1. Long Term Network Development Demand Forecast for a Distribution Network David Spackman Dr. Nirmal Nair David Spackman Dr. Nirmal Nair

2. David Spackman, Dr. Nirmal Nair 2 2  Summary:  Vector needed a long-term electricity demand forecast  This will feed into their long-term plans  Designed a new long-term forecast methodology: the ‘policy-guided model’  Tested on Vector’s Auckland network and obtained promising results Long Term Network Development Demand Forecast for a Distribution Network

3. David Spackman, Dr. Nirmal Nair 3 3  Background  Forecast Model  Results  Future work  Conclusions Outline

4. David Spackman, Dr. Nirmal Nair 4 4 Vector Electricity Network  Largest distribution company in NZ  Auckland, Northern, Wellington  660,000 connections  Zone substations: 123  Distribution substations: 24,000  Planning for demand growth  10-15 year forecasts  Long-Term Forecasting:  Strategic long-term (30-70 years)  Network asset investment  Purchasing land

5. David Spackman, Dr. Nirmal Nair 5 5 Designing a Forecast Model  Many existing methods considered  Econometric  Artificial Neural Networks  Cellular Automata: Computer based Land Use Simulations  New methodology designed

6. David Spackman, Dr. Nirmal Nair 6 6 Designing a Forecast Model  Consider saturation of land From Willis, H.L., Spatial Electric Load Forecasting

7. David Spackman, Dr. Nirmal Nair 7 7 Basis for Forecast Model More customersMore demand per customer

8. David Spackman, Dr. Nirmal Nair 8 8 Forecast Model

9. David Spackman, Dr. Nirmal Nair 9 9 Future Land Use: A Policy-Guided Approach ARC 2050 Growth Strategy  Auckland Regional Council sets land use rules

10. David Spackman, Dr. Nirmal Nair 10 Processing District Plan Zoning  Zoning information readily available from Councils  Processing of this data was required  Simplification into classes defined by ‘electricity demand’ Policy-guided model: 19 Classes Auckland City Council: 36 Classes Papakura District Council: 25 Classes Manukau City Council: 138 Classes Total: 199 Classes

11. David Spackman, Dr. Nirmal Nair 11 Forecast Model

12. David Spackman, Dr. Nirmal Nair 12 Electricity Demand for each Customer/Land Use Class  The 19 simplified zone classes need to be assigned load densities

13. David Spackman, Dr. Nirmal Nair 13 Load Densities: Approach 1 1. Select feeders with one simplified zone class 2. Remove feeders not fully developed 3. Record area for each useful feeder (m 2 ) 4. Record peak load for each useful feeder (W) Open Space Res Low

14. David Spackman, Dr. Nirmal Nair 14 Determining Peak Load

15. David Spackman, Dr. Nirmal Nair 15 Calculated Load Densities Land use classLoad density (W/m 2 ) Open Space0 Residential – Low Intensity3.99 Residential – Medium Intensity5.82 Business – High Intensity86.4 Industrial – Light Intensity11.54 …… Land use classLoad density (W/m 2 ) Open Space Residential – Low Intensity Residential – Medium Intensity Business – High Intensity Industrial – Light Intensity ……

16. David Spackman, Dr. Nirmal Nair 16 Load Densities: Approach 2  Further simplify zone classes  More areas to work with Res High Res Med-High Res Med Res Low Res

17. David Spackman, Dr. Nirmal Nair 17 Approach 2 Results Residential Load Densities

18. David Spackman, Dr. Nirmal Nair 18 Load Densities: Approach 3  Smart Metering data  Finer resolution of load densities  Applicable now to some Commercial and Industrial customers

19. David Spackman, Dr. Nirmal Nair 19 Forecast Model

20. David Spackman, Dr. Nirmal Nair 20 Combining  Applying load densities to zone classes x 107,886 m 2 430.5 kW 3.99 W/m 2

21. David Spackman, Dr. Nirmal Nair 21 Forecast Model

22. David Spackman, Dr. Nirmal Nair 22 Scenario Analysis  Long-term horizon causes forecast to be scenario-dependent  A ‘Business-as-usual’ scenario to begin  Scenarios modify one or more variables of the model

23. David Spackman, Dr. Nirmal Nair 23 Scenario Analysis  Examples:  New transport links  Rezoning of land  DSM, DG  Intelligent Buildings: EMCS

24. David Spackman, Dr. Nirmal Nair 24 Scenario Analysis Scenarios classified as: 1.End-use change scenarios  eg. All Industrial peak demand increases by 5% 2. Re-zoning scenarios  eg. Tank Farm redevelopment  Industrial  Commercial + Residential 3. Micro-scale  Creation of new ‘zone’ for specific development 4. Macro-scale  Selection of areas based on other variables

25. David Spackman, Dr. Nirmal Nair 25 Forecast Model Completed

26. David Spackman, Dr. Nirmal Nair 26 Case Study Results  Auckland Region

27. David Spackman, Dr. Nirmal Nair 27 Case Study Results  Scenario Analysis: Residential Growth  High infill of zones near a major transport corridor Height = Peak Demand

28. David Spackman, Dr. Nirmal Nair 28 Verification  Found no other small area study to directly compare with, during our literature survey  However, small area should be consistent with larger area  Electricity Commission forecasts to 2040 for major industry investments  By obtaining their data we can align our forecast and check…

29. David Spackman, Dr. Nirmal Nair 29 Verification

30. David Spackman, Dr. Nirmal Nair 30 Application  Vector’s Long-term Strategic Network Development Plan  Australasian Universities Power Engineering Conference (AUPEC) Perth, Australia; December 2007  Provisionally accepted, paper to be made available through IEEE Explore

31. David Spackman, Dr. Nirmal Nair 31 Future Work  Update with new data as it becomes available  Include CBD method  Cross-checking ARC 2050 plan  Amendments current and future  Extend to:  Northern region  North Shore, Waitakere, Rodney  Wellington region  Wellington City, Lower Hutt, Upper Hutt, Porirua

32. David Spackman, Dr. Nirmal Nair 32 Future Work  Compare summed CAU results with an econometric model at CAU level:  Use population, GDP forecasts (2-20 years max- extrapolate?)  Need residential/commercial breakdown

33. David Spackman, Dr. Nirmal Nair 33 Conclusions  Investigated various forecasting methods for a long-term forecast  Designed new long-term forecast methodology  Completed a forecast for Vector’s Auckland Region  Sum of Auckland Region forecast results compare well with Electricity Commission forecast Acknowledgements  Vector  Guhan Sivakumar Auckland City Council Manukau City Council Papakura District Council

34. David Spackman, Dr. Nirmal Nair 34 Long Term Network Development Demand Forecast for a Distribution Network David Spackman Dr. Nirmal Nair David Spackman Dr. Nirmal Nair