Transcript Document
Difficulties Integrating Wind Generation Into Urban Energy Load
Russell Bigley Shane Motley Keith Parks
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
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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 3
Utility Overview-Load
Understanding Power Usage (load)
Power Load Forecasts
Highly dependent on weather conditions
– Temperatures – Cloud Cover – Precipitation
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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) 5
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 6
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 7
Scheduling Wind Generation?
Many Issues with wind generation 1) Generation is dependent on wind
Generation is typically not static 2) 1) Requires an excellent wind forecast 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) 8
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!!!
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Why is generation so variable & the forecast performance poor.
1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 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!!!!!
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Peetz/Logan Wind Farm
Wind farm over 40 miles across and over 200 turbines 11
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Turbines size: HUGE!!
These are 2.3MW
Seimens turbines located near Adair, IA.
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
15 Pa = 1/2 ρ μ A v3 (2) where μ = efficiency of the windmill (in general less than 0.4, or 40%)
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) 16
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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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 19
Other impacts
Debris buildup on blades
Dirt and insect buildup reduce the aerodynamics around the blade 20
Communication
Information from the wind plant operators is critical in this whole process Downtime due to different causes Maintenance Weather Weather Weather
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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 22
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 23