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ON IT Operational Utilization and Evaluation of a Coupled Weather and Outage Prediction Service for Electric Utility Operations Northeast Regional Operations.

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Presentation on theme: "ON IT Operational Utilization and Evaluation of a Coupled Weather and Outage Prediction Service for Electric Utility Operations Northeast Regional Operations."— Presentation transcript:

1 ON IT Operational Utilization and Evaluation of a Coupled Weather and Outage Prediction Service for Electric Utility Operations Northeast Regional Operations Workshop 2010 Albany, NY 1 Brandon Hertell - ConEdison Lloyd Treinish, Anthony Praino, Hongfei Li IBM – Thomas J. Watson Research Center

2 ON IT Agenda Overview Methodology Performance Challenges Summary 2

3 ON IT Overview 3

4 ON IT Overview Con Edison Service Territory 3.2 million electric customers 1.0 million gas customers 1,800 steam customers 709 MW of regulated generation Con Edison Co. of New York 300,000 electric customers 127,000 gas customers Orange and Rockland 4

5 ON IT Overview 5 The goal is to be prepared, otherwise….. Restoration delays Upset customers Potential fines Company reputation When bad weather strikes…..

6 ON IT Overview 6 IMPACT Forecast Models Private Services NWS Local TV Internet Rain 0.75/3hrs Winds 35+ mph Extreme Heat Temperature Variable Heavy Wet Snow Weather Services Weather Triggers

7 ON IT Overview Partnered with IBM in 2006 on Deep Thunder project Targeted weather information – Specific to Con Edison – Utilize high resolution weather model – Investigate link between weather and impact – Improve preparation and response 7

8 ON IT Overview Weather Model Utilize WRF-ARW – 2km resolution forecast – Assimilate additional weather data – 84hr forecast – 2x daily (0z,12z) – Temp, wind, wet bulb, precip – Content available via web browserweb browser Javascript movie Data tables Charts – alert system 8 Deep Thunder Domain 2 km 6 km 18 km

9 ON IT Overview Impact Model 9 Westchester Substation Map Westchester County overhead electric Post-Process of weather model Output # of jobs per substation Predictive & probable mode Quantifies uncertainty alert system Historical Damage Data Historical Weather Data Impact Model Calibrated Weather Model Gust Calculation Model Training

10 ON IT Overview Deep Thunder Damage ModelGust Calculation 10

11 ON IT Overview Probability Map 11

12 ON IT Methodology 12

13 ON IT Methodology Weather Validation Westchester County: April 2009 to March 2010 Deep Thunder, NAM, 2 private services, 2 public services Parameters – Forecast vs. actual – Temperature, Wind, Precipitation – RMSE, bias, contingency table 13 Observe Rain Yes Observe Rain No Forecast Rain Yes HITFALSE ALARM Forecast Rain No MISSCORRECT NEGATIVE

14 ON IT Methodology Forecast Score FS = [(TE +TB) × 0.5] + [(WE + WB) × 3] + [PE × 3] = 100 max. TE, TB = temperature RMSE and bias WE, WB = wind RMSE and bias PE = precipitation error 14 Forecast Score (FS)

15 ON IT Performance 15

16 ON IT Performance Forecast Score - Weighted 16 Pvt. 1 DT NAM Pub. 1 Pub. 2 Pvt. 2

17 ON IT Performance Forecast Score – Non-Weighted 17 Pvt. 1 DT NAM Pub. 1 Pub. 2 Pvt. 2

18 ON IT Performance Damage Model

19 ON IT Performance Damage Model 19

20 ON IT Challenges 20

21 ON IT Challenges Data Quality – Observational data Dense network of surface & upper air Reporting inconsistent – Job ticket data Rely on field crews and service reps Filtering storm related damage 21

22 ON IT Challenges Weather and impact model – Thunderstorm forecasting – Proper inputs (gusts, soil moisture, foliage, etc) – Correlation between data inputs – Incorporation of Black Swan events Utilization – Build trust – Delivery of complex information – Integration with company procedures 22

23 ON IT Summary Deep Thunder weather forecast – Better results than other sources in Westchester day to day – More analysis of specific events for accuracy Deep Thunder impact model – Not enough events for clear determination Weather Community – Collaboration 23

24 ON IT Questions? Brandon Hertell ConEdison Emergency Management

25 ON IT Extra Slides 25

26 Big Green Innovations © Copyright IBM Corporation 2010 Simplified Deep Thunder Processing Data Flow Observations Global Forecasting System: T190L28, 16 days Ensemble model, 4x/day, various products and resolutions Spectral, spherical solution North American Model System: 12km resolution, 84 hours Deterministic model, 4x/day Primarily dynamics and physics Complete data assimilation NOAA (NCEP, NWS) IBM Deep Thunder 2 km 6 km 18 km Data Used to Generate Boundary conditions Initial conditions Forecast verification Calibration of model and observations AWS Surface Observations: hundreds in each of several major metropolitan areas (e.g., urbanet) 5 minute updates

27 Big Green Innovations © Copyright IBM Corporation 2010 Pre-processing Processing Post-processing and Tracking Weather Data Analysis Initial Conditions Synoptic Model Boundary Conditions Analysis Data Explorer Advanced Visualization Weather Server Cloud-Scale Model Data Assimilation NAM Other Input Products FCST NCEP Forecast Products Satellite Images Other NWS Data NWS and AWS Observations NOAAPORT Data Ingest Forecast Modelling Systems Custom Products for Business Applications and Traditional Weather Graphics pSeries Cluster 1600 Deep Thunder Implementation and Architecture User-driven not data-driven (start with user needs and work backwards) Sufficiently fast (>10x real-time), robust, reliable and affordable Ability to provide usable products in a timely manner Visualization integrated into all components

28 Big Green Innovations © Copyright IBM Corporation 2010 Key Steps Modelling –Meteorology: apply more sophisticated physics to enable improved forecasts with up to 72 hours lead time (e.g., WRF-ARW) 2 km resolution across entire extended service area for 84 hours NAM/RUC for background and boundary conditions WSM 6-class microphysics, YSU PBL, NOAH LSM, Grell-Devenyi ensemble, urban canopy model Assimilation of WeatherBug data for initial conditions –Outages: spatial-temporal modelling to enable predictions of damage Dissemination –Tailored weather visualizations available via a web browser, which are automatically updated for each forecast cycle –Storm classification and outage estimation –Uncertainty visualization for operational decision making – alerting system 2 km 6 km 18 km

29 Big Green Innovations © Copyright IBM Corporation 2010 Web Interface for Consolidated Edison Surface Wind Animation Interactive Site-Specific Forecast Table

30 Big Green Innovations © Copyright IBM Corporation 2010 Web Interface for Consolidated Edison Surface Precipitation Animation Site-Specific Forecast Plots

31 Big Green Innovations © Copyright IBM Corporation 2010 Modelling Extreme Values Distribution of daily maximum gust speed shows a highly right skewed tail which indicates a Gaussian distribution assumption does not hold Generalized extreme value (GEV) distribution have three parameters, which controls the location, scale and shape of a distribution, respectively Use a GEV distribution to model daily maximum gust speed given a daily maximum wind forecast, while location parameter and scale parameter are spatially correlated Right-skewed distribution of daily maximum gust speed from 06/01/2009 to 11/30/2009 Example of generalized extreme value distribution with location parameter varied

32 Big Green Innovations © Copyright IBM Corporation 2010 Modelling Process The goal is to obtain Bayesian Hierarchical Modeling Data Process: Location parameter and scale parameter are location dependent Model set-up:Bayesian Hierarchical Modeling Latent Process: Prior set-up and derive posterior distributions of the parameters Use Markov Chain Monte Carlo (MCMC) to draw posterior samples and stop after convergence is achieved

33 Big Green Innovations © Copyright IBM Corporation 2010 Sample Modelling Results Comparison of forecasted and observed daily maximum gust speed. The black curve is the forecasted values and the red curve is the observed values Forecasted vs. observed daily maximum gust speed for all of November 2009 The points are lined up with 45 degree line (red solid line)

34 ON IT Deep Thunder Validation Monthly Temperature RMSE 34 Pvt. 1 DT NAM Pub. 1 Pub. 2 Pvt. 2

35 ON IT Deep Thunder Validation Monthly Wind RMSE *Fleet does not provide day 3 wind 35 Pvt. 1 DT NAM Pub. 1 Pub. 2 Pvt. 2

36 ON IT Deep Thunder Validation Monthly Temperature Bias 36 Pvt. 1 DT NAM Pub. 1 Pub. 2 Pvt. 2

37 ON IT Deep Thunder Validation Monthly Wind Bias 37 Pvt. 1 DT NAM Pub. 1 Pub. 2 Pvt. 2

38 ON IT Deep Thunder Validation Monthly Precipitation Error *precip data not available for NAM & DTN 38

39 ON IT Performance Damage Model 39

40 ON IT Overview Impact Model 40 Westchester Substation Map Westchester County overhead electric Mod el Train ing Historical Damage Data Historical Weather Data Deep Thunder weather model output Gust calculation Calibrate wind forecast against gust observations Impact Model Runs concurrent with weather model Output # of jobs per substation Predictive & probable mode Quantifies uncertainty alert system


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