Transcript Shared Services PowerPoint Presentation

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
1
ON IT
Agenda
• Overview
• Methodology
• Performance
• Challenges
• Summary
2
ON IT
Overview
3
ON IT
Overview
Con Edison Service Territory
Con Edison Co. of New York
•
•
•
•
3.2 million electric customers
1.0 million gas customers
1,800 steam customers
709 MW of regulated generation
Orange and Rockland
• 300,000 electric
customers
• 127,000 gas customers
4
ON IT
Overview
When bad weather strikes…..
• Restoration delays
• Upset customers
• Potential fines
• Company reputation
The goal is to be prepared,
otherwise…..
5
ON IT
Overview
Local TV
Forecast Models
Internet
Rain 0.75”/3hrs
Temperature Variable
Winds 35+ mph
Private Services
Heavy Wet Snow
Extreme Heat
NWS
Weather Services
Weather Triggers
IMPACT
6
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
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 browser
 Javascript movie
 Data tables
 Charts
–
Email alert system
18 km
6 km
2 km
Deep Thunder Domain
8
ON IT
Overview
Impact Model
Westchester County overhead electric
Historical
Weather
Data
Calibrated Weather
Gust
Calculation
Model
Model
Training
Model
Training
Historical
Damage
Data
•
•
•
•
•
Impact Model
Post-Process of weather model
Output # of jobs per substation
Predictive & probable “mode”
Quantifies uncertainty
Email alert system
Westchester Substation Map
9
ON IT
Overview
Gust Calculation
Deep Thunder Damage Model
10
ON IT
Overview
Probability Map
11
ON IT
Methodology
12
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
Observe Rain
Yes
Observe Rain
No
Forecast Rain
Yes
HIT
FALSE ALARM
Forecast Rain
No
MISS
CORRECT NEGATIVE
13
ON IT
Methodology
Forecast Score
Forecast Score (FS)
Temp., deg F
Error, Bias
Pts.
0 to 2
10
2 to 4
7
4 to 6
3
>6
0
Wind, mph
Error, Bias
Pts.
0 to 5
10
5 to 10
5
>10
0
Precip., %
Error
Pts.
0 to 10
10
10 to 20
8
20 to 30
6
30 to 40
4
40 to 50
2
>50
0
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
ON IT
Performance
15
ON IT
Performance
Forecast Score - Weighted
Day 1
DT
NAM
Pub. 1
Score %
Pvt. 1
Pub. 2
Pvt. 2
100
90
80
70
60
50
40
30
20
10
0
Day 3
Score %
Score %
Day 2
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
16
ON IT
Performance
Forecast Score – Non-Weighted
Day 1
DT
NAM
Pub. 1
Score %
Pvt. 1
Pub. 2
Pvt. 2
100
90
80
70
60
50
40
30
20
10
0
Day 3
Score %
Score %
Day 2
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
17
ON IT
Performance
Damage Model
Weather Events <100 Jobs
120
460
110
385
100
90
Jobs
80
70
60
50
40
30
20
10
0
Predictive
Actual
Probable
18
ON IT
Performance
Damage Model
Weather Events >100 Jobs
1000
900
800
Jobs
700
600
500
400
300
200
100
0
Predictive
Actual
Probable
19
ON IT
Challenges
20
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
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
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
ON IT
Questions?
Brandon Hertell
ConEdison Emergency Management
[email protected]
212-460-3129
24
ON IT
Extra Slides
25
ON IT
Big Green Innovations
Simplified Deep Thunder Processing Data Flow
NOAA (NCEP, NWS)
Data Used to Generate
 Boundary conditions
 Initial conditions
 Forecast verification
 Calibration of model and
observations
AWS
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
Surface
Observations:
hundreds in each of
several major
metropolitan areas
(e.g., urbanet)
5 minute updates
IBM Deep Thunder
Observations
18 km
6 km
2 km
© Copyright IBM Corporation 2010
Big Green Innovations
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
Post-processing
Weather Data
Pre-processing
Processing
and Tracking
Synoptic
Model
pSeries
Cluster 1600
NAM
NOAAPORT Data Ingest
FCST
Boundary
Conditions
Other Input Products
Advanced
Visualization
Weather Server
NCEP Forecast Products
Satellite Images
Other NWS Data
http://www...
Analysis
Initial
Conditions
Forecast
Modelling
Systems
Analysis
NWS and AWS
Observations
Data Explorer
Data Assimilation
Cloud-Scale Model
Custom Products
for Business
Applications
and
Traditional
Weather Graphics
© Copyright IBM Corporation 2010
Big Green Innovations
 Modelling
Key Steps
– 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
18 km
6 km
2 km
© Copyright IBM Corporation 2010
Big Green Innovations
Web Interface for Consolidated Edison
Surface Wind
Animation
Interactive Site-Specific Forecast Table
© Copyright IBM Corporation 2010
Big Green Innovations
Web Interface for
Consolidated
Edison
Site-Specific Forecast Plots
Surface Precipitation
Animation
© Copyright IBM Corporation 2010
Big Green Innovations
Modelling Extreme Values
500
300
100
0
0
10
20
30
40
50
60
70
Gust speed (mph)
0.2
0.3
=1
=5
=10
0.1
 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
0.0
 Generalized extreme value
(GEV) distribution have three
parameters, , , , which
controls the location, scale and
shape of a distribution,
respectively
Histogram of gust speed
Frequency
 Distribution of daily maximum
gust speed shows a highly
right skewed tail which
indicates a Gaussian
distribution assumption does
not hold
0
5
10
15
20
Example of generalized extreme value distribution
with location parameter varied
© Copyright IBM Corporation 2010
Big Green Innovations
Modelling Process
Model set-up:
Bayesian Hierarchical
Modeling Latent Process:
The goal is to obtain
Bayesian Hierarchical Modeling Data Process:
Location parameter and scale
parameter are location dependent
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
© Copyright IBM Corporation 2010
Big Green Innovations
50
40
30
20
10
0
observed/forecasted gust speed
Sample Modelling Results
0
100
200
300
400
500
30
40
Forecasted vs. observed daily
maximum gust speed for all of
November 2009
10
20
The points are lined up with 45
degree line (red solid line)
0
observed gust speed (mph)
50
60
Comparison of forecasted and observed daily maximum gust speed. The black
curve is the forecasted values and the red curve is the observed values
10
20
30
40
forecasted gust speed (mph)
50
© Copyright IBM Corporation 2010
Deep Thunder Validation
Monthly Temperature RMSE
Day 1
12
Pvt. 1
NAM
Pub. 1
RMSE (F)
10
DT
8
6
4
2
Pub. 2
0
Pvt. 2
Day 3
12
10
10
8
6
4
RMSE (F)
RMSE (F)
Day 2
12
8
6
4
2
2
0
0
34
ON IT
Deep Thunder Validation
Monthly Wind RMSE
Day 1
14
Pvt. 1
NAM
Pub. 1
RMSE (mph)
DT
12
10
8
6
4
2
Pub. 2
0
Pvt. 2
*Fleet does not
provide day 3 wind
Day 3
14
12
12
10
8
6
4
RMSE (mph)
RMSE (mph)
Day 2
14
10
8
6
4
2
2
0
0
35
ON IT
Deep Thunder Validation
Monthly Temperature Bias
Day 1
4
Pvt. 1
3
Bias (F)
DT
NAM
Pub. 1
2
1
0
-1
-2
Pub. 2
-3
Pvt. 2
Day 2
Day 3
4.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
Bias (F)
Bias (F)
3.00
4.00
3.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
-4.00
36
ON IT
Deep Thunder Validation
Monthly Wind Bias
Day 1
DT
NAM
Pub. 1
Bias (mph)
Pvt. 1
Pub. 2
Pvt. 2
12
10
8
6
4
2
0
-2
-4
Day 3
8
6
Bias (mph)
Bias (mph)
Day 2
12.00
10.00
8.00
6.00
4.00
2.00
0.00
-2.00
-4.00
4
2
0
-2
-4
37
ON IT
Deep Thunder Validation
Monthly Precipitation Error
% Incorrect
Day 1
100
90
80
70
60
50
40
30
20
10
0
*precip data not
available for NAM &
DTN
Day 3
% Incorrect
% Incorrect
Day 2
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
38
ON IT
Performance
Damage Model
1000
B/W Significant Weather Events
1075
1490
900
800
Jobs
700
600
500
400
300
200
100
0
DT
Probable
Actual
39
ON IT
Overview
Impact Model
Westchester County overhead electric
Deep Thunder
weather model
output
Calibrate wind
forecast against
gust
observations
“Gust calculation”
•
•
•
•
•
Historical
Weather
Data
Impact
Historical
Damage
Data
Mod Model
el
Train
ing
Runs concurrent with weather model
Output # of jobs per substation
Predictive & probable “mode”
Quantifies uncertainty
Email alert system
Westchester Substation Map
40
ON IT