INL/MIS-11-22167
Increasing Transmission
Capacities with Dynamic
Monitoring Systems
Kurt S. Myers
www.inl.gov
Jake P. Gentle
March 22, 2012
Concurrent Cooling – Background
• Project supported with funding through US Department of Energy EERE Wind and
Water Power Program funding, and utility funding through Idaho Power/GridApp
• Concurrent Cooling
– Resource areas and transmission can share the same wind
– Wind has significant effect on transmission line ampacity ratings
– Impacts of wind may prove advantageous to power transmission
– Provisional support for additional wind or other energy at low capital cost
– Coincidental Cooling creates coincidental transmission capacity
Concurrent Cooling – Background
• Challenges
– Inconsistencies of cooling
– Rough terrain and obstructions that are detrimental to cooling
– Relationships between resources and transmission lines
– Line segments that may not receive enough cooling
• Benefits
– Expansions in the integration of renewables
– Short span re-conductoring supports near term capacity increases
Background – Continued
• Identified representative transmission and wind farms
• Developed topographical and roughness models
• Determined initial and follow-on locations for wind speed instrumentation
• INL and IPCO shared in cost of anemometers
• INL and Idaho Power working together; installing, monitoring, and
validating 15 wind data anemometers
• Optimizing locations to capture critical areas (identified 3 new weather
station locations and the desire to move two existing weather stations to
improve area coverage).
• Validating line losses and optimum performance parameters
Background – Continued
• Real-time and historical data collected and used for research and
validation
• Computational Fluid Dynamics (CFD) modeling software package,
WindSim used to better understand the concurrent cooling effects
• Developing a historical database and understanding, freeing
concurrent thermal ampacity ratings
• Various scenarios modeled to understand and determine field
measurements and validity of CFD model
• Developing process and transition to IPCo planning and operations
Area of Interest
Approximately 1500km2
WindSim Model Verification Approach
• Input: 3 – minute averaged climatology data sets
– Wind speed
– Direction
• Various combinations
– Remove 1 – 3 input files
– Compare predicted at the removed location modeled in WindSim
with the actual measured data.
• Investigate percent error results
– Develop historical/statistical database
– Look-up tables with weighting factors for speed and direction
– 500 – 1000 meter separation between modeled points
WindSim Terrain Conversion
48,000,000 cells
WindSim % Error – Layouts 1 & 2 (Original)
Weather Stations Used
Average Wind Speed
(Actual)
Ave Wind Speed of
Average Wind Speed % Error (Data actual vs.
% Error (Data actual vs.
Missing WS (WindSim(WindSim-Adjusted)
WindSim-Adjusted)
WindSim Predicted)
Predicted)
WS03
3.31
3.24
2.1148%
3.27
1.2085%
WS04
3.45
3.39
1.7391%
3.45
0.0000%
WS09
2.97
3.01
1.3468%
3.07
3.3670%
WS10
1.76
1.75
0.5682%
1.95
10.7955%
WS11
3.23
3.15
2.4768%
3.22
0.3096%
WS12
1.99
1.88
5.5276%
2.08
4.5226%
WS03
3.31
3.24
2.1148%
3.29
0.6042%
WS04
3.45
3.39
1.7391%
3.46
0.2899%
WS09
2.97
3.01
1.3468%
3.09
4.0404%
WS10
1.76
1.75
0.5682%
2.73
55.1136%
WS11
3.23
3.15
2.4768%
3.24
0.3096%
WS12
1.99
1.88
5.5276%
2.08
4.5226%
WindSim % Error – Layouts 1 & 2 (Current)
Layout
Layout 1
Layout 2
Weather Stations
Used
Average Wind
Speed (Actual)
Average Wind
Speed (WindSimAdjusted)
WS01
WS02
WS03
WS04
WS05
WS06
WS07
WS08
WS09
WS10
WS11
WS12
WS13
WS15
WS01
WS02
WS03
WS04
WS05
WS06
WS07
WS08
WS09
WS10
WS11
WS12
WS13
WS15
1.95
5.16
2.97
3.82
3.60
4.34
4.11
4.60
3.38
2.85
3.40
3.48
2.80
3.24
1.95
5.16
2.97
3.82
3.60
4.34
4.11
4.60
3.38
2.85
3.40
3.48
2.80
3.24
1.95
5.09
2.83
3.71
3.52
4.31
4.02
4.51
3.22
2.64
3.29
3.37
2.73
3.20
1.95
5.09
2.83
3.71
3.52
4.31
4.02
4.51
3.22
2.64
3.29
3.37
2.73
3.20
% Error (Data Ave Wind Speed of
% Error (WindSim
% Error (Data
actual vs.
Missing WS
Adjusted vs.
actual vs. WindSim
WindSim(WindSimWindSim
Predicted)
Adjusted)
Predicted)
Predicted)
2.13
0.0000%
9.2308%
9.2308%
5.03
1.3566%
2.5194%
1.1788%
3.00
4.7138%
1.0101%
6.0071%
3.75
2.8796%
1.8325%
1.0782%
3.51
2.2222%
2.5000%
0.2841%
4.12
0.6912%
5.0691%
4.4084%
4.00
2.1898%
2.6764%
0.4975%
4.39
1.9565%
4.5652%
2.6608%
3.34
4.7337%
1.1834%
3.7267%
2.81
7.3684%
1.4035%
6.4394%
3.38
3.2353%
0.5882%
2.7356%
3.42
3.1609%
1.7241%
1.4837%
2.84
2.5000%
1.4286%
4.0293%
3.33
1.2346%
2.7778%
4.0625%
0.0000%
2.12
8.7179%
8.7179%
1.3566%
5.04
2.3256%
0.9823%
4.7138%
2.99
0.6734%
5.6537%
2.8796%
3.75
1.8325%
1.0782%
2.2222%
3.52
2.2222%
0.0000%
0.6912%
4.24
2.3041%
1.6241%
2.1898%
4.01
2.4331%
0.2488%
1.9565%
4.43
3.6957%
1.7738%
4.7337%
3.36
0.5917%
4.3478%
7.3684%
3.12
9.4737%
18.1818%
3.2353%
3.38
0.5882%
2.7356%
3.1609%
3.43
1.4368%
1.7804%
2.5000%
2.84
1.4286%
4.0293%
1.2346%
3.77
16.3580%
17.8125%
Look-up Tables
Average Wind Speed and Directions
• A map was created showing the high and low average wind speeds for
each location (by line section).
• The transferred climatologies are color coordinated in accordance to
their respective weather station.
• These values were then used to develop an average summary of the
potential Line Ampacity Ratings due to concurrent cooling on the four
main transmission lines within the study Area of Interest.
Line Sections: Average Wind Speeds
This figure indicates mean average measured wind speeds (mph) from 15
weather stations.
Concurrent Cooling – Wind Data Collection
Data Analysis 1
Data Analysis 1 - Continued
WS002
WS003 WS004 WS005 WS006 WS009 WS010 WS011 WS013 WS015
% Wind Speeds >= 3
mph
91.58%
65.96% 79.86% 76.48% 87.71% 73.28% 56.34% 71.45% 71.20% 76.31%
% Wind Speeds >= 6
mph
77.21%
51.59% 60.16% 55.26% 72.44% 50.34% 38.25% 52.58% 48.33% 59.00%
Jun-10
Jul-10
Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11
% Wind
Speeds >= 53.71% 38.15% 30.95% 25.90% 21.87% 46.50% 33.19% 35.76% 34.91% 39.16% 49.56%
3 mph
% Wind
Speeds >= 30.92% 26.44% 19.12% 16.13% 14.59% 34.36% 21.41% 20.69% 20.67% 26.03% 35.83%
6 mph
Total % Wind Speeds > 3 mph
35.9%
Total % Wind Speeds > 6 mph
23.6%
Data Analysis 1 - Continued
Data Analysis 1 - Continued
Concurrent Cooling – Dynamic Line Rating
System
Dynamic Line Rating
• Equations describing cooling of bare overhead conductors were
developed in the 1920’s.
• IEEE Standard for Calculating the Current-Temperature of Bare
Overhead Conductors. IEEE Std. 738-2006.
– Blowing wind can provide significant additional capability over
minimum wind conditions.
– The problem has always been knowing the weather conditions at
all points along the transmission line.
– Substation equipment must also be rated for the additional
capacity. That may require upgrades of station bus, switches and
other equipment.
– Additional reactive support may be needed.
– The least capable line section or substation device determines the
capability of the complete line.
Dynamic Line Rating – Continued
• The balance equation is used to calculate the steady state
temperature and capacity of a conductor.
• The steady state temperature of a conductor is the value
that balances heat loss from convection and radiation with
heat generated by solar radiation and current flow.
• Two equations are evaluated to calculate heat loss due to
convection.
– Equation 1 is more accurate at lower speeds.
– Equation 2 is more accurate at higher wind speeds.
• The highest value is used for the calculation.
– Pf (air density), Uf (dynamic viscosity), Kf (thermal
conductivity), are all values that must be calculated for
each temperature.
• Energy radiated from the conductor (qr) is dependant on
conductor temperature and ambient temperature.
Dynamic Line Rating – Continued
• Conductor resistance (qR) is a function of conductor temperature (Tc).
– The change is nearly linear over the temperature range of interest.
• Heating of the conductor by solar energy is a function of sun angle (time
of year and time of day), line angle, elevation, and conductor reflectivity.
– A’ in the equation is relationship between the line area, line angle
and solar angle.
– Qse is either measured or calculated solar radiation.
• The steady state current capability of the conductor can be determined
using maximum ambient temperature, conductor maximum temperature,
and present wind conditions.
– Steady state conductor temperature is calculated from present
weather conditions and line loading.
• After the present steady state line temperature and capacity are
calculated, the actual or dynamic line temperature and capacity are
calculated using a (1-e^(-X)) relationship.
– X is the time step divided by the line thermal time constant.
Line Ampacity Calculations – Wind @ 0 degrees
to the line. (Baseline – Worst case)
Conductor
Respective
Weather
Station
Wind Wind
Line
Wind Line
Speed Speed
Point
Angle Azimuth
(MPH) (ft/sec)
Static Summer
Rating
Line
Voltage
(kV)
MVA Amps
Baseline
230.0 433.4 1088.0
ACSR - 715.5
WS7
Dynamic Winter Rating
5 deg C
Line
Voltage
(kV)
MVA
Amps
230.0 433.4 1088.0
Dynamic Summer Rating
40 deg C
Line
Percent Voltage
Change (kV)
MVA Amps
Percent
Change
0%
230.0 433.4 1088.0
0%
90
230.0 1202.3 3018.0 177%
230.0 846.9 2126.0
95%
72%
41%
88%
66%
35%
Ave High
161
9.48
13.94
30
Ave High
161
9.48
13.94
15
90
230.0 1069.2 2684.0 147%
230.0 747.3 1876.0
Ave High
161
9.48
13.94
0
90
230.0 888.4 2230.0 105%
230.0 609.5 1530.0
Ave Low
160
8.306
12.21
30
90
230.0 1157.7 2906.0 167%
230.0 813.5 2042.0
Ave Low
Ave Low
160
160
8.306
8.306
12.21
12.21
15
0
90
90
230.0 1030.2 2586.0 138%
230.0 857.3 2152.0 98%
230.0 717.9 1802.0
230.0 584.8 1468.0
Columns 11 and 15 of Table 2 indicate that even higher ampacity
ratings can be realized when baseline ampacity ratings are
conservative (wind @ 0 degrees, or down-line).
Bottom Lines
• Dynamic line rating is doable, but process is complex
• Wind modeling technology is good, and is getting even better with wind
forecasting and other related research.
• Quality of model outputs depend on how it’s done, how much input
data is used, time periods modeled, terrain complexity, quality of the
data, etc.
• Real questions are how to keep costs manageable, have a fast enough
process, and how much data is enough to stay within
projected/validated error bands with enough granularity to see capacity
improvements at good economic value.
Continuing Work
• Develop historical/statistical database through various scenarios
– Seasonal
– Time of day
– Times when all areas are receiving cooling (or need upgrades to
achieve higher ampacity)
• Improve obstructions and surface roughness layers to improve modeling
accuracy.
• Improve refinement grid for better resolution
• Investigate improvements with better resolution native map files
• Determine areas of highest interest for upgrades/reconductoring
• Determine upgrade costs of identified areas of interest to improve overall
ampacity/capacity of the system
• Expand Area of Interest to include IPCo – INL proposal area
Mobile Met Tower – Modeled Point Validation
Mobile Met Tower Test Point Locations
Met Tower Data ↔ Model Data
230-06 Starling (bundled)
Average Average
Speed Direction
(mph) (degrees)
Test Point 162
10.2
135.4
Model Point 162
7
167.7
6.7
166.3
WS7
Test Point 135
8
233.2
Model Point 135
7.3
237
7.3
235.5
WS7
Test Point 136
7.6
148
Model Point 136
6.6
169.3
6.6
169.3
WS7
Test Point 151
3.8
128
Model Point 151
3.9
191.8
3.9
192.3
WS9
Test Point 152
9.2
157.7
Model Point 152
7.5
196.8
7.8
197.1
WS9
Test Point 159
6.1
120.3
Model Point 159
7.6
109.2
7.8
109.2
WS7
138-06 Penguin
Test Point 116
Model Point 116
WS2
Test Point 108
Model Point 108
WS2
Test Point 95
Model Point 95
WS8
Test Point 147
Model Point 147
WS4
Test Point 84
Model Point 84
WS4
Average Average
Speed Direction
(mph) (degrees)
15.4
234
15
242.9
16.5
240.9
6
187.2
4.8
169.9
5.7
168.6
8.3
184.4
8.1
176.7
8.1
172.5
6.6
180.1
7.5
149.7
8.7
153.8
8.1
167.1
7.7
159.6
8.3
160.1
Model % Difference Directional Corresponding Weather Station
Point Wind Speed Error (deg) Weather Station Distance (miles)
84
-4.40%
-7.6
WS4
1.9
95
-2.20%
-7.7
WS8
0.96
108
-20.20%
-17.3
WS2
10.01
116
-2.60%
8.8
WS2
5.04
135
-9.40%
3.7
WS7
6.01
136
-13.10%
21.3
WS7
5.39
147
14.30%
-30.4
WS4
0.89
151
2.30%
39.1
WS9
5.06
152
-19.00%
-11.2
WS9
5.1
159
24.90%
63.9
WS7
9.33
162
-31.70%
32.4
WS7
7.65
Test Point 147
Test Point 84
Future Items to Address
• Computer programming development for application to operations
– Utilizing look-up tables and/or other methods
– How to handle equipment or communication problems with
particular monitoring equipment
– Calculation of all modeled point parameters, how to sort and which
ones to display
– Best ways to handle line temps, thermal time constants, true-ups
from specific line temp measurements, areas of darker/drier
ground cover, etc.
• Other power system upgrades and modeling to handle effects of
operating at higher ampacities
Power System Operators Control Board Supplemental Screen
Questions?
Questions? Contact:
Kurt Myers, MSEE, PE
208-526-5022
Kurt.Myers@inl.gov
Jake P. Gentle, MSMCE
208-526-1753
Jake.Gentle@inl.gov
INL Wind Website: http://www.inl.gov/wind