INL/MIS-11-22167 Increasing Transmission Capacities with Dynamic Monitoring Systems Kurt S. Myers Jake P. Gentle www.inl.gov 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) Average Wind Speed (WindSim-Adjusted) % Error (Data actual vs. WindSim-Adjusted) Ave Wind Speed of % Error (Data actual vs. Missing WS (WindSim- 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 (WindSim- Adjusted) % Error (Data actual vs. WindSim- Adjusted) Ave Wind Speed of % Error (Data Missing WS actual vs. WindSim (WindSim- Predicted) Predicted) % Error (WindSim Adjusted vs. WindSim Predicted) WS01 1.95 1.95 0.0000% 2.13 9.2308% 9.2308% WS02 5.16 5.09 1.3566% 5.03 2.5194% 1.1788% WS03 2.97 2.83 4.7138% 3.00 1.0101% 6.0071% WS04 3.82 3.71 2.8796% 3.75 1.8325% 1.0782% WS05 3.60 3.52 2.2222% 3.51 2.5000% 0.2841% WS06 4.34 4.31 0.6912% 4.12 5.0691% 4.4084% WS07 4.11 4.02 2.1898% 4.00 2.6764% 0.4975% WS08 4.60 4.51 1.9565% 4.39 4.5652% 2.6608% WS09 3.38 3.22 4.7337% 3.34 1.1834% 3.7267% WS10 2.85 2.64 7.3684% 2.81 1.4035% 6.4394% WS11 3.40 3.29 3.2353% 3.38 0.5882% 2.7356% WS12 3.48 3.37 3.1609% 3.42 1.7241% 1.4837% WS13 2.80 2.73 2.5000% 2.84 1.4286% 4.0293% WS15 3.24 3.20 1.2346% 3.33 2.7778% 4.0625% WS01 1.95 1.95 0.0000% 2.12 8.7179% 8.7179% WS02 5.16 5.09 1.3566% 5.04 2.3256% 0.9823% WS03 2.97 2.83 4.7138% 2.99 0.6734% 5.6537% WS04 3.82 3.71 2.8796% 3.75 1.8325% 1.0782% WS05 3.60 3.52 2.2222% 3.52 2.2222% 0.0000% WS06 4.34 4.31 0.6912% 4.24 2.3041% 1.6241% WS07 4.11 4.02 2.1898% 4.01 2.4331% 0.2488% WS08 4.60 4.51 1.9565% 4.43 3.6957% 1.7738% WS09 3.38 3.22 4.7337% 3.36 0.5917% 4.3478% WS10 2.85 2.64 7.3684% 3.12 9.4737% 18.1818% WS11 3.40 3.29 3.2353% 3.38 0.5882% 2.7356% WS12 3.48 3.37 3.1609% 3.43 1.4368% 1.7804% WS13 2.80 2.73 2.5000% 2.84 1.4286% 4.0293% WS15 3.24 3.20 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% % Wind Speeds >= 3 mph % Wind Speeds >= 6 mph Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 53.71% 38.15% 30.95% 25.90% 21.87% 46.50% 33.19% 35.76% 34.91% 39.16% 49.56% 30.92% 26.44% 19.12% 16.13% 14.59% 34.36% 21.41% 20.69% 20.67% 26.03% 35.83% 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. P f (air density), U f (dynamic viscosity), K f (thermal conductivity), are all values that must be calculated for each temperature. Energy radiated from the conductor (q r ) is dependant on conductor temperature and ambient temperature.
Dynamic Line Rating Continued Conductor resistance (q R ) is a function of conductor temperature (T c ). 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. Q se 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 ACSR - 715.5 Respective Weather Station WS7 Line Point Wind Speed (MPH) Baseline Wind Speed (ft/sec) Wind Angle Line Azimuth Static Summer Rating Line Voltage (kv) MVA Amps Dynamic Winter Rating 5 deg C Line Voltage (kv) MVA Amps 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). Percent Change Dynamic Summer Rating 40 deg C Line Voltage (kv) MVA Amps Percent Change 230.0 433.4 1088.0 230.0 433.4 1088.0 0% 230.0 433.4 1088.0 0% Ave High 161 9.48 13.94 30 90 230.0 1202.3 3018.0 177% 230.0 846.9 2126.0 95% Ave High 161 9.48 13.94 15 90 230.0 1069.2 2684.0 147% 230.0 747.3 1876.0 72% Ave High 161 9.48 13.94 0 90 230.0 888.4 2230.0 105% 230.0 609.5 1530.0 41% Ave Low 160 8.306 12.21 30 90 230.0 1157.7 2906.0 167% 230.0 813.5 2042.0 88% Ave Low 160 8.306 12.21 15 90 230.0 1030.2 2586.0 138% 230.0 717.9 1802.0 66% Ave Low 160 8.306 12.21 0 90 230.0 857.3 2152.0 98% 230.0 584.8 1468.0 35%
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) 138-06 Penguin Average Average Speed Direction (mph) (degrees) Test Point 162 Test Point 116 Model Point 162 Model Point 116 WS7 WS2 Test Point 135 Test Point 108 Model Point 135 Model Point 108 WS7 WS2 Test Point 136 Test Point 95 Model Point 136 Model Point 95 WS7 WS8 Test Point 151 Test Point 147 Model Point 151 Model Point 147 WS9 WS4 Test Point 152 Test Point 84 Model Point 152 Model Point 84 WS9 WS4 Test Point 159 Model Point 159 WS7 Average Speed (mph) Average Direction (degrees) 10.2 135.4 15.4 234 7 167.7 15 242.9 6.7 166.3 16.5 240.9 8 233.2 6 187.2 7.3 237 4.8 169.9 7.3 235.5 5.7 168.6 7.6 148 8.3 184.4 6.6 169.3 8.1 176.7 6.6 169.3 8.1 172.5 3.8 128 6.6 180.1 3.9 191.8 7.5 149.7 3.9 192.3 8.7 153.8 9.2 157.7 8.1 167.1 7.5 196.8 7.7 159.6 7.8 197.1 8.3 160.1 6.1 120.3 7.6 109.2 7.8 109.2 Model Point % Difference Wind Speed Directional Error (deg) Corresponding Weather Station 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