1. CPRG Workshop 25 May 2016 Graham Phelan Framework for forecasting Constant Price Revenue Growth

2. Forecasting CPRG for the 2017 DPP reset This presentation will cover What CPRG is How we might weigh up a change in approach Challenges in forecasting in an uncertain environment Some thinking about fit-for-purpose forecasts Looking forward to the 2017 DPP reset Purpose 2

3. The forecast of CPRG reflects the increase in the quantities of the services provided from one year to the next The quantities provided are a mix of quite different measures, such as the number of connections and the amount of energy supplied This is calculated by calculating the increase in revenue if the prices in the earlier year were to be kept the same in the subsequent year What is CPRG? 3

4. 4 Previous approach to forecasting CPRG in gas

5. CPRG process map Previous Distribution approach 5 Information disclosure Supply & Demand report Information disclosure

6. Transmission process map Previous Transmission approach 6

7. Constant price revenue growth in the previous gas DPP GasNet: - 0.53% Powerco: 0.08% Vector Distribution: 0.55% MDL: -6.17 % – 0.86% Vector Transmission: -1.02% – 0.27% How is CPRG used? 7

8. Data Assessment periods not aligned with disclosure years Limited years of information disclosure (ID) data Assumptions Pricing change in assessment period one Seasonality Fixed vs Variable costs How have our forecasts performed? Analysis limitations 8

9. How has our forecast performed? The gas DPP is complicated… 9

10. Seasonality indexPowercoGasNetVector Distribution 100 (No seasonality)95.8%100.2%94.3% 10498.0%102.2%97.0% 108100.2%104.1%99.7% 112102.2%105.9%102.3% 116104.2%107.7%104.8% No evidence to suggest our previous approach is not fit for purpose 10 MBIE Distribution seasonality = 27.3% - 109.2 100.6%104.5%100.2% 109.2 CPRG forecast error

11. Process and Issues paper signalled a similar approach to forecasting CPRG – but there may be opportunities for improvement. We seek to retain forecasting approaches where they remain fit for purpose Assessment of forecast error from 2013 approach Improvements where possible We consider there may be a case for tailoring CPRG forecasts Regional forecasts? AMP forecasts? Low-cost forecasting How a change in approach might be justified 11

12. Challenges: Gas demand volatility 12 Gas is a discretionary fuel What’s happening in electricity EDB price reform Extent to which consumers act rationally Limited historical data Uncertainty over future population and GDP growth Technology changes, both gas and electricity

13. Assessing forecast error* Some thinking about fit-for-purpose forecasts 13

14. Forecast averaging can improve performance A mid-point of separate forecasts embodies more information and better formed expectations than the forecasts of a single forecaster Academic literature generally states that ex ante forecast accuracy can be substantially improved through a method of combining individual forecasts More sophisticated or complex approaches do not guarantee improved forecasts Some thinking about fit-for-purpose forecasts 14

15. Fit for purpose forecasts: Overly complex models can be poor predictors Assessing forecast performance to improve future forecasts Combining forecasts of comparable quality from different forecasters can improve forecasts Acknowledge uncertainty and limitations Looking forward 15

16. DRAFT DemandProjections_4ComCom_v02.pptm Distribution network gas demand projections Simon Coates Concept Consulting 25 May 2016 www.concept.co.nz

17. DRAFT DemandProjections_4ComCom_v02.pptm Objective of Gas Supply / Demand study GIC objective – Produce analysis on key medium to long-term NZ gas sector drivers – Produce projections of demand and wholesale prices to illustrate likely nature and potential scale of outcomes Commerce Commission (CC) used 2012 demand projections as input for constant- price-revenue-growth forecasts CC may use 2016 projections for the next control period, and has asked GIC to make Dx region forecasts available earlier than the rest of study Accordingly, 2016 projections developed in greater detail – Dx area-specific projections (i.e. Vector, 1 st Gas, Pco Central & Lower) – Greater analysis of other drivers of gas demand (pop’n growth, GDP) – But still v. simple approach! 17

18. DRAFT DemandProjections_4ComCom_v02.pptm Important to distinguish between networks Key differences between networks: – Population growth (Auckland vs the rest!) – Customer segment mixes (Res/Com/Ind) 18 Different customer segments have different gas end- uses (space heat, water heat, process heat) Relative competitive position of gas different between end-uses

19. DRAFT DemandProjections_4ComCom_v02.pptm Observed variation in historical outcomes: Total annual quantity (AQ) 19

20. DRAFT DemandProjections_4ComCom_v02.pptm Observed variation in historical outcomes: Residential (Res) 20

21. DRAFT DemandProjections_4ComCom_v02.pptm Observed variation in historical outcomes: Commercial (Com) 21

22. DRAFT DemandProjections_4ComCom_v02.pptm Observed variation in historical outcomes: Industrial (Ind) 22

23. DRAFT DemandProjections_4ComCom_v02.pptm Projection methodology 1.‘Decompose’ reported network segment splits (Res / Com / Ind) into end-use segments: Space heating, water heating, process heat, cooking 2.Project change in demand for underlying end use 3.Project extent to which gas wins inter-fuel competition for provision of end use energy service 4.Address inherent uncertainty in projections 23

24. DRAFT DemandProjections_4ComCom_v02.pptm 1) End-use decomposition: EECA-reported end-use breakdown for North Island gas consumers Residential split aligns reasonably with another data point - HEEP Concept projections take account of regional differences in average Res SH demand 24 Is 85% of Com really SH?

25. DRAFT DemandProjections_4ComCom_v02.pptm 2) Energy service projection Change in demand for energy services driven by: – Population – GDP – Energy efficiency 25 Diff projections for different networks Diff assumptions for Res / Com / Ind as to relative importance of Pop’n vs GDP as driver Diff assumptions for different end-uses (e.g. space heat, water heat, process heat, etc.) NZ Stats & Treasury used as basis for pop’n & GDP projections Concept own assumptions for energy efficiency

26. DRAFT DemandProjections_4ComCom_v02.pptm 3) Project extent to which gas wins inter-fuel competition Projection distinguishes between gas competition to meet: – new demand – existing demand i.e. fuel switching dynamic, taking into account capital replacement cycles Assumptions for relative gas success differentiates between end-uses, based on findings in Consumer Energy Options (CEO) study: – Process heatGas v. competitive – Mass-market water heatGas competitive for new-build – Mass-market space heatGas faces tough competition However, inherently hard to estimate – Consumer value ascribed to non-price ‘quality’ benefits (e.g. never running out of hot water) – Extent to which consumers act rationally in choosing least-cost option 26

27. DRAFT DemandProjections_4ComCom_v02.pptm Projected overall change in end-use segment demand 27

28. DRAFT DemandProjections_4ComCom_v02.pptm Projected change in individual network demands 28

29. DRAFT DemandProjections_4ComCom_v02.pptm 4) Factors driving inherent uncertainty Limited historical data – End-use splits (space, water, process heat) by customer segment and network – No weather-correction for historical demand (out of scope for this exercise)  Might ‘Q 0 ’ also be an issue? – i.e. should projections be driven from a weather-corrected base? Uncertainty over future population & GDP growth Uncertainty over future changes in drivers of relative gas competitiveness – In short- to medium term, particularly an issue for electricity price reform – In longer term, CO2 prices and technology change could also increasingly impact 29

30. DRAFT DemandProjections_4ComCom_v02.pptm Huge variation across New Zealand in electricity network pricing signals for space and water heating 30 Scale of variation generally not reflective of differing underlying costs More reflective of – legacy decisions. E.g. one or two-meter approach for charging for hot water original network philosophy for recovering fixed costs – other factors (e.g. low- user fixed charge regulations)

31. DRAFT DemandProjections_4ComCom_v02.pptm Electricity Authority encouraging tariff reform  change relative competitiveness of gas 31 Are the system cost implications of a water heater in these two situations really so different? Annual network-related bill for electric water heating

32. DRAFT DemandProjections_4ComCom_v02.pptm Resultant network demand projections 32

33. DRAFT DemandProjections_4ComCom_v02.pptm Resultant network demand projections (2) 33

34. DRAFT DemandProjections_4ComCom_v02.pptm Resultant network demand projections (3) 34

35. DRAFT DemandProjections_4ComCom_v02.pptm Resultant network demand projections (4) 35

36. DRAFT DemandProjections_4ComCom_v02.pptm Resultant network demand projections (5) 36

37. DRAFT DemandProjections_4ComCom_v02.pptm Comparison with forecasts in asset management plans Perhaps projections should be weighted with some proportion of AMP projections? 37

38. 38 Options being explored for the next DPP reset

39. Options being explored 39 Learnings from electricity? Further tailoring Regional focus Use of existing data Use of ID vs forecast demand Demand scenario used AMPs Investigating how we can utilise these How accurate have they been?

40. Electricity approach 40 CPRG % Δ due to residential usage % Δ due to industrial & Commercial usage Forecast Δ in # of users Elasticity of CPR to GDP F/C Δ in GDP Δ in use per residential user % of charge that is variable

41. Further tailoring – regional forecasting 41

42. Using information from supplier AMPs 42 We have signalled our intention look into relying more heavily on suppliers’ forecasts in calculating opex and capex allowances Can we utilise this in CPRG forecasting? Fixed: Number of ICPs Variable: Gas Conveyed

43. 2013 AMP forecasts of gas conveyed vs actuals 43 YOY % Change

44. What’s next? 44 Policy paper published September 2016 An opportunity for submissions and cross submissions will follow Draft determination published February 2017 An opportunity for submissions and cross submissions will follow