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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 Workshop 2010 Albany, NY 1 Brandon Hertell - ConEdison Lloyd Treinish, Anthony Praino, Hongfei Li IBM – Thomas J. Watson Research Center
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ON IT
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Operational Utilization and Evaluation of a Coupled Weather and Outage Prediction Service for Electric
Utility Operations
Northeast Regional Operations Workshop 2010
Albany, NY
Brandon Hertell - ConEdison
Lloyd Treinish, Anthony Praino, Hongfei Li
IBM – Thomas J. Watson Research Center
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Agenda
• Overview
• Methodology
• Performance
• Challenges
• Summary
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Overview
ON IT
OverviewCon 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
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Overview
The goal is to be prepared, otherwise…..
• Restoration delays
• Upset customers
• Potential fines
• Company reputation
When bad weather strikes…..
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Overview
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
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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
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OverviewWeather 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 browser Javascript movie Data tables Charts
– Email alert systemDeep Thunder Domain
2 km6 km18 km
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OverviewImpact Model
Westchester Substation Map
Westchester County overhead electric
• Post-Process of weather model
• Output # of jobs per substation
• Predictive & probable “mode”
• Quantifies uncertainty
• Email alert system
Historical Damage
Data
Historical Weather
Data
Impact Model
Calibrated WeatherModel
Gust Calculation
Model Training
Model Training
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Overview
Deep Thunder Damage ModelGust Calculation
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Overview
Probability Map
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Methodology
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MethodologyWeather 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
Observe RainYes
Observe RainNo
Forecast RainYes HIT FALSE ALARM
Forecast RainNo MISS CORRECT NEGATIVE
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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
Error, Bias Pts. Error, Bias Pts. Error Pts.0 to 2 10 0 to 5 10 0 to 10 102 to 4 7 5 to 10 5 10 to 20 84 to 6 3 >10 0 20 to 30 6
>6 0 30 to 40 440 to 50 2
>50 0
Temp., deg F Wind, mph Precip., %Forecast Score (FS)
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Performance
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Performance Forecast Score - Weighted
0102030405060708090
100Day 1
Scor
e %
0102030405060708090
100Day 2
Scor
e %
0102030405060708090
100Day 3
Scor
e %
Pvt. 1
DT
NAM
Pub. 1
Pub. 2
Pvt. 2
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Performance Forecast Score – Non-Weighted
0102030405060708090
100Day 1
Scor
e %
0102030405060708090
100Day 2
Scor
e %
0102030405060708090
100Day 3
Scor
e %
Pvt. 1
DT
NAM
Pub. 1
Pub. 2
Pvt. 2
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0
10
20
30
40
50
60
70
80
90
100
110
120Weather Events <100 Jobs
Predictive Actual Probable
Jobs
6/26/2009
9/30/2010
PerformanceDamage Model
385460
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PerformanceDamage Model
7/7/2009
1/25/2010
2/25/2010
3/13/2010
0
100
200
300
400
500
600
700
800
900
1000Weather Events >100 Jobs
Predictive Actual Probable
Jobs
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Challenges
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Challenges
• Data Quality– Observational data
Dense network of surface & upper air Reporting inconsistent
– Job ticket dataRely on field crews and service reps Filtering storm related damage
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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
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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
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Extra Slides
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 km6 km18 km
Data Used to GenerateBoundary conditions Initial conditionsForecast verificationCalibration of model and
observations
AWSSurface Observations: hundreds in each of several major metropolitan areas (e.g., urbanet)
5 minute updates
Big Green Innovations
© Copyright IBM Corporation 2010
Pre-processing
Processing Post-processing
and TrackingWeather Data
Analysis
Initial Condition
s
Synoptic Model
Boundary Conditions
Analysis
http://www...
Data Explorer
AdvancedVisualization
Weather Server
Cloud-Scale ModelData Assimilation
NAM
Other Input ProductsFCST
NCEP Forecast ProductsSatellite ImagesOther NWS Data
NWS and AWS Observations
NOAAPORT Data Ingest
Forecast Modellin
g Systems Custom
Products for Business
Applicationsand
Traditional Weather Graphics
pSeries Cluster 1600
Deep Thunder Implementation and ArchitectureUser-driven not data-driven (start with user needs and work backwards)Sufficiently fast (>10x real-time), robust, reliable and affordableAbility to provide usable products in a timely mannerVisualization integrated into all components
Big Green Innovations
© Copyright IBM Corporation 2010
Key StepsModelling
– 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– E-mail alerting system 2 km6 km18 km
Big Green Innovations
© Copyright IBM Corporation 2010
Web Interface for Consolidated Edison
Surface Wind Animation Interactive Site-Specific Forecast Table
Big Green Innovations
© Copyright IBM Corporation 2010
Web Interface for
Consolidated Edison
Surface Precipitation Animation
Site-Specific Forecast Plots
Big Green Innovations
© Copyright IBM Corporation 2010
Modelling Extreme ValuesDistribution 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
,,,
Histogram of gust speed
Gust speed (mph)
Freq
uenc
y
0 10 20 30 40 50 60 70
010
030
050
0
Right-skewed distribution of daily maximum gust speed from
06/01/2009 to 11/30/2009
Example of generalized extreme value distributionwith location parameter varied
0 5 10 15 20
0.0
0.1
0.2
0.3 =1
=5=10
Big Green Innovations
© Copyright IBM Corporation 2010
Modelling Process
The goal is to obtainBayesian 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
Big Green Innovations
© Copyright IBM Corporation 2010
Sample Modelling Results
0 100 200 300 400 500
010
2030
4050
obse
rved
/fore
cast
ed g
ust s
peed
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)
10 20 30 40 50
010
2030
4050
60
forecasted gust speed (mph)
obse
rved
gus
t spe
ed (m
ph)
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Deep Thunder ValidationMonthly Temperature RMSE
0
2
4
6
8
10
12Day 1
RM
SE (F
)
0
2
4
6
8
10
12Day 2
RM
SE (F
)
0
2
4
6
8
10
12Day 3
RM
SE (F
)
Pvt. 1
DT
NAM
Pub. 1
Pub. 2
Pvt. 2
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Deep Thunder ValidationMonthly Wind RMSE
0
2
4
6
8
10
12
14Day 1
RM
SE (m
ph)
0
2
4
6
8
10
12
14Day 2
RM
SE (m
ph)
0
2
4
6
8
10
12
14Day 3
RM
SE (m
ph)
*Fleet does not provide day 3 wind
Pvt. 1
DT
NAM
Pub. 1
Pub. 2
Pvt. 2
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Deep Thunder ValidationMonthly Temperature Bias
-3
-2
-1
0
1
2
3
4Day 1
Bia
s (F
)
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00Day 2
Bia
s (F
)
-4.00-3.00-2.00-1.000.001.002.003.004.00
Day 3
Bia
s (F
)
Pvt. 1
DT
NAM
Pub. 1
Pub. 2
Pvt. 2
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Deep Thunder ValidationMonthly Wind Bias
-4-202468
1012
Day 1
Bia
s (m
ph)
-4.00-2.000.002.004.006.008.00
10.0012.00
Day 2
Bia
s (m
ph)
-4
-2
0
2
4
6
8Day 3
Bia
s (m
ph)
Pvt. 1
DT
NAM
Pub. 1
Pub. 2
Pvt. 2
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Deep Thunder ValidationMonthly Precipitation Error
0102030405060708090
100Day 1
% In
corr
ect
0102030405060708090
100Day 2
% In
corr
ect
0102030405060708090
100Day 3
% In
corr
ect
*precip data not available for NAM &
DTN
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PerformanceDamage Model
6/26/20097/7/20097/29/20099/10/200910/7/200910/24/200911/13/200912/19/200912/29/20091/25/20101/28/20102/6/20102/10/20102/23/20102/25/20103/13/20105/8/2010
0
100
200
300
400
500
600
700
800
900
1000B/W Significant Weather Events
DT Probable Actual
Jobs
1075 1490
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OverviewImpact Model
Westchester Substation Map
Westchester County overhead electric
Model
Training
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
• Email alert system
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