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Difficulties Integrating Wind Generation Into Urban Energy Load

Difficulties Integrating Wind Generation Into Urban Energy Load

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Difficulties Integrating Wind Generation Into Urban Energy Load

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

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

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

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

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

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

  13. 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

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

  15. 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 Wake can propagate up to 700 meters (~2296 ft) The Diameter is then over 70 meters (~230 feet

  16. 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.

  17. 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

  18. Other impacts • Debris buildup on blades • Dirt and insect buildup reduce the aerodynamics around the blade

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

  20. 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

  21. Without improvements in Communication with wind plant operatorsData 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