Difficulties Integrating Wind Difficulties Integrating Wind Generation Into Urban Generation Into Urban

Energy LoadEnergy Load

Russell BigleyShane MotleyKeith Parks

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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

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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 ErrorWeather Forecast Error

Load forecast Error (MAE) is typically less than 3%-averaged over the 24 hour period (even day ahead)

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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

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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

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Scheduling Wind Generation?

Many Issues with wind generation

1) Generation is dependent on wind Generation is typically not static

2) Requires an excellent wind forecast1) Even a great wind forecast doesn’t result in an

accurate generation forecast

3) Accurate Power Curves for wind turbines4) A better understanding of generation output

on a large farm scale basis Many estimates for total farm output are

overestimated (Danish Wind Industry)

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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) Wind speeds are variable2) Terrain differences3) Elevation and hub height difference4) Turbine availability/turbine types5) Turbine induced wake effects6) Turbulent eddies induced by terrain7) Wind speed variations with height8) Turbine blades build up debris and affect the

aerodynamics9) Weather model resolution10) Data Data Data11) 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

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Turbines size:

HUGE!!

These are 2.3MW

Seimens turbines located near Adair, IA.

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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

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

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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)

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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 damagePrecipitation

Rain and snow reduce power output Freezing Rain may damage blades and throw ice

Decreases power output

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Other impacts

Debris buildup on blades Dirt and insect buildup reduce the aerodynamics

around the blade

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Communication

Information from the wind plant operators is critical in this whole process Downtime due to different causes

MaintenanceWeatherWeatherWeather

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Key Issues and Solutions

Wind and generation data Attempting to acquire all wind speed, wind

direction, and generation data by turbine1000’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

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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