Renewable Energy Requirements Rapid Update Analysis/Nowcasting Workshop - June 4, 2015 Sue Ellen Haupt Director, Weather Systems & Assessment Program Research Applications Laboratory National Center for Atmospheric Research

Industry Needs Need to predict POWER based on met variables 80-m wind speed Surface irradiance – GHI, DNI, DIF Time frames for prediction Long range – weeks – maintenance and distribution Medium range – days – hourly day ahead trading Nowcast range – hours – 15-min grid integration Very short range – seconds to minutes – voltage control

Scientific Advances in Wind Power Forecasting 3 WRF RTFDDA System Natl Center Data HRR, NAM, GFS, RAP GEM (Canada), ECMWF Natl Center Data HRR, NAM, GFS, RAP GEM (Canada), ECMWF Wind Farm Data Nacelle wind speed Generator power Node power Availability Wind Farm Data Nacelle wind speed Generator power Node power Availability VDRAS (nowcasting) VDRAS (nowcasting) Supplemental Wind Farm Data Met towers Wind profiler Surface Stations Supplemental Wind Farm Data Met towers Wind profiler Surface Stations Operator GUI Meteorologist GUI WRF Model Output Wind to Energy Conversion Subsystem Dynamic, Integrated Forecast System (DICast ® ) Dynamic, Integrated Forecast System (DICast ® ) CSV Data Statistical Verification Statistical Verification Expert System ( nowcasting) Expert System ( nowcasting) Ensemble System Extreme Weather Events Potential Power Forecasting Data Mining for Load Estimation Probabilistic and Analog Forecast Solar Energy Forecast Variable Energy Forecasting System

WRF RTFDDA HRRR RAP GFS GEM Other Model Data Integrator Wind Power Forecast Nacelle Winds Turbine Power Prediction ECMWF Wind speed example 10-15% improvement over best model Bill Myers Dynamic Integrated Forecast System (DICast)

Scientific Advances in Wind Power Forecasting DICast System Blends Output from Several Numerical Weather Prediction Models Public Service of Southwestern Public Service Company Total Power, 03/08 Ramp CAPACITY (%) TIME

Scientific Advances in Wind Power Forecasting 6 Wind Power Forecasts Resulted in Savings for Ratepayers Drake Bartlett, Xcel Also: saved > 267,343 tons CO2 (2014) Forecasted MAEPercentageSavings * Improvement 16.83%10.10%40% $49,000,000 *Data through November, 2014

A Public-Private-Academic Partnership for Solar Power Forecasting

Weather Monitoring Observation Modelling Forecasting Dissemination & Communication Perception Interpretation Uses / Decision Making Outcomes Economic & social values Value Chain: What is the value of solar power forecasting? Clouds Aerosols Clear Sky SURFRAD Satellites Total Sky Imagers Pyranometers WRF-Solar HRRR StatCast TSICast CIRACast MADCast DICast NowCast Production Cost Changes Unit Allocations Area Forecast Point Forecast Reserve Estimates Reserve Analysis Projected Power Production Day Ahead Planning Real Time Operation Actual Power Production Load Balancing Uncertainty Quant Power Conversion

Seamless Scaled Approach to Solar Power Forecasting 48

Engineering the System

Quantify Value - Metrics Model-Model ComparisonEconomic Value Base Mean Absolute Error Root Mean Square Error Distribution (Statistical Moments and Quantiles) Categorical Statistics for Events Operating Reserves Analysis Production Cost Enhanced Maximum Absolute Error Pearson's Correlation Coefficient Kolmogorov-Smirnov Integral Statistical Tests for Mean and Variance OVER Metric Renyi Entropy Brier Score incl. decomposition for probability forecasts Receiver Operating Characteristic (ROC) Curve Calibration Diagram Probability Interval Evaluation Frequency of Superior Performance Performance Diagram for Events Taylor Diagram for Errors Cost of Ramp Forecasting

NWP Models NAM GFS WRF-Solar GEM RAP/HRRR Initial Grid Interpolated to 4 km CONUS Grid 1-Hour Averaging Archive data near observation sites Observations SMUD MADIS OK Mesonet BNL SURFRAD Xcel DeSota ARM Statistical Correction/Blending DICast Point Correction Gradient Boosted Regression Trees Cubist Random Forests Analog Ensemble Output Products Maps of solar irradiance Single point forecasts % of clear sky irradiance Future: Other met. variables Gridded Atmospheric Forecasts: GRAFS-Solar

Grid Forecast Timeseries: Sunny Day DICast Correction

GRAFS A new forecast is generated every hour Individual images are generated for each lead-time – Currently hourly out to 60 hours.

GRAFS AI methods at SMUD Sites

MADIS Obs in SW US solarRadiation = global 5 min 144 solarRadiation = global 1 hr 830 solarRadiation not defined 176 solarRadiation = global instantaneous 30 solarRadiation = global 15 min 5

Renewable Energy Needs: Special Variables for RE – Hub height wind speed (80 m) – GHI, DNI, DIF High resolution Local corrections for model ICs Good background for gridded forecasts Frequent update, Low latency For AI Training, Need Good obs & Realistic gridding (assim) Historical data Consistency with Models Blend Dynamics + Statistics

Questions