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Long Term Network Development Demand Forecast for a Distribution Network

Long Term Network Development Demand Forecast for a Distribution Network

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Long Term Network Development Demand Forecast for a Distribution Network

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

  2. Long Term Network Development Demand Forecast for a Distribution Network • 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

  3. Outline • Background • Forecast Model • Results • Future work • Conclusions

  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. Designing a Forecast Model • Many existing methods considered • Econometric • Artificial Neural Networks • Cellular Automata: Computer based Land Use Simulations • New methodology designed

  6. Designing a Forecast Model Consider saturation of land From Willis, H.L., Spatial Electric Load Forecasting

  7. Basis for Forecast Model More customers More demand per customer

  8. Forecast Model

  9. Future Land Use: A Policy-Guided Approach • Auckland Regional Council sets land use rules ARC 2050 Growth Strategy

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

  11. Forecast Model

  12. Electricity Demand for each Customer/Land Use Class • The 19 simplified zone classes need to be assigned load densities

  13. Load Densities: Approach 1 Select feeders with one simplified zone class Remove feeders not fully developed Record area for each useful feeder (m2) Record peak load for each useful feeder (W) Res Low Open Space

  14. Determining Peak Load

  15. Calculated Load Densities Land use class Land use class Load density (W/m2) Load density (W/m2) Open Space Open Space 0 Residential – Low Intensity Residential – Low Intensity 3.99 Residential – Medium Intensity Residential – Medium Intensity 5.82 Business – High Intensity Business – High Intensity 86.4 Industrial – Light Intensity Industrial – Light Intensity 11.54 … … … …

  16. Load Densities: Approach 2 } Res • Further simplify zone classes • More areas to work with Res Low Res Med Res Med-High Res High

  17. Approach 2 Results Residential Load Densities

  18. Load Densities: Approach 3 • Smart Metering data • Finer resolution of load densities • Applicable now to some Commercial and Industrial customers

  19. Forecast Model

  20. Combining • Applying load densities to zone classes x 107,886 m2 3.99 W/m2 430.5 kW

  21. Forecast Model

  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. Scenario Analysis • Examples: • New transport links • Rezoning of land • DSM, DG • Intelligent Buildings: EMCS

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

  25. Forecast Model Completed

  26. Case Study Results • Auckland Region

  27. Case Study Results • Scenario Analysis: Residential Growth • High infill of zones near a major transport corridor Height = Peak Demand

  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. Verification

  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. 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. 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. 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 Auckland City Council Manukau City Council Papakura District Council • Vector • Guhan Sivakumar

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