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Presented by: Magnus Hindsberger

Presented by: Magnus Hindsberger. 100 Per Cent Renewables Study Demand Assumptions and Forecast Stakeholder Information Forum 28 September 2012. 4 cases - Main building blocks . But alternative sources needed for Electric Vehicle (EV) uptake and DSP. High level process.

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Presented by: Magnus Hindsberger

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  1. Presented by:Magnus Hindsberger 100 Per Cent Renewables StudyDemand Assumptions and ForecastStakeholder Information Forum28 September 2012

  2. 4 cases - Main building blocks But alternative sources needed for Electric Vehicle (EV) uptake and DSP

  3. High level process • Decide on overall scenario assumptions • Forecast demand growth for energy and maximum demand (MD) • Remove Solar PV contribution from 2012 NEFR forecast • Adjust to account for alternative energy efficiency uptake • Adjusted energy demand is extrapolated from 2032 to 2050 • Adjust for higher expected electricity prices • MD assumed to grow with same rate as energy beyond 2032 • Create regional demand profiles • Grow base year demand trace based on the MD and energy targets • Adjust profiles based on hourly rooftop PV generation profiles • Add in impact of electric vehicles • Add in impact of any other changes (such as DSP, fuel switching)

  4. Scenario design parameters Notes: This is targeting a 5% reduction in greenhouse gas emissions by 2020 This is addition to the price increase already assumed in the NEFR

  5. Some key decisions • Rooftop solar PV and electric vehicle demand to be applied to load traces • Small scale generation (apart from rooftop solar PV) to be modelled as generation • DSP will be modelled as a mix of: • Generation: load reduction if cost of supply > DSP price; and • Storage: shifts in load rather than foregone load

  6. NEFR as the starting point

  7. Rooftop solar PV From AEMO’s Rooftop Solar PV 2012 information paper. Both scenarios assumed moderate uptake.

  8. Energy Efficiency forecast 2012 NEFR energy efficiency scenario. Both scenarios assumed 50% of this.

  9. regression model Need to extend the forecast from 2032 (NEFR) to 2050: • Use publicly available time series extending beyond 2032 (ABS) or extended NIEIR data and fit a model to the adjusted forecasted demand from NEFR. • Regression model on full demand based on: • Population (population specific – hot water) • Households (fixed component – lighting, freezers, etc.) • Economic activity (GSP) • Electricity price (residential)

  10. Additional Price response due to Forecasted LCOE (BREE ATEA) $70/MWh Approximate wholesale price assumed in the 2012 NEFR forecast longer term It has been assumed end user price increases another 50% before 2030 for all four cases – also to account for integration costs

  11. Resulting forecasts – Scenario 1

  12. Resulting forecasts – Scenario 2

  13. From forecast to hourly data Two step approach: • Pick a base year for demand • 2009-10 has been selected • For each region, grow hourly demand series for base year to match energy target and summer/winter MD targets. • PLEXOS has been used The resulting profiles ensure: • Realistic demand behaviour over time • Realistic diversity between regional maximum demand By using a demand profile consistent with the renewable generation profiles (same base year), we ensure reasonable “weather driven” correlation between generation and demand

  14. Base year shape (2009-10)

  15. Resulting profiles - Example

  16. Extended rooftop solar PV forecast Scenario 1 Scenario 2

  17. Adjust hourly demand with rooftop solar PV generation South Australia Match with base year to capture correlation Contribution to meet maximum demand diminishing Hourly rooftop PV generation created by ROAM as for utility scale PV

  18. AEMO’s electric vehicle forecast

  19. Electric Vehicle demand by 2050 Significant uptake – in line with moving towards 100% renewables, but no other fuel switching assumed.

  20. EV charging - load profile adjustment Electric Vehicle charging • Scenario 1: Electric vehicles are assumed to charge optimally depending on demand and generation availability: • If high wind & solar generation during daytime, it may be better to recharge during day than overnight. • Scenario 2: This scenario assumes a percentage (20%) of “convenience” charging across all daytime hours with the remaining being fully optimised as Scenario 1.

  21. Demand Side Participation • Demand Side Participation (DSP) can be load shedding (load foregone) and load shifting technologies (load is moved to another time, typically after, but if good forecasts are available, it could be brought forward). • For this study, the following levels of DSP have been assumed for both 2029/30 and 2049/50: • Scenario 1: Up to 10% of the maximum demand that year • Scenario 2: Up to 5% of the maximum demand that year • DSP will be modelled as a 50/50% mix of: • Generation: load reduction if cost of supply > DSP price; and • Storage: shifts in load rather than foregone load

  22. Final annual energy demand Scenario 1

  23. Final annual energy demand Scenario 2

  24. Final NEM-wide Annual Energy and MD • Note: • NEM is Winter peaking under all four cases • Impact (if any) of EVs on Summer and Winter MD not captured • For comparison, in financial year 2010-11: • Annual Energy was ~196,000 GWh • Maximum Demand was ~34,000 MW (Summer peaking)

  25. Questions ?

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