Network Demand and Customer Connections Forecasting Procedure Power Networks

Transcription

1 Network Demand and Customer Connections Forecasting Procedure Power Networks 1 Forecast Purpose Forecast Scope Reference Characteristics of a robust forecasting process Forecast improvement Forecast documentation Forecast reconciliation Outline of demand forecast process Demand forecast overview Outline of customer connections forecast process Customer connections forecast overview Timing and change control Information requirements Load forecast timing Network forecasting data repository Load Log Regional forecast Spatial demand forecast processes High Voltage feeder forecast Determine the real load history Extract non growth related load changes Project future base demand from the historical data Forecast demand growth Feeder forecast calculations Zone substation forecast Determine the real load history Extract non growth related demand changes Weather correction of demand data Project future base demand from the historical data Forecast demand growth...16 Approved by: Prepared by: Issue Date: June 2013 Status: Approved Paul Ascione Group Manager Strategy and Planning Jim McKay Manager Network Development and Planning File No: F2012/ Version: 3.0

2 8.2.6 Zone substation forecast calculations Transmission system demand forecast Customer connections forecast Forecast reconciliation Forecast distribution Further Information Appendix 1 Demand Forecasting Document Use and Process Flow Attachment 1 Weather correction A1.1 Weather variables...28 A1.2 Demand data exclusions...28 A1.3 Statistical correlation between demand and weather...29 A1.4 Process to correct historical demand data for daily maximum temperature...30 Attachment 2 Diversity of demand... 32

3 1 Forecast Purpose The network demand and customer connections forecasts underpin Power and Water Network s capital and operating expenditure programs, by highlighting where network constraints are expected to emerge. 2 Forecast Scope The overall objective of the network forecasting process is to develop the projections of peak demand and customer connections shown in Table 1. Table 1 Network demand and customer connections forecasting requirements Forecast Period Purpose Region 10* years Overall demand, based on economic considerations, for comparison with corporate forecasts and lower level forecasts. Transmission substations (132/66 kv), transmission connected customers and generators To plan the development of the transmission network and existing and new transmission connected substations. Zone substations (132 or 66/22 or 11 kv) High Voltage feeders (22 or 11 kv) Customer connections (all voltages) *Current year plus. ie = * years To plan the development of the subtransmission network and existing and new subtransmission connected zone substations. To plan the development of the High Voltage network. The primary demand forecasts above form the basis of other forecasts used for planning at different levels within the network and for reconciliation with the Corporate Regional forecasts. The customer connections forecast is reconciled with the tariff forecast used for estimating network revenue. 3 Reference This document should be read in conjunction with the Power and Water Network Technical Code and Network Planning Criteria. 4 Characteristics of a robust forecasting process An adequate, soundly based approach robust forecasting process should embody the following characteristics: Secure and reliable data collection and storage, to ensure the integrity of the base data from which the forecast is prepared; Identification of the exogenous drivers of electricity consumption and demand; A forecast modelling process which will provide consistent and repeatable results, which accommodates all of the material drivers of consumption or demand and has a level of sectoral breakdown consistent with the identified drivers;

4 Quantification of the historic relationship between the identified exogenous drivers and corresponding changes in electricity consumption and demand; Statement of the means by which the exogenous drivers will be calculated or estimated to produce the forecast; Where there can be a degree of judgement in determining factors influencing consumption or demand, definition of the factors to be considered in making that judgement; Production of electricity forecasts by inputting projections of those exogenous drivers into forecast models; Ongoing monitoring and review of the forecasts against outcomes, with the view to process improvement; A level of documentation sufficient to be used by staff as a work instruction and by third parties to review the appropriateness of the processes; and Appropriate governance and approval processes on key input assumptions, process and outputs. The network peak demand forecasting process employed by Power and Water Networks is very similar to that employed by other Australian distributors. The process makes use of the available relevant data and has been logically structured and documented. This written procedure has been designed to ensure the quality and repeatability of forecast outcomes. The network peak demand forecasting process is therefore an appropriate basis from which to construct capital and operating cost forecasts, which reasonably reflect a realistic expectation of the requirement to meet or manage demand growth on the network. 4.1 Forecast improvement An understanding of the factors driving peak network demand and the duration of that demand is the key to improving the forecast process so that emerging constraints can be better anticipated and managed, using network and non-network approaches. In most cases, these drivers will have been anticipated and will fall within expected bounds. Where peak demand growth falls outside expected bounds (eg. if the growth rate were to differ by more than a few percent from recent trends) or appears to be the result of a change in drivers, special investigations shall be undertaken as part of the forecast process, and the factors deemed to be driving peak demand growth determined. The objective is to improve the forecast accuracy by identifying the main drivers of peak demand, any changes in these drivers and the factors behind those changes, and the specific level of growth expected, either at an overall or local level. 4.2 Forecast documentation The forecast process is to be accompanied by documentation sufficient to ensure the forecast is repeatable and able to withstand external and regulatory scrutiny. This includes the documentation of any associated investigations on peak demand drivers in section Forecast reconciliation The network demand forecast shall be reconciled as described in section 10 to demonstrate its internal consistency. It shall also be reconciled with the Regional Corporate level forecasts through the application of suitable diversity factors to lower level inputs.

5 4.4 Outline of demand forecast process The formal reporting of forecasts is made at four levels: The Regional level, for comparison with Corporate and lower level forecasts; The transmission level, for the planning of the transmission network; The Zone substation level for the planning of substation capacity; and For High Voltage feeders, to plan their capacity. The latter two of these forecasts are normally termed spatial demand forecasts. The forecasting process is illustrated in overview form in Figure 1 and described below. Figure 1 Network Demand forecast process Demand forecast overview The forecasting process comprises the following basic steps: 1. Collate and update input information; 2. Determine the real load history; 3. Extract non growth related loads; 4. Correct for weather conditions if required and extrapolate a trend line;

6 5. Consider adjustments for changes in key drivers; 6. Add back non growth related loads; and 7. Reconcile/validate the forecast. Each of these steps is explained in detail in subsequent sections 6 to 9 of this document. 4.5 Outline of customer connections forecast process The customer connections forecast process is similar to the demand forecast process but contains a smaller number of steps. The process overview is shown in Figure 1. Figure 2 Network customer connections process Customer connections forecast overview The customer connections forecasting process contains the following steps: 1. Collate and update input information; 2. Correlate with economic drivers 3. Project historical information with regard to forecast economic trends; 4. Consider adjustments for known developments such as housing lot releases or developments; 5. Reconcile/validate the forecast.

7 5 Timing and change control The network forecasts are produced annually, after each summer or wet season peak demand period, thereby incorporating the most up-to-date information on consumption trends. The approved forecasts shall be completed by 31 August each year and available for inclusion into the Network Management Plan. These forecasts shall be used for the purpose of network analysis by planning personnel and as the basis to determine the capital and operating expenditure forecasts for Power and Water s network. During the course of the year, the forecasts may be found to be inaccurate due to events that may occur, or by calculation issues. This can have a direct impact on the current forecasts and therefore the timing of or need for proposed projects. In order for the forecasts to be changed during the course of the year, the following must occur: The Senior Manager Network Planning and Development shall be advised of the proposed change to the forecast; The proposed change must be demonstrated to be material and not adequately captured in the current forecast (which is principally derived from historical data); All affected feeder and substation forecast demands shall be assessed using the new information, to ensure a consistent result; If a new forecast is issued, all relevant staff shall be advised by of the change; and If such a change is sufficiently material to affect Regional forecasts, the Manager Network Planning and Development shall advise the General Manager Networks.

8 6 Information requirements This section details the information which shall be used in the demand forecast process. Load records: May include the following sources of data: o Half hourly records from metering used for operational purposes (termed Supervisory Control and Data Acquisition or SCADA records) at zone substations and other network locations; o interval metering records for meters installed at locations within the network; o interval metering records installed at major and minor customers premises; o from meters at minor substations which record the maximum demand at the substation; and o portable metering survey equipment used to record the demand. Spot loads: Refers to committed 1 single load increases of above 1000 kva or multiple related load increases totalling above 1000 kva. This information is gathered by the Distribution planners with input from Customer Connections and is stored in a database or Load Log against individual feeders and zone substations. Temporary switching and load transfers: Refers to planned 11 and 22 kv load transfers between HV feeders or between zone substations. The information is gathered by the Distribution and Subtransmission planners from control room records and is stored in the database against individual zone substations. Known network augmentation or reconfiguration: Refers to committed projects that will impact on the capacity of a feeder or zone substation to supply the associated load area, such as new zone substations, additional zone substation transformers, upgraded switchgear or feeders etc. This information is gathered from plans prepared by Distribution or Subtransmission planners. These events may be reflected in existing zones as spot load increases or load transfers and are stored as above. Embedded generation and capacitors: Are identified by the Distribution or Subtransmission planners and are treated either as negative spot loads, or in the case of small generators like solar PV, as negative load growth. Other relevant information such as economic activity, air conditioner penetration, demographics of individual zones, building construction activity etc. is not quantified but considered as described in sections and below. 6.1 Load forecast timing Forecast spot loads, temporary switching or system augmentation may occur anytime throughout the year and is generally segmented by financial year. Given the purpose of this procedure is to forecast peak demand, loads that occur late in a financial year may be delayed from impacting the actual peak demand until the following financial year. As such these loads should be forecast according to when they will next impact the peak demand. 1 A committed load is one for which a connection application has been lodged with Power and Water.

9 Practically, loads that are forecast to occur in the April June quarter should be attributed to the following year s peak demand. 6.2 Network forecasting data repository The data used for spatial demand forecasting are derived from Supervisory Control and Data Acquisition (SCADA) records, and may be compared to, or supported by customer and other energy meters as necessary. Spatial demand forecasting is carried out primarily with Microsoft Excel. The electronic versions of the forecasts are stored in TRIM, Power and Water Corporation s Electronic Document Management System. 6.3 Load Log The Load Log is a MS Excel spread sheet that records all loads expected to impact the Network in the coming years. Certainty around the likelihood of these loads proceeding is generally limited to 6-12 months in advance. Typically loads that are beyond this window are less likely to proceed, or at least not in the magnitude provided by the developer or customer. In addition to the uncertainty of the load, expected maximum demands can be calculated with differing methods and, in the PWC experience, tend to be overstated. To overcome the excessive maximum demand the Load log contains a PWC estimate of the demand as is expected to be experienced from the HV feeder, after some diversification. This figure is determined based on engineering assessment and comparisons to other similar installations if possible. This is the demand figure used for the forecast (only). To adjust the number of new loads expected in the outer years of the forecast, the resulting spatial forecast will be scaled at the regional level to provide a long term trend inline with other indicators. Zone substation and feeder forecasts need not be scaled as the diversification is less and forecasts are reviewed annually.

10 7 Regional forecast Power and Water prepares regional forecasts for the three separate systems (Darwin- Katherine, Tennant Creek and Alice Springs). These forecasts moderate the growth trends in historic demand with the growth expectations of a number of economic indicators that have been demonstrated to drive changes in electricity demand. The process used to determine the regional demand forecast is as follows: Known large step changes in demand, (spot loads) such as those arising from the connection of large customers or embedded generators, are used to adjust the historical recorded demands; Historical maximum demand records are corrected to the Standard Weather Maximum Demand (SWMD), using the process described in Appendix 1; A trend line and the historical growth rate are established from the SWMD. This is used as the starting point for demand projections (ie. forecast trends are extrapolated from the SWMD rather than the recorded peak demand in the most recent summer or wet season); The projection of regional demand is made after the consideration of economic indicators that may include, but are not limited to, the following: o o o o o o o The historical Northern Territory Gross State Product (GSP) and Treasury GSP projections 2, 3 ; Historical and forecast population growth; Dwelling starts; Historical network connections and connection inquiries; Trends in major appliance penetration and consumption patterns; Trends in energy conservation; and The outcomes of any customer surveys and load research. 2 3 The correspondence between the historical growth of the Darwin-Katherine system and the GSP is 90%. The annual variation in GSP may be used to construct high and low forecast scenarios.

11 The outcome of this process is the regional demand forecast. As an indication of the required outcome, the forecast for is shown in Figure 1. This forecast is normally extrapolated for a period of 10 years. Although the difference is very small, for this forecasting procedure percentage growth rates are determined arithmetically, rather than geometrically. Figure 3 Indicative regional forecast for Darwin Katherine 360 Maximum demand, MW Raw MD Temp corrected MD temp corrected trend Historical trend 2.51% P10 Forecast trend 2.75% High 3.25% Low 2.25% Financial year ending

12 8 Spatial demand forecast processes Power and Water undertakes spatial demand forecasts at two separate but aligned levels of the network. These are: The High Voltage feeder forecast; and The Zone substation feeder forecast. Other forecasts are developed for the purpose of planning the development of particular sections of the network and for the subtransmission and transmission networks. These are built up from the relevant elements of the above two forecasts. 8.1 High Voltage feeder forecast The demand of each 22 kv and 11 kv feeder is forecast using the process illustrated in Figure 4. The base data for the forecast is derived from the historical SCADA records for the feeder concerned, where these are available. Alternatively, maximum demand indicators located at Zone substations may be used Figure 4 - High Voltage feeder forecast Feederload,Amps Illustra ve Rawdata Loadcorrec on Corrected 120 Trend 100 Rawprojec on Adjustedprojec on Year Figure 4 displays the forecast process, which is undertaken in year 0 for a period of five future years. The following corrections sections describe the steps involved Determine the real load history The load history for feeders is determined on the basis of each feeder s maximum demand for the summer or wet season (November to March inclusive), extracted from SCADA records or other sources of information. Corrections are made to the load history as part of the feeder forecast process, to allow for known load transfers that were in place between HV feeders at the time of the peak demand are identified from inspection of the SCADA history and confirmed from operational records. In the example, load transfers are shown in years -5 and -3;

13 8.1.2 Extract non-growth related load changes Corrections are also made to the load history, to allow for: Any known abnormal fluctuations in large customer demands at the time of peak load would be corrected; The demand would be increased to exclude medium and large scale embedded generation in service at the time of peak demand. Unless such generation is subject to a network support agreement it would potentially not be available for service. Smaller embedded generators are not separately considered but form part of the historical demand records and are thus included in the growth trends; Load which had been curtailed, potentially at the request of Power and Water Networks or a Retailer, or because of system limitations, would be estimated and added back to the historical data; and Any material known block load increase would be identified. In the example, a block load is connected in year These corrections are all made to provide the Corrected load history (red) Project future base demand from the historical data Ordinary Least-Squares (OLS) regression is then used to fit a trend line (green) to the Corrected load history. The trend is projected for the duration of the forecast period (dashed green). As with previous the regional forecast, percentage growth rates are determined arithmetically, rather than geometrically. Some modification of this process may be required for recently commissioned or rearranged feeders, where growth rates may need to be determined from the original sources of the demand Forecast demand growth In formulating the spatial forecasts, further adjustments to the demand projections at individual locations may need to be made, to accommodate the following factors: To add back any block loads excluded from the load history, in section 8.1.1; Network configuration changes, which can result in the transfer of load between locations and in changes to the proportion of demand met by individual components of the network; Planned network load transfers; Customer driven changes to historical trends (such as changes in major appliance consumption); Lot developments proposed by developers (which are reconciled against the forecast number of new customer connections); New large customer connections and demand, either committed or highly probable; and Large customer reductions or closures. 4 The block load would form part the base projection in future years, after it had been included in each year of the load history.

14 This trend line thus needs further adjustment to establish the forecast of feeder demand. In the final two stages of the example: The block load, which was excluded before establishing the trend line, is added back; and Material committed load changes (not included in the base load growth) are made. In this example, a further load is added in year 2. The outcome of this process is the Adjusted projection of HV feeder demand (in black) Feeder forecast calculations The HV feeder peak demand data and forecast calculations described in sections to are contained within a spreadsheet. Once finalised, this spreadsheet becomes a controlled document. 8.2 Zone substation forecast A similar but slightly more detailed process is followed at the next network level, in developing the zone substation forecast. The following additional steps are involved in the development of the forecast for each zone substation. This is illustrated with reference to Figure 5. Figure 5 - Zone substation forecast 1,900 1,800 1,700 Illustra ve Zonesubsta onload,amps 1,600 1,500 Rawdata Loadcorrec on 1,400 Rawcorrected 1,300 Tempcorrec on Tempcorrected 1,200 Trend 1,100 Rawprojec on Adjustedprojec on 1, Year Determine the real load history The real load history of each zone substation is determined through the following process: The peak load is monitored during the summer or wet season for a sufficient duration to ensure that the effects of both early and late weather extremes (and therefore the seasonal peak) have been captured. This is ordinarily the months of November to March, inclusive;

15 The required months of raw SCADA load data are downloaded for each zone transformer and for the diversified zone total, in order to process loads on a seasonal basis. If the (Primary side MW/MVAr) SCADA data is not available, or determined to be unsatisfactory, other metering data is requested if available; The top ten maximum demand days from this output are reviewed, and whenever an abnormality is apparent, the outputs for each transformer and each substation are scanned for gaps, step changes and signs of abnormal switching between zone substations, etc. If the abnormality is the result of switching, the load is adjusted to compensate; and The daily maximum temperature for the corresponding period and Region is obtained Extract non-growth related demand changes The process in section is followed for each zone substation, to remove the effect of abnormal loads on the historical data Weather correction of demand data An additional step applied to the zone substation demand forecast is the weather correction of the historical load data, following the removal of abnormal load and generator fluctuations. This process is described in Attachment 1. The maximum daily temperature has been found to exhibit good correlation with the maximum daily system demand on those occasions of high network demand, namely summer or wet season working weekdays. This regional level correspondence is used to temperature correct the historical spatial demand data, as follows: The maximum daily temperature is recorded for each day of maximum demand forming part of the load history; A proportionate adjustment is made to the daily demand, to obtain the SWMD (or P 50 demand), using the %/ o C correction factors of Table 2; For Zone substations that are radially supplied, a proportionate adjustment is made to the daily demand, to obtain the demand which would have occurred at the average daily maximum P 10 temperature. Again, this uses the relevant correction factor from Table 2 The temperature adjusted historical demand is used for the projections in the following section This adjustment and the normalised results are depicted in black in Figure Project future base demand from the historical data As with the feeder forecast, Ordinary Least-Squares (OLS) regression is then used to fit a trend line (green) to the Corrected load history. The trend is projected for the duration of the forecast period (dashed green). As with previous the regional forecast, percentage growth rates are determined arithmetically, rather than geometrically. Some modification of this process may also be required for recently commissioned or rearranged zone substations, where growth rates may need to be determined from the original sources of the demand.

16 8.2.5 Forecast demand growth As with the feeder demand forecast, further adjustments to the demand projections at individual locations may need to be made, to accommodate the range of factors described in Where such factors are considered to require adjustment of the trend they shall be documented. This trend line thus needs further adjustment to establish the forecast of feeder demand. In the final two stages of the example: The block load, which was excluded before establishing the trend line, is added back; and Material committed load changes (not included in the base load growth) are made. In this example, a further load is added in year 2; The outcome of this process is the Adjusted projection of HV feeder demand (in black) Zone substation forecast calculations The zone substation peak demand data and forecast calculations described in sections to are contained within a spreadsheet. Once finalised, this spreadsheet becomes a controlled document.

17 9 Transmission system demand forecast The transmission system forecast is used for the consideration of normal and contingency flows on the network and underpins proposals to develop that network. It comprises a forecast of the load and generation at each of the existing and new connection points to the transmission network. The process used to determine the transmission system forecast is as follows: Existing and known generation developments that, taken together, broadly match the regional demand forecast described in section 10 are determined; Known step changes in demand, such as those arising from the connection of new transmission customers or changes in demand for existing customers, are determined; The forecast of zone substation demands described in section 8.2 is used as the basis to determine the demand at each connection point to the transmission network. The demand of the relevant zone substations normally supplied from each transmission connection point are aggregated, with an appropriate allowance for the diversity of their demands determined from comparison of SCADA records of individual zone substation demands and the transmission connection point demand. Average Zone substations power factor are determined and applied to determine MW, MVA and MVAr values for forecasting. The methodology used for the transmission forecasting is outlined in the Transmission Planning Report 2012, Trim Ref: D2012/

18 10 Customer connections forecast At this stage the input information for the customer connections forecast is derived from the tariff service availability charge volumes. Year on year changes in the historical tariff customer numbers represent the net of customer connections and disconnections and are therefore scaled up by a factor to account for new customer connections only. As Maximo is developed, the historical information on customer connections will be drawn directly from that system. The historical information on customer numbers is compared with economic indicators (Gross State Product, Dwellings and Population) to determine the correlation between the customer numbers and these drivers. The outlook for most appropriate economic driver(s) is used to guide the forecast growth in customer connections. In 2013 a good correlation was observed between the GSP and the connections (lagged by one year). The forecast outcome reflected the outlook for GSP and is shown in Figure 7. Figure 6 Indicative network customer connections forecast The customer connections forecast is used to estimate the customer connections capex and the expected level of capital contributions.

19 11 Forecast reconciliation The four primary demand forecasts that are the subject of this Procedure are: The regional forecast; The transmission system forecast; The zone substation forecast; and The HV feeder forecast Reconciliation of these forecasts shall be carried out as described below to ensure their internal consistency and to inform Power and Water s corporate forecasts. The HV feeder forecast for the feeders emanating from each zone substation shall be reconciled with that zone substation forecast; The aggregate of the zone substation forecasts in each region shall be reconciled with the regional forecast. As the transmission system forecast is built up from the zone substation forecast, its reconciliation is not required. In order for this reconciliation to be performed at different levels of the network, the diversity of loads forming a sub-component at each level need to be considered. The process described in the Attachment 2 shall be used to determine diversity factors where necessary. The objective of this reconciliation is to allow for variations to be understood and to identify where discrepancies need to be investigated. The focus of this reconciliation should be mainly to ensure that the projected growth rates are consistent between the forecasts at different levels, rather than the comparison of the magnitude of the aggregated demands with the demand at the higher level. The analysed information shall be compiled in a spreadsheet and percentage variations used to establish where material variations exist. The customer connections forecast, when derived from Maximo, will also be reconciled with the network tariff forecasts.

20 12 Forecast distribution The demand forecast should be completed by 31 August each year. The forecast shall be distributed by the Manager Network Development and Planning as a controlled document, to the following personnel: General Manager Power Networks Group Manager Strategy & Planning General Manager Strategy and Corporate Affairs General Manager Generation

21 13 Further Information If further information is required, please contact: Manager Network Planning and Development

22 14 Appendix 1 Demand Forecasting Document Use and Process Flow 1 document per ZSS, per year Document 1 Annual ZSS and Feeder Demand Review Part 1 Raw Data Part 2 FDR MD Part 3 ZSS MD Reference Data SCADA/Loads Load Log Temperature Economic data (All documents) 1 document per ZSS, per 5 years 1 document per ZSS, per 10 years Doc. 2 5yr Feeder Forecast Demand Doc. 3 10yr ZSS Forecast Demand 1 document per region, per 10 years 1 document per region, per 10 years Doc. 5 10yr Forecast Transmission Doc. 4 10yr Regional / System Forecast Document 6 Reconciliation Deliverables, including: Economic Summary GSP, Dwellings, Population (and other as used ) Summary of Reconciliation & each regional growth rates Regional LDC and Load factors curves Regional Forecast charts (each region) Zone Substation utilisation and Summary Data (such as NMP Appendix) HV feeder utilisation Transmission Line Utilisation Transmission Line Contingency Analysis

23 General Information Requirements & Process flow: Load Log (including Transfer log) One document per year Stored in: F2011/6307 Example: D2012/ Naming: 20XX Load Log for Forecasting ALL REGIONS File contains: Load / Transfer Description Date Modified Date (Qtr) Load expected Expected kva Customer Expected kva PWC Load Forecast ZSS affected (To and From) Feeder Affected Comments Annual SCADA Data (ZSS & Feeder Loads) Download from SCADA system in a.csv file. Raw data is transferred to individual substation sheets for further analysis. Downloaded.csv file is not saved. File contains: Hourly data (Date and Time) HV feeders (AMPS) including: Capacitor Banks HV feeders (XXX) for incoming (MV) transformer System Loads One document per year Stored in: F2005/12337 Overwrite: D2012/ File contains: Hourly System loads (Date and Time) Darwin- Katherine / Alice Springs / Tennant Creek (MW) All generating sites > 1MW ie SunPower Other Data Reference Data Reference data should be referenced when used and original documents need not be saved. Other supporting information, from any reputable source, may also be considered. Data includes:

24 Daily Max temperature data Bureau of Meteorology(BOM) GSP Historical - Australian Bureau od Statistics (ABS ) GSP Forecast NT Gov. Territory Economic Review Population Growth ABS Dwelling Approvals ABS Process Working Documents: Document 1 Annual ZSS and Feeder Demand Review One document per substation per year. Stored in: F2005/12337 Example: D2013/ Naming: XXXX Zone Substation SCADA Data 20XX-20XX This is the base document for annual forecast. The document contains the raw SCADA data for feeders and incomers as downloaded from the SCADA system. This data is then analysed to determine annual maximum demand figures for the ZSS and all feeders. The data is broken into 3 general parts: Part 1 - Raw SCADA Data From SCADA download for entire substation (only). Part 2 Individual feeder MD Individual feeder data o Notes on data errors o Temp / Permanent transfers with transfer log o Spot loads with reference to load log. o Base/ adjusted data o Comments o Charted data Profile + LDC o Peak value, date and time. Summary of summated feeders for ZSS MD (if required only) o Relevant data as shown above for reconciliation. Part 3 ZSS MD (Based on Transformers) Power Transformer (secondary) incomer data o Notes on data errors o Temp / Permanent transfers with transfer log (if external) o Spot loads with reference to load log. o Base/ adjusted data o Comments o Charted data Profile + LDC o Peak value, date and time.

25 Document 2 (5 Yrly Feeder Demand Forecast) One document per ZSS per 5 years (rolling) Stored in: F2005/12337 Example: D2012/ Naming: Feeders XXXX Zone Substation 20XX Document is based on the output of annual MD figures determined from Document 1 noted above. Document contains: Individual Feeder Forecast o Raw MD data (each year) o Temp / Permanent transfers with transfer log (if external) o Spot loads with reference to load log. o Base/ adjusted data o Forecast data (incl 5yr arithmetic growth) o Charted feeder data Zone Substation - Feeder Summary o Feeder details o Feeder ratings o Alternative feeders o Feeder utilisation o ZSS max demand (undiversified) Document 3 10 yrly ZSS Demand Forecast One document per ZSS per 10 years (rolling) Stored in: F2005/12337 Example: D2012/ Naming: ZSS XXXX (location) 20XX/XX MD Forecast Document is based on the output of annual MD figures determined from Document 1 noted above. Document contains: Zone substation Forecast o Raw MD data (each year) o Temp / Permanent transfers with transfer log (if external) o Spot loads and comments with reference to load log. o Base/ adjusted data o ZSS Max MD with date, date and daily max temperature. o ZSS Temperature corrected MD o ZSS Temperature corrected Forecast data (incl 10yr arithmetic growth) o Charted data

26 Document 4 System/Region Demand Forecast One document per region per year Stored in: F2012/4829 Example: D2012/ Naming: 20XX Network Demand Forecasting Data XX( region) Load analysis Document is based on system loads and other reference material. Document contains: Regional Spatial Forecast o Raw System MD data o System Max MD with date, date and daily max temperature. o System Temperature corrected MD o System Temperature corrected Forecast data (incl 5yr arithmetic growth) o Charted data LDC and Load Factor* *Due to the MB size the calculation of LDC is in separate sheets see D2012/ Regional economic forecast o GSP forecast o Dwellings forecast o Population forecast o Corporate Sales / energy forecast data if available. Document 5 Transmission One document per year Stored in: F2005/12337 Example:D2012/ Naming: Transmission Utilisation from 20XX to 20XX Document is based on the reconciled, diversified ZSS loads as determine in Document 5. This information is modelled to determine transmission loads under normal and contingency conditions. Document contains: Transmission Line Ratings (normal & contingency) Line Maximum Demand % Utilisation (normal) Contingency utilisation and inter-relationships of lines.

27 Document 6 Reconciliation One document per region per year Stored in: F2012/4829 Example: D2012/ Naming: 20XX ZSS to System Forecast Reconciliation XXXX(region) System Document is based on Documents 2,3 &4. Document contains: Fdr to ZSS Spatial reconciliation (per ZSS from Fdr & ZSS) o Diversified vs Undiversified ZSS MD (raw & TC) o Annual ZSS % growth comparison ZSS to regional spatial reconciliation (System level) o Diversified vs Undiversified ZSS MD (raw & TC) o Annual ZSS % growth comparison Regional Spatial to Regional economic reconciliation o Spatial scaling and reasons o Spatial forecast P50, P10 o Regional forecast P50 and high and low window o Regional forecast P10 Deliverable Documents : Process documents contain vast amount of detail, and at selected data is summarised and can be provided for information. The following list is the key deliverable information from this forecasting process and is generally included in the Network management Plan, although all supporting process documents can be available for further in depth review and development of subsequent works etc. Economic Summary GSP, Dwellings, Population (and other as used ) Summary of Reconciliation & each regional growth rates Regional LDC and Load factors curves Regional Forecast charts (each region) Zone Substation utilisation and Summary Data (such as NMP Appendix) HV feeder utilisation Transmission Line Utilisation Transmission Line Contingency Analysis

28 15 Attachment 1 Weather correction Weather correction of the spatial demand history in order to develop the forecast is a desirable improvement as to a certain degree it explains the impact of one of the most significant drivers of demand. Ideally, correction for the weather at each forecast point would be carried out, using the nearest weather station data and individual load dependencies. The intensive nature of this analysis means that it would not be possible to implement such an approach in the short term. As a consequence, the weather correction of historical data using the parameters of the Regional load is carried out. A1.1 Weather variables To this end, Power and Water s peak demand for the Darwin-Katherine Area was analysed to determine the weather variable that provided the best statistical fit of those available. The dependencies between this range of weather variables and the network demand at times likely to affect the need for augmentation of the network were analysed. The weather variables that were tested included the following: Maximum daily temperature Tmax; Average daily temperature (Tmax + Tmin)/2.; Maximum daily enthalpy (a combination of temperature and humidity intended to represent the perceived level of heat); Average daily enthalpy; Average enthalpy over two days, the day of demand and the prior day. This parameter was included to determine whether there was a build up effect in the use of air conditioning during a hot, humid spell of weather; Average enthalpy over three days. A1.2 Demand data exclusions The demand against which the weather variables were tested was the Darwin-Katherine total hourly demand for all days. The periods most likely to result in a high system demand were isolated for testing of statistical fit. The following exclusions were made: The wet season period was taken to be the months of November through to March; Weekends and NT public holidays were excluded (leaving working weekdays); The period between Christmas and 15 January was excluded, as most commercial and industrial businesses do not resume normal activity until well into the new year. These exclusions were directed at improving the statistical correlation by confining the analysis to those periods most likely to result in a high system demand and consequently the need to augment the network.

29 A1.3 Statistical correlation between demand and weather Of the weather variables described in section A1.1, the best fit with network demand was displayed by the daily maximum temperature, with a very good R 2 of The outcome of this comparison is displayed in Figure 7. Figure 7 Wet Season day demand vs maximum daily temperature, Darwin - Katherine The slope of the line of best fit in Figure 7 (expressed in percent of maximum demand per degree) is 3.20%. This relationship is used to normalise the historical demand records (after other adjustments have been made for major loads or load transfers).

30 A1.4 Process to correct historical demand data for daily maximum temperature The long-term distribution of maximum daily temperature for Darwin is set out in Figure 8. This is used to determine the Standard Weather Maximum Demand or SWMD for this system, at a temperature of 35.9 o C. Other things being equal, the SWMD will be exceeded on average during 50% of wet seasons and the demand corrected to this temperature is also known as the P 50 demand. Figure 8 - Temperature correction of demand The distribution of the temperature maxima may also be used to determine the temperature which is exceeded in 10% of wet seasons, of 36.7 o C. This is also shown in Figure 8, and the demand at this temperature would give rise to a higher P 10 demand. The slope of 3.20% per degree means that the Darwin-Katherine P 10 demand will be 2.6% higher than the P 50 demand. The P 10 demand adjustment is used for planning the supply security to radially supplied areas. Where there is no capacity available to meet a supply contingency or high load caused by high temperatures, the radial supply must also be capable of meeting that higher demand, and the P10 demand is used for planning the capacity of that portion of the network.

31 A similar process was followed for the Alice Springs system, for its summer season from November to March. As limited load data is available for Tennant Creek, the Alice Springs temperature sensitivity was used, with the local temperature distribution. sets out the temperature correction parameters for the three regions. Table 2 - Temperature correction parameters Region Darwin-Katherine Tennant Creek Alice Springs Temperature sensitivity 3.20%/ o C 2.88%/ o C 2.88%/ o C P 50 temperature o C 35.9 o C 42.3 o C 42.9 o C P 10 temperature o C 36.7 o C 43.8 o C 44.2 o C P 10 demand increase over P % 4.3% 3.7%

32 Attachment 2 Diversity of demand The following simplified example is intended to illustrate the effect of diversity of demand. Account needs to be taken of the diversity of demand in reconciling the HV feeder level forecast with the zone substation forecast. The diversity of demand between zone substations also needs to be taken into account in developing a regional forecast. In the chart below, two loads with different profiles are to be summed over a period of 24 hours: Load A has a peak demand of 15 MW and a domestic profile; and Load B has an industrial profile and a peak demand of 10 MW. The timing of the maximum demands differs, with the domestic load peaking at 17:00 and the industrial load at 13:00. When the two loads are summed for each hour, the resultant total load has a maximum demand of MW. This maximum demand occurs at 14:00, which does not align with the peak time of either of its components. The diversity factors for the two component loads are calculated from their contribution to the total load, at the time of its peak of MW. This calculation is shown in the table below. Demand at time of Maximum demand Diversity factor total peak maximum Load A /15 = 0.95 Load B /10 = 0.98 This process described above is applied with multiple components to determine the diversity factors for feeder loads that make up a zone substation total and for the zone substation loads that make up a Regional total. The process is applied to over a full year, rather than for the 24 hour period shown above, and involves the summation of component loads that may have demands taking place in different months of the year. The effect of diversity is also considered in reconciling the Region forecast with the aggregate of the zone substation forecasts.