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Presented by: Magnus Hindsberger

Presented by: Magnus Hindsberger. 100 Per Cent Renewables Study System modelling approach Stakeholder Information Forum 28 September 2012. Intended Steps. We are here. Data collection Probabilistic modelling (@Risk based model) Calculate correlations between data series

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Presented by: Magnus Hindsberger

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  1. Presented by:Magnus Hindsberger 100 Per Cent Renewables StudySystem modelling approachStakeholder Information Forum28 September 2012

  2. Intended Steps We are here • Data collection • Probabilistic modelling (@Risk based model) • Calculate correlations between data series • Pre-optimise locational split of wind and solar investments • Find optimal mix of generation technologies for allowed levels of USE • Time domain modelling • Reasonableness check that USE is in line with probabilistic model • Capacity sufficiency (can peak demand generally be met); and • Energy sufficiency (can energy demand be met on a sustained basis) • If too much USE, after considering storage, adjust inputs to the probabilistic model and rerun step 2

  3. Key input: Generation time series by location • Data by polygons will capture locational differences in potential and costs • It will capture geographical (lack of) correlation and its impact on “smoothing” generation • Demand is still based on states

  4. Probabilistic modelling Build in @RISK, an Excel add-on. Based on probability distributions of demand, wind generation, solar generation, geothermal generation and wave generation. Hydro generation and energy storage discussed later.

  5. Time domain modelling USE Check if ≤ 0.002% Covered later in the presentation

  6. Capacity sufficiency check Important to capture variable generations contribution to meet peak demand Solar PV Wind

  7. Energy sufficiency check Synoptic chart problem: Can demand be met through a big high during winter? • Limited wind across NEM • Solar limited by short days and low angle • Demand for heating would be high NEM-wide wind generation (MW) Storage is key if significant penetration of variable generation

  8. Storages in 100 Per cent study • Biomass – solids • Biogas • CAES (with biogas) • Batteries (different type) • Solar thermal • Pumped hydro But also: • Existing hydro with storage reservoirs • DSP • EVs These partly modelled as “storages”

  9. Probabilistic modelling Optimise for different levels of deficit, with deficit being a mix of USE and “shortfall” to be handled with storage. Deficit Surplus USE (fixed – 0.002%) Existing hydro (fixed) New storage solutions

  10. Time domain modelling USE If no storage Generation Demand Time

  11. Time domain modelling Assumed DSP + EV load Existing hydro New storages Add storage Generation Demand Time

  12. Time domain modelling Residual USE to be ≤ 0.002% USE Generation Demand Time

  13. Compare total costs of runs Note: Conceptual figure – numbers made up Find out storage needed to reduce deficit to 0.002% USE, once allowance has been made in time domain model for USE and assumed DSP, EV and existing hydro has been applied.

  14. The overall picture Demand data Generation data Storage data Probabilistic modelling Time sequential modelling Add storage + USE check Transmission check Operations check Scenario outcome

  15. Questions ?

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