $800 $700 $600 $500 $400 $300 $200 $100 0% $0 Current Estimates (%) Customer EP ($) Figure 2 shows that the current like-day estimation model performed well between March and October 2009 due to low prices and small differences in customer energy between the like-day and the previous day. However, the performance deteriorates in the peak months of January, February, November and December due to significant differences in customer energy between the like-day and the previous day, combined with high prices. The average performance level during these peak period months is 81% compared with 87% during the other eight months of the year. On one day during the peak period (13 January 2009) the performance fell to 57%. The impact of performance in the estimation calculation to a market participant s liabilities can be further analysed through the net daily amount. The current method was tested for specific peak period days in December During these peak days the current model had overestimated the customer energy for one market participant by as much as 50% and underestimated the customer energy for another by as much as 27%. Analysis showed a clear pattern regarding the types of market participants being overestimated or underestimated. Market participants which had a significant overestimation of daily energy typically have a high proportion of industrial customers. Conversely, market participants with a significant underestimation have a high proportion of residential customers. For tier 1 retailers the current model works reasonably well as their customer energy profiles tend to correspond to regional averages. 1 The performance is measured by calculating the daily total absolute difference in energy value divided by the daily total energy value, and subtracting from 100%. The difference amounts are calculated for each participant in each trading interval for each wholesale connection point. Consultation Paper 4 April 2012 Page 8 of 16

9 2.3 Estimation Process Limitations The main limitation with the current like-day estimation process is the scaling factor. Using a regional scaling factor assumes that all market participants have the same demand profile as the regional average. However, the market participant s demand profile is dependent on the proportion of their energy distributed to their customers (industrial, commercial or residential). Market participants with predominantly residential customers will have a different load profile to market participants with predominantly industrial customers. The latter will consequently have less or no energy fluctuation during peak periods. On a day with high regional demand the current like-day estimation model will overestimate energy for market participants with predominantly industrial customers and underestimate energy for market participants with predominantly residential customers. There are other factors that contribute to the limitations of this model: Like-day The criteria for selecting the like-day are explained in Section 2.1. The correctness of this selection is tested during days when there is a large customer energy deviation between the like-day and the previous day due to situations such as customer transfers between participants. This problem will remain as long as the estimation is based on a like-day (which represents the closest available reflection of an energy profile for customer energy). Generator load profiles Generation profiles of generators whose energy is not sold directly to the wholesale market, for example embedded generators, can significantly impact the demand at a connection point. These generation profiles are not reflective of the regional demand profile and as such can result in the connection point demand exhibiting low levels of correlation with the regional demand. Data quality There is a possibility that inaccurate or corrupt data could be used in the estimation process. This inaccurate data can occur if an interval meter data is corrupt. The corrupt data may be replaced using approved validation techniques between the metering data provider (MDP) and AEMO. 2.4 Conclusion The performance and issues considered in Sections 2.2 and 2.3 can lead to a prudential assessment that is not as accurate a reflection of a market participant s true outstandings position as possible. An overestimation may cause a market participant to breach their trading limit unnecessarily; conversely an underestimation of a market participant s position could lead to an exposure to the NEM over and above the credit support held by AEMO.? AEMO seeks stakeholder views on the current settlement estimation methodology, its effectiveness, performance, limitations and any further comments they wish to contribute. Consultation Paper 4 April 2012 Page 9 of 16

10 3 Proposed Changes to Prudential Estimation AEMO has analysed a series of alternatives to the current prudential estimation process for customer energy (see Appendix A) and is proposing to implement the following: A hierarchy of information to ensure the best quality information is used in the calculation of total outstandings. An initial bill run performed two business days after the settlement day to coincide with MDP data provision requirements. The use of wholesale connection point scaling factors for like-day estimation. A refinement to the current estimation calculation to better reflect how an individual market participant s profile moves with a change in the regional profile. Each of these aspects of the improved process is described in more detail below. 3.1 Hierarchy of Information to be used in Prudential Estimation AEMO s recommended change to the prudential estimation process for customer energy is to use a range of additional data that may be available, and to process this data according to accuracy. It is important that any data used in the assessment of a Market Participant s prudential position should be the most accurate available. At the very least the introduction of a new estimation process should not degrade the estimation data. To manage this AEMO is proposing the application of a hierarchy in the estimation process, which is based on the following data sources in decreasing preference: 1. Actual meter data (discussed in following sections). 2. TNI SCADA data (if there is a single market participant consuming energy at the TNI). 3. Estimated data generated for each NMI by the MDP in accordance with MDP procedures. 4. Estimated data generated for each NMI by MSATS in accordance with the MDM procedures. 5. Estimated data based on like-day energy and wholesale connection point scaling factors (TNISFs) 6. Estimated data based on like-day energy and regional scaling factors with a calculation refinement 7. Estimated data based on like-day energy and regional scaling factors (current methodology) AEMO will develop a set of data quality, market participant, and wholesale connection point data parameters and validations to determine which level in the hierarchy is appropriate.? AEMO seeks stakeholder views on a hierarchy of information approach to the data used in the settlement estimation and prudential assessment process. 3.2 Initial Bill Run Two business days after a settlement day approximately 95% of metering data has been submitted by MDPs. This data is not currently used as part of the daily prudential assessment process until the fourth business day following the end of the billing week, after the preliminary billing run is performed. The proposed alternative would enable AEMO to process a new initial bill run each calendar day, utilising all available actual metering data for the settlement day two calendar days prior. The results of this new initial bill run will replace the estimates for that settlement day and the new Consultation Paper 4 April 2012 Page 10 of 16

11 outstanding amount will be recalculated to be part of the next day prudential assessment. It is not AEMO s intention that this initial bill run will lead to an increase in the current level of service for data delivery required by the MDPs. 2 In the case of metering data being unavailable or missing, a level of validation will be performed to ensure that the outcome is at least as good as the current estimation process. The hierarchy of information process will be used to ensure that any changes to the prudential estimation do not result in an outcome that is poorer than the status quo as described in Section 3.1. In terms of prudential assessment, the changes from the current process (see Figure 1) are summarised in the diagram below. It should be noted that other options for timing of an initial bill run could be considered, these include: Run the initial bill run two business days after the settlement day to align with the data validation requirements under the MDP service level procedure. This option would lead to a greater number of estimate bill runs in the prudential assessment. Run an initial bill run two calendar days after the settlement day with the bill run to cover the three days up to and including the settlement day two calendar days ago. This option would see three initial bill runs for each settlement day and increase the amount of actual data being captured. Run an initial bill run two calendar days after the settlement day with the bill run to cover those days up to and including the settlement day two calendar days ago which are not yet included in a preliminary bill run. This option would typically see between 3 and 8 initial bill runs for each settlement day, depending on the day of the week, and increase the amount of actual data being captured. Figure 3 Prudential Assessment Schedule Assuming an Initial Bill Run Analysis of Initial Bill Run In analysing the performance of the initial bill run it was assumed that the quality of the initial data is the same as the preliminary data was chosen for analysis as it was a very volatile year with a total of 764 prudential breaches under the current settlement estimation process. November was the worst month in 2009 with the total dollar value of trading limit breaches approaching $1 billion. If AEMO received the actual meter data immediately each day 810 breaches would have been identified. However, receiving this is not considered practical as it imposes a very stringent timeframe on the MDPs to supply AEMO with the required data. Implementing an initial bill run would have resulted in an increase in estimation accuracy with 790 of the 810 actual breaches being identified. This compares with 764 breaches being identified 2 Service Level Procedure: Metering Data Provider Services Category D for Metering Installation Types 1,2, 3, and 4 Consultation Paper 4 April 2012 Page 11 of 16

12 Performance(%) SETTLEMENT ESTIMATION AND REVISIONS POLICY under the current estimation model. These results are based on the assumption that the initial data is as good as the preliminary data. This is not always the case as some of the data will not be submitted two days after settlement day due to timing constraints Initial Bill Run Accuracy AEMO has performed testing on the accuracy of initial bill run data, to ensure that the use of such data will not degrade the prudential estimation accuracy. Based on the interval value for each market participant at each connection point, the following graph shows that the initial data significantly improves performance 3 to between 98% and 99% from an average performance of 88% on the estimated data. Figure 4 Initial Bill Run Data Accuracy 100% MODEL PERFORMANCE COMPARISON Estimated Data against Initial Data 95% 90% 85% 80% 75% 24/08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/2010 ESTIMATE INITIAL? AEMO seeks stakeholder views on the proposal to execute an initial bill run two calendar days after settlement day to utilise all available metering data. In particular AEMO seeks views on the prudential implications of an initial bill run initial bill run data accuracy timing and coverage of the initial bill run 3.3 Wholesale Connection Point Data Scaling Factors (TNISFs) This approach utilises energy data at the wholesale connection point level to calculate scaling factors for the like-day estimation model where appropriate. The like-day wholesale connection point data is SCADA data retrieved from the Energy Management System (EMS). Wholesale energy data that flows in and out of the connection point can be available immediately from EMS. This allows estimates to be calculated based on the profile of the customer energy at each connection point. This alternative significantly benefits market participants with predominantly industrial customers that are represented uniquely by a connection point. It will also benefit market participants that purchase the majority of their wholesale energy at a connection point. 3 Performance is measured as per Section 2.2 Consultation Paper 4 April 2012 Page 12 of 16

13 It is important to note that the energy data in EMS and the revenue meters are not exactly matched as EMS captures the energy at an instant point in time. Nevertheless, EMS and revenue meters are highly correlated and therefore a scaling factor based on the EMS data for the previous day and the like-day is appropriate for estimating the wholesale energy profile at that particular connection point. Under this alternative the like-day energy will be multiplied by its connection point scaling factor (TNISF). The TNISF is calculated by dividing the total net energy purchase at each connection point on the previous day by the like-day, as illustrated in the following formula: A market participant s total estimated regional energy for each interval would be an aggregation of the estimated energy determined for that market participant at each connection point for each interval. AEMO s analysis of the TNISF shows that, in conjunction with the calculation refinement where TNISFs are inappropriate, there is an improvement in the absolute error on a peak day of over 50%. Analysis of the wholesale connection point scaling factors assumes that a perfect mapping exists between the wholesale connection points and the SCADA points for which there is data in EMS. Currently, a significant number of connection points are not well mapped in EMS. AEMO considers that in the initial implementation of TNISFs only a select number of sufficiently matched connection points will be used. However, a process to map further connection points will be adopted so that this scaling factor can be applied to an increasing amount of data estimates. It should be noted that some wholesale connection points (virtual TNIs) are not expected to be included in this approach.? AEMO seeks stakeholder views on the use of wholesale connection point data in the calculation of a scaling factor for like-day energy and a phased approach to the implementation of these factors based on the sufficiency of mapping in EMS. 3.4 Refine Like-Day Estimation Method The current like-day prudential estimation model estimates half-hourly customer energy for the previous day based on regional scaling factors (calculated from regional dispatch data) and customer energy from a like-day. This calculation implies that the regional scaling factors are equal to a market participant s energy scaling factors for each trading interval at each connection point as shown below: For most market participants this assumption is not correct as they have a different load profile to the regional load depending on the energy distribution to their customers Proposed Calculation It is possible to refine the current like-day estimation model by weighting up or down the regional scaling factor to better fit the actual market participant scaling factor. This can be achieved through linear regression analysis of the actual market participant scaling factor at each connection point and the regional scaling factor. This analysis generates two parameters and for each participant at each connection point where is the sensitivity parameter of the regional scaling factor and is a constant. The scaling factor for each market participant can then be altered as follows: Consultation Paper 4 April 2012 Page 13 of 16

14 In the performance analysis conducted the and applied for the estimation were the average from the most recent preliminary bill run performed by the beginning of the current billing week. For example, to estimate the energy purchase on the 1 December 2009 (Week 49), the new model would use the average and from Week 47. The results of this refinement to the methodology show that market participants with predominantly industrial customers have close to 0 and close to 1 due to their consistent level of energy purchases. Market participants that have a customer demand similar to the regional demand will have a close to 1 and close to 0. AEMO s analysis of this refinement shows an improvement in the absolute error on a peak day of approximately 35%.? AEMO seeks stakeholder views on the refinement of the current like-day estimation model and in particular the weighting of the regional scaling factor to create an improved market participant scaling factor. 3.5 Other Issues? Are there any other issues that AEMO should take into consideration or include in their update of the Settlement Estimation and Revisions Policy? Consultation Paper 4 April 2012 Page 14 of 16

15 Appendix A Summary of Prudential Estimation Review AEMO conducted a review of the current process of estimation for prudential purposes, and considered a range of options to improve the process. The table below summarises the options that were considered: Table A1 Prudential Estimation Options Option Description Like-Day Estimation (Current Customer Method) Refine Like-Day Estimation Segregate Energy Data Into Interval and Consumption Segregate Energy Data Into Large and Small Wholesale Connection Point Data Scaling Factors Initial Bill Run Immediate Data Provision The current like-day estimation process where customer energy is estimated using a scaling factor derived from the like-day regional dispatch data. Weighting the regional scaling factor between the like-day and previous day so it can be better adapted to calculating a market participant scaling factor. Segregating the energy data into interval and consumption data to predetermine or scale the energy data of the market participant at the connection point. Segregating the energy data into large and small, based upon the NMI classification, to identify industrial, commercial and residential customers to improve the market participant energy profile. Use of wholesale connection point data in the like-day estimation model to calculate a connection point scaling factor. Use of actual metering data 2 business days after the settlement day Use of actual metering data immediately after the settlement day A1.1 Refine Like-Day Estimation AEMO performed linear regression analysis to investigate the correlation between actual metered energy and the regional and connection point energy. This was performed at a participant and connection point level for each trading interval over a number of sample days of prudential significance. The analysis clearly demonstrated trends based on the proportion of consumer types for which the participant was responsible. Retailers with a large proportion of residential customer tended to show a higher sensitivity to changes in regional and connection point energy. Using the factors derived from the analysis, AEMO performed a back-cast of estimates for several prudential days. The performance was then assessed against the current estimation method in terms of the absolute error of the estimate for each participant and connection point in each trading interval. The results indicated an improvement in performance of approximately 35%. A1.2 Segregate Energy Consumption Based on the analysis which confirms the distinct patterns of correlation for different consumer types, the option was analysed of segregating data based on consumer type. There were two segregation techniques that were tested: Based on the type of meter data, specifically interval (explicit data for each trading interval) and consumption (a single reading for a period, which must be profiled prior to settlement). Based on the NMI classification of Large or Small. Segregated data for a number of sample days was extracted, which was then used in combination with the refined like-day estimation previously modelled. The segregated data was used in the determination of linear regression analysis, with the intention of refining the correlation factors. Consultation Paper 4 April 2012 Page 15 of 16

16 When tested over a number of days, neither segregation techniques demonstrated any significant improvement. A1.3 Wholesale Connection Point Data Scaling Factors AEMO currently utilises EMS data for the estimation of generation output for all scheduled and a number of non-scheduled generators. Data is also available in EMS which relates to wholesale connection points, although there is currently no explicit mapping. Analysis was undertaken into the use of wholesale connection point data as a means of performing a lower level of granularity in the determination of scaling factors. In effect a scaling factor would be calculated for each wholesale connection point, based on the ratio of current to like-day demand. This would then be used to calculate an estimate of energy for each participant at that connection point. In addition there are a number of connection points that only have a single responsible participant. The use of EMS data would enable this energy to be estimated with a high degree of confidence. AEMO modelled the wholesale connection point scaling factor across a number of sample days, with results showing a noticeable improvement in estimation performance. A1.4 Initial Bill Run AEMO performed an analysis of the meter data provided by MDPs prior to deadline for preliminary statements, and whether this data could be used in the prudential process. The results indicated a large proportion of interval meter data was available to AEMO after 2 business days. Analysis was also undertaken to assess the improvement in prudential assessment by using actual meter data for settlement days more than 2 business days prior to the current prudential day (referred to as an initial bill run). The performance was assessed by comparing the number of trading limit breaches that occurred under the following scenarios: Number of actual trading limit breaches (based on the current like-day estimation) Number of breaches that would have occurred if actual metering data was available for the entire outstanding period (this is the perfect result) Number of breaches that would have occurred by using an intial bill run The results indicated a major improvement in prudential performance by using the initial bill run. This was both in terms of the absolute number of breaches identified, but also in avoiding breaches that occurred due to over-estimation. A1.5 Immediate Data Provision AEMO considered the options of using actual meter data in the daily prudential process. The current timing requires any prudential actions to be made prior to 10:30am Sydney time, therefore it would be necessary for AEMO to have produced an updated prudential position by 8:00am. Given the required processing time, data would need to be available no later than 6:00am. Given the large number of retail connection points, it would be practically impossible for all meter data to be retrieved and submitted to AEMO in the necessary timeframes. However an option would be to have data provided for a subset (for instance the largest 10,000 NMIs), with the remainder of the NMIs being estimated. The current MDP data delivery requirements do not involve the provision of data with such short timeframes, and it is likely the cost to the industry of this level of delivery performance would be substantial. AEMO considers this option may be beneficial in improving the prudential accuracy in the future, and intends to perform further work including initiating discussions with MDPs and other stakeholders. Consultation Paper 4 April 2012 Page 16 of 16