1. The Vermont Weather Analytics Center Project: Electricity, Weather and Accelerating the Renewable Grid
CIGRE: Grid of the Future Symposium
12 October 2015
Chicago, IL
Chris Root, Rob D’Arienzo
Vermont Electric Power Company (Rutland, VT, USA)

2. Outline VELCO Motivation Vermont Weather Analytics Center (VTWAC)
Overview
Partners
Models
Renewable Integration
IBM Deep Thunder
Background
Model Specifications
Capabilities/Challenges
Performance Metrics
Renewable Integration Stochastic Engine (RISE)
Summary
Future Work

3. VELCO
Vermont Electric Power Company (VELCO) was founded in 1956 when local utilities joined together to create the nation’s first “transmission only” electric company
VELCO operates an interconnected electric transmission grid consisting of:
738 miles of transmission lines
13,000 acres of rights-of-way
55 substations, switching stations, and terminal facilities
1,300 miles of fiber optic communication network
Equipment that enables interconnected operations with Hydro-Québec
VELCO: Vermont’s transmission reliability resource

4. Motivation Global Risk Trends

5. Motivation Weather & Utilities
Climate Central: a nonprofit news organization that analyzes and reports on climate science. ”Outage” = at least 50,000 customers affected
Source: “Blackout: Extreme Weather, Climate Change and Power Outages” (Climate Central)

6. Motivation Weather & Utilities
A tenfold increase in major power outages (those affecting more than 50,000 customer homes or businesses), between the mid-1980s and 2012.
Non-weather related outages also increased during that time, but weather caused 80 percent of all outages between  
Source: “Blackout: Extreme Weather, Climate Change and Power Outages” (Climate Central)

7. Vermont Weather Analytics Center Project Overview
Two-year, $16 million research undertaking to develop intellectual property using coupled data models and related software
2-year agreement/partnership with IBM
3 main goals:
Increase grid reliability
Lower weather event-related operational costs
Optimize utilization of renewable generation resources
Irene –cold river K32, KCW

8. Vermont Weather Analytics Center Partners
VT Distribution Utilities (DU’s):
VT College/ University:
VLITE? ISO-NE?
Other VT Organizations:

9. Renewable Integrated Stochastic Engine (RISE)
Vermont Weather Analytics Center Models
Deep Thunder
Demand
Renewable
Renewable Integrated Stochastic Engine (RISE)
Model
Function
Weather (Deep Thunder)
To produce accurate weather forecasts up to 48 hours in advance down to 1 sq. km
 Lower weather event costs
Demand
To increase accuracy of state load forecasts
 Better plan for future needs
Renewable
To produce generation forecasts for solar and wind farms
 Improve power supply/planning
Renewable Integration Stochastic Engine (RISE)*
To integrate the models’ results to optimize the value of Vermont's generation, demand response, and transmission assets
*VELCO Only

10. Vermont Weather Analytics Center Models
Renewable Sensor
Data
Renewable Generation
Weather Data
Wind
Weather Model
Solar
Renewable
Integration
Power Data
Electric Demand
Meter Data

11. Vermont Weather Analytics Center Renewable Integration
3/31/2015 (Tue)
Cloud Cover
Overcast
Sunny
High/Low
(°F)
41/26
42/24
Max Radiation
(w/m^2)
241
965
2 weekdays with similar temperatures but large differences in cloud cover
Overcast

12. IBM Deep Thunder Model Specifications Utilizes WRF-ARW
1071x1071 km, every 9 km
564x564 km, every 3 km
300x297 km, every 1 km
Utilizes WRF-ARW
(v since July 2014)
9/3/1 km horizontal nest (previously: 18/6/2 km)
51 vertical levels to target turbine hub heights
Run 2x daily (00/12Z) out to 48 hours in 10 minute intervals
RAP used for background fields
NAM used for lateral boundary conditions
Complex physics configurations for highly rural and urban environments
~4000 stations (gray markers on map): 9km nest (~4000), 3km nest (~1750), 1km nest (~550) – varies for each forecast

13. IBM Deep Thunder Model Specifications 16 km 1 km
Observed radar on 5/27/14 – Isolated supercell over Rutland that produced golf ball-sized hail, high wind gusts, and flash flooding
Horizontal Resolution:
16 km
1 km

14. Forecasted Wind Gusts – 6/28 Wind Event
European (ECMWF) [16km]
NWS [4km]
Deep Thunder [1km]
Increased Resolution

15. Deep Thunder [1km]

16. IBM Deep Thunder Capabilities Temperature Liquid Precipitation
Typical Model Parameters
Additional DT Parameters
Temperature
Liquid Precipitation
Pressure
Wet Bulb
Dew Point
Wind Speed
Wind Chill
Heat Index
Wind Gusts 
Cloud Water Density
Cloud Height
Visibility
Shortwave Radiation
Precipitation Rate
Accumulated Precipitation
Snowfall Rate
Snowfall Depth
Snow-to-Liquid Ratios
Surface Runoff
Maximum Reflectivity
Lightning Potential Index
Freezing Rain*
*Potential future addition

17. Lightning Forecasting
Deep Thunder (Lightning Potential Index)
Observed Lightning Strikes
Low
Medium
High

18. IBM Deep Thunder Challenges Convective events Hail Lightning Tornadoes
Microbursts/downbursts
Straight line winds
Long-range forecasts
Real-time observations
Bottom line: Deep Thunder will serve as a powerful, complementary weather prediction tool

19. Performance Metrics Metric Description December 2014 April 2015
June 2015
Goal
Actuals
Wind speed error
Wind direction error
1 – 4 m/sec
4 – 6 degrees
1 – 2 m/sec
1 – 4 degrees
1 m/sec
1 – 2 degrees
1.65 m/sec
.09 degrees
Wind power forecast precision (farm-level)
75%
1 wind farm
85%
4 wind farms
2 wind farms*
3 wind farms
80% with alarm data 1 wind farm*
Solar power forecast precision (farm-level)
86%
1 solar farm
>86%
12 solar farms
17 solar farms
12 farms with 86% accuracy
Demand forecast accuracy, state level (MAPE)
No goals set
+/- 4.5%
41 substations
+/- 3.0%
60 substations
+/-2.9%
83 substations**
<2% for state & DU service territory
<5% for distribution substations
State: 2.4%
DUs: 2.7%
Substations 3-8%
* Additional wind farm data in process
** In some cases, forecasts include total loads aggregated from more than one substation (typically 1-4) for a total of 255 substations

20. Renewable Integration Stochastic Engine (RISE)
RISE dashboard consists of the following four views:
Analysis Context
Every 12 hours, RISE produces a new set of forecasts using fresh inputs from VELCO (grid state (OSI model), planned outages (TOA) , ISO’s Generation (Unit Commitment) and IBM’s Deep Thunder, Demand and Renewables models
Allows users to define new what-if scenarios by changing e.g. grid state, outages etc. and ask RISE to analyze under this scenario
Stochastic Contingency Analysis
For each analysis context, this view summarizes the analysis results for all the contingency situations
User can select any contingency case (line in table) and step into next view
Grid State
For each contingency case, provides detailed output on each line and bus in the network
Users can select up to five lines and buses and plot the predictions of power-flow/voltage over the next 24 hours
Mitigation Actions
Effect of various optimized mitigation actions
RISE dashboard in VTWAC’s platform consists of four views. Two of these have been built, the Analysis view is being worked on now, and the mitigation actions view is to be defined. 20

21. Summary
Development
2/2014 – 5/2015
Prove Out
6/2015 – 1/2016
Operational
2/2016
Today
Initial feedback from prove-out efforts indicates that weather, demand, and renewable generation information provides value
Weather & Energy Innovation Workshop (8/13)
Participation from utilities across the country – ConEd, SDG&E, DTE, and HQ
Shared strategies for effectively operationalizing weather prediction as well as lessons learned
Collaboration with ISO-NE
Wind power forecasting
Renewable Energy Vermont (REV) Conference
Working with Sandia to secure DOE funding for renewable integration research
Submitted several abstracts to American Meteorological Society 2016 Energy Conference

22. Future Work Finalize hardware/software strategy for ongoing project
Deploy first version of RISE model
Finalize customizations requests amongst all models
Additional training with VT-DU operators
Continue model validation (working with ISO-NE)
Expand project to serve other societal sectors and applications:
Outage/Impact prediction
AMI Analytics
Agriculture
Transportation
Recreation/Tourism