1. Difficulties Integrating Wind Generation Into Urban Energy Load Russell Bigley Shane Motley Keith Parks

2. 2 Currently in 2009: Xcel Energy is the #1 utility provider of wind in the nation ~2,876 MW’s of Wind Generation on Xcel Energy system

3. 3 Utility Overview  Primary goal  Keep the lights on  Secondary goals  Run at peak efficiency  Prepare for plant maintenance and other outage issues such as transmission

4. 4 Utility Overview-Load  Understanding Power Usage (load)  Power Load Forecasts  Highly dependent on weather conditions –Temperatures –Cloud Cover –Precipitation

5. 5 Utility Overview-Load  Load Forecast Error  Error comes from 2 sources  Model Error  Weather Forecast Error  Load forecast Error (MAE) is typically less than 3%-averaged over the 24 hour period (even day ahead)

6. 6 Generation Forecasting Optimizing Power Plant Output for forecasted Load—Typically this involves scheduling  Coal Power Plants  Gas Power Plants  Hydro/Geothermal Facilities  Wind Plants--highly variable output

7. 7 Generation Assets  Many physical differences in power producing assets  Main concern: Assets that can be dispatched and assets that cannot be dispatched  Wind Generation is non-dispatchable  wind generation can be curtailed  Wind Generation is forecasted and scheduled  Thus there is risk associated with the generation

8. 8 Scheduling Wind Generation?  Many Issues with wind generation 1) Generation is dependent on wind  Generation is typically not static 2) Requires an excellent wind forecast 1) Even a great wind forecast doesn’t result in an accurate generation forecast 3) Accurate Power Curves for wind turbines 4) A better understanding of generation output on a large farm scale basis  Many estimates for total farm output are overestimated (Danish Wind Industry)

9. 9 Wind Generation Forecast Error  Wind Generation forecast Error average around 20% for the 24 hour day ahead period  Persistence is a good forecast in real time, but misses the ramps  How can the forecast be sooo bad!!!

10. 10 Why is generation so variable & the forecast performance poor. 1) Wind speeds are variable 2) Terrain differences 3) Elevation and hub height difference 4) Turbine availability/turbine types 5) Turbine induced wake effects 6) Turbulent eddies induced by terrain 7) Wind speed variations with height 8) Turbine blades build up debris and affect the aerodynamics 9) Weather model resolution 10) Data Data Data 11) Communication with wind farm operators….and there’s more!!!!!

11. 11 Peetz/Logan Wind Farm Wind farm over 40 miles across and over 200 turbines

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13. 13 Turbines size: HUGE!! These are 2.3MW Seimens turbines located near Adair, IA.

14. 14 Generation Forecasting  Wind fields tend to be variable and output is even more variable  Small changes in wind speed tend to make large differences in power generation  Air Density differences also affect the power output (i.e. Summer vs. Winter)  Power Curves are not well documented and are performed at sea level and at standard temperatures

15. 15 Pa = 1/2 ρ μ A v3 (2) where μ = efficiency of the windmill (in general less than 0.4, or 40%)

16. 16 Wind Forecasting  Wind direction can make a huge impact on power generation as turbine placement enhances turbine wake effects  Wake effects can propagate up to 10 times the blade diameter of the turbine (Danish Wind Industry Assocation) Blade Lengths are ~35 meters (~114 ft) long The Diameter is then over 70 meters (~230 feet Wake can propagate up to 700 meters (~2296 ft)

17. 17 A rare, aerial photo of an offshore windfarm in Denmark clearly shows how turbulence generated by large turbine rotors continues to build with each successive row of turbines.

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19. 19 Weather Impacts  High Winds  Turbines ‘cut-out’ at a predetermined wind speed to prevent damage to the turbine (blades, generator, etc.)  Cold Temps  Turbines ‘cut-out’ at predetermined temperatures to prevent damage  Precipitation  Rain and snow reduce power output  Freezing Rain may damage blades and throw ice  Decreases power output

20. 20 Other impacts  Debris buildup on blades  Dirt and insect buildup reduce the aerodynamics around the blade

21. 21 Communication  Information from the wind plant operators is critical in this whole process  Downtime due to different causes  Maintenance  Weather

22. 22 Key Issues and Solutions  Wind and generation data  Attempting to acquire all wind speed, wind direction, and generation data by turbine  1000’s of pieces of data to stream to a database  Modeling  Acquired the assistance of NCAR and NREL (National Central for Atmospheric Research and the National Renewable Energy Lab)  Use latest modeling technology and bias corrections to achieve better results for real-time and day-ahead wind and generation forecasts

23. 23 Without improvements in Communication with wind plant operators Data at the Turbine Level & Modeling we head down a dangerous path if we plan on integrating even more wind on our systems. youtube video: turbine failure