Quantifying Probabilistic Weather and Load Forecasting

Transcription

1 Quantifying Risk Click to edit Master title style Probabilistic Weather and Load Forecasting Chris Hyde, Radiant Solutions & Michael Goodman, TESLA, Inc.

2 Radiant Solutions (formerly MDA Weather or EarthSat) EarthSat creates commercial division called CropCAST Great Russia Grain Robbery Natural Gas deregulated and EarthSat creates EnergyWx program NASA creates LACIE EarthSat Corp awarded Prime contract CropCast model operationalized Wx + Imagery VAPs for Utilities and Launch of Weather Desk Renewable Energy forecasting program developed EarthSat helps structure the first WxDeriv contract USDA AgRISTARs & IC Upstreet programs 1970s Maxar forms following SSL-MDA purchase of DG MDA LTD buys EarthSat Corp Wx + Imagery VAPs for Agriculture Awarded official weather data provider contract by CME Group 1980s 1990s 2000s 2 Close to 500 Customers Worldwide Focus 7/25/ 2010s

3 TESLA, Inc. Forecasting load since 1992 Americas Richmond, Virginia TESLA, Inc. founded in US 3 years later -13 years before the first Roadster Serving Utilities Customizing solutions for IOUs, G&Ts, Coops, and Munis EMEA Serving the Markets London, England All US & Canadian markets Coming soon: Mexico Asia Pacific Auckland, New Zealand

4 Extreme = Uncertain = Risky Extreme weather is rare Few observations in sample Forecasting is an uncertain process A relatively accurate point forecast is only half of the story. How can we address the other half Often not observed in years Exacerbated by extreme weather events

5 Uncertainty

6 Traditional Demand Forecasting

7 CH1 Traditional Demand Forecasting TESLA Demand Forecast Single Weather Forecast NAM, SREF, GFS, etc Single Demand Model Vendor aggregate MWH :00 5:00 9:00 13:00 17:00 21:00

8 Slide 7 CH1 Talk about weather model blending Christopher Hyde, 7/24/2018

9 Traditional Demand Forecasting Operationally convenient Simple Intuitive Inexpensive Can be very accurate Especially with Radiant Solutions weather forecasts Does not address inherent uncertainty

10 Uncertainty Remains MODEL ERROR MODEL CHOICE WEATHER DEMAND Demand Model Error Demand Model Choice Weather Model Error Weather Model Choice

11 Probabilistic Demand Forecasting

12 Probabilistic Demand Forecasting Method 1: Single Weather Forecast 1. Generate PDF around point temperature forecast 2. Extract temperatures Ex. 5/50/95th percentile Generate Demand Forecasts

13 Probabilistic Demand Forecasting Results are not always intuitive Hot Weather: 95th percentile will be highest forecast TESLA Probabilistic Forecast Cold Weather: 5th percentile will be highest forecast Mild Weather:???? Asymmetrical risk Temperature only :00 5:00 9:00 5th Percentile 13:00 50th Percentile 17:00 95th Percentile 21:00

14 CH2 Uncertainty Remains MODEL ERROR MODEL CHOICE WEATHER DEMAND Weather Model Error Quantified Weather Model Choice Demand Model Error Demand Model Choice

15 Slide 13 CH2 Quantuify wx model error with distribution Christopher Hyde, 7/24/2018

16 Probabilistic Demand Forecasting Method 2: Traditional Demand TESLA Probabilistic Forecast Forecast Generate PDF around point demand forecast Extract demands Ex. 5/50/95th percentile Results are intuitive Symmetrical risk :00 5:00 9:00 50th Percentile Addresses all weather parameters 13:00 5th Percentile 17:00 95th Percentile 21:00

17 Uncertainty Remains MODEL ERROR MODEL CHOICE WEATHER DEMAND Weather Model Error Implicitly Quantified Weather Model Choice Demand Model Error Quantified Demand Model Choice

18 Ensemble Demand Forecasting

19 Ensemble Demand Forecasting No Weather Model TESLA Ensemble Demand Forecast No Demand Model Selection of meteorologically similar days in history Ensemble of demand observations TESLA Comparable Days :00 5:00 9:00 13:00 17:00 21:00

20 Ensemble Demand Forecasting Single Weather Model TESLA Ensemble Demand Forecast Single Demand Model Perturbations of single weather forecast Ensemble of demand forecasts TESLA Temperature Scenarios :00 5:00 9:00 13:00 17:00 21:00

21 CH3 CH4 Ensemble Demand Forecasting TESLA Ensemble Demand Forecast Multiple weather models NAM, SREF, GFS, etc Single Demand Model Ensemble of demand forecasts Radiant Solutions: POWER :00 4:00 8:00 12:00 16:00 20:00

22 Slide 19 CH3 start here CH4 Christopher Hyde, 7/24/2018 Christopher Hyde, 7/24/2018

23 Ensemble Demand Forecasting: POWER Produced jointly by Radiant Solutions & TESLA Multiple weather models NAM, SREF, GFS, ECMWF, etc. Major RTOs/ISOs and subregions and cities Ensemble of demand forecasts Humidity, cloud cover and other variables

24 Ensemble Demand Forecasting: POWER Produced jointly by Radiant Solutions & TESLA Multiple weather models NAM, SREF, GFS, ECMWF, etc. Major cities Data via web, FTP or API Ongoing Verification

25 Uncertainty Remains MODEL ERROR MODEL CHOICE WEATHER DEMAND Weather Model Error Weather Model Choice Quantified Demand Model Error Demand Model Choice

26 Probabilistic Ensemble Demand Forecasting

27 CH5 Probabilistic Ensemble Demand Forecasting 0.12 Method 1: Multiple Weather 0.1 Forecasts Generate PDF around each temperature forecast 2. Aggregate PDFs 3. Extract temperatures Ex. 5/50/95th percentile Radiant Solutions Error Bound Temperature Forecasts Generate Demand Forecasts 0

28 Slide 24 CH5 do this section Christopher Hyde, 7/24/2018

29 Probabilistic Ensemble Error Bound Temp. Forecast Example CAISO (Burbank, CA) Range: 92F to 106F (Wednesday Peak Day) Causes Confidence & Risk Decisions

30 Probabilistic Ensemble Demand Forecasting TESLA Ensemble Probabilistic Forecast Results are still not always intuitive Hot Weather: 95th percentile will be highest forecast Cold Weather: 5th percentile will be highest forecast Mild Weather:???? Asymmetrical risk :00 5:00 9:00 Temperature only 5th Percentile 13:00 50th Percentile 17:00 95th Percentile 21:00

31 Uncertainty Remains MODEL ERROR Weather Model Error Quantified WEATHER Weather Model Choice Quantified Demand Model Error Demand Model Choice DEMAND MODEL CHOICE

32 Probabilistic Ensemble Demand Forecasting 0.12 Method 2: Ensemble Demand Forecast 1. Generate PDF around each demand forecast Aggregate PDFs CDF Extract demands

33 Probabilistic Ensemble Demand Forecasting TESLA Ensemble Probabilistic Forecast Results are intuitive Asymmetrical risk Addresses all weather parameters :00 5:00 9:00 5th Percentile 13:00 50th Percentile 17:00 95th Percentile 21:00

34 Uncertainty Remains MODEL ERROR Weather Model Error WEATHER DEMAND Implicitly Quantified Weather Model Choice Quantified Demand Model Error Quantified Demand Model Choice MODEL CHOICE

35 CH6 Extreme Weather Applications Intensity Timing Demand Possibilities Probabilities of Exceedance

36 Slide 31 CH6 jump in Christopher Hyde, 7/24/2018

37 Future Devlopments MODEL ERROR More weather models Refine assumptions WEATHER DEMAND Non-normal distributions Further stratify RMSE Multiple demand models Address uncertainty from all sources MODEL CHOICE

38 Probabilistic Ensemble Demand Forecasting CAISO s Forecast 0.12 Method 2: Ensemble Demand Forecast 1. Generate PDF around each demand forecast Aggregate PDFs CDF Extract demands

39 Future Devlopments MODEL ERROR WEATHER DEMAND Include CAISO s forecast Address uncertainty from all sources MODEL CHOICE

40 Thank You Questions

41 Contact details Chris Hyde Michael Goodman Director, Marketing & Sales Vice President, North America Radiant Solutions Gaithersburg, MD TESLA, Inc. Richmond, VA (240) (804)