High Quality Wind Data for Energy Systems Models

Iain Staffell, Imperial College London

© Imperial College Business School

High quality wind data for

energy systems models

University of Reading, 5th November 2014

330 GW of capacity

at the start of 2014

33% growth globally

UK has 6th largest

capacity (1st for offshore)

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Wind power is becoming big

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10

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1995 2000 2005 2010

GW

In

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US

Germany

Spain

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UK

330 GW of capacity


33% growth globally

UK has 6th largest


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330 GW of capacity


33% growth globally

UK has 6th largest


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330 GW of capacity


33% growth globally

UK has 6th largest


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Motivation

REF study suggested very bad

things for the wind industry.

“normalised load factor for UK onshore

wind farms declines from a peak of

24% at age 1 to

15% at age 10 and

11% at age 15.”

“few wind farms will operate for more

than 12–15 years.”

“The levelised cost for onshore wind

increases from £86/MWh [DECC] to

£183/MWh”

Hughes (2012), p7;

Report Launch Event, De Morgan House, London (19/12/2012)

• The REF study used no weather data

– Argued it was impossible to get hourly wind speeds at 282 sites

• Econometric model chose wind speeds to maximise R²

– The model chose wrong…

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Motivation

80%

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100%

110%

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130%

2002 2004 2006 2008 2010

DECC Monthly Wind Index

Hughes Model

Our calculations, based on Hughes’s data.

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Motivation

• But still people listened…

• Many studies use wind speed data from UK Met masts*

– Sizeable effort required (data cleaning and validation)

– Non-transferable results (where do I get data for Country X?)

– Poor accuracy (correction factors & heuristics needed)

• Reanalysis is an emerging option†

– Data is already cleaned and homogenised

– Global coverage for the last 30 years

– Problem is that ‘data’ comes from a numerical weather

model’s simulation of the atmosphere. Can it be trusted?

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Simulating wind farm output

*Holttinen, 2005; Sinden, 2007; Strbac et al, 2007; Oswald et al, 2008; Sturt & Strbac, 2011; Green et al, 2014. †Kiss et al, 2009; Pöyry, 2009; Tradewind, 2009; Hawkins et al, 2011; Kubik et al, 2012; Ofgem, 2012.

The Virtual Wind Farm Model

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(a) Take NASA hourly wind

speeds from MERRA 10

25 m/s

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15

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5

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(b) Interpolate from grid points

to site of each wind farm

The Virtual Wind Farm Model

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rWind speed (m/s)

Farm

Turbine

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igh

t a

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Wind speed (m/s)

NASA data

Log wind profile

Extrapolated speedat hub height

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(d) Convert from wind speed

to power output using

whole-farm power curve

(c) Extrapolate wind speeds

to hub height with place-

and time-specific parameters

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Simulating GB Wind

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Jan-2012 Jul-2012 Jan-2013 Jul-2013 Jan-2014

Elexon FPN Data

VWF Simulation

Fleet Output (GW)

Staffell, 2014. Developing an Open-Access Wind Profile for Great Britain. Written for DECC.

Results from simulating the GB fleet over two years.

– Comparison made against historic data from the 48 farms

which report their half-hourly output to Elexon

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Simulating GB Wind

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Power Swing (MW)

FPN Data, 4 hours Simulation, 4 hours

FPN Data, 30 minutes Simulation, 30 minutes

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Elexon FPN Data

VWF Simulation

Fleet Output (GW)

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Weather Correction

Burradale Wind Farm

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Weather Correction

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Reported monthly load factors Weather corrected load factors

Trend: –0.123 points per year

2013

• Metered output from Burradale

– Can you spot a trend?

• Estimated output if all months had ‘average’ weather

– Reduces the noise and reveals periods of downtime

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Weather Correction

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2013

• Find the average rate of decline

– Fit a straight line (as we economists are simple people)

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Weather Correction

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• Repeat for all 282 large

farms in the UK

• Average farm loses

0.4 ± 0.1 points per

year

– Decline from 28.5%

new to 21% at age

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• Degradation adds 9%

to the levelised cost of

wind

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How do wind farms degrade?

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-3 -2 -1 0 1 2 3

Nu

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Degradation rate (annual change in CF)

Late 2000s

Early 2000s

Late 1990s

Early 1990s

Cauchy fit

• Are more recent

turbines performing

better?

• Large spread of

results – need to

increase n to

decrease σ

• What are the

engineering causes?

• So… more work to do…

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How do wind farms degrade?

Mo

re r

ece

nt

Faster decline

• Mechanical wear?

• Early death?

• Increasing downtime?

• O&M practices?

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Why do wind farms degrade?

• MSc project to validate

MERRA in other countries

– 5,000 individual turbines

in Denmark

– 350 farms in the US

• Try to match monthly

output from 2001 to

2014

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Expanding the Study

X. Yao, 2014. Assessing the reliability of NASA MERRA in estimating wind power outputs.

• Similar quality of

results to the UK

– 60% of turbines

have a monthly R²

over 90%

– Overall average is

only 82%, which

can improve with

data cleaning

• To be expected?

– DK is rather similar

to UK after all…

Validation in Denmark

22 X. Yao, 2014. Assessing the reliability of NASA MERRA in estimating wind power outputs.

• Very mixed results

– R² averages 49%

– 64% in central and

eastern US

– Just 13% on the

west coast –

completely wrong

seasonal pattern!

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Validation in the US

X. Yao, 2014. Assessing the reliability of NASA MERRA in estimating wind power outputs.

• Poorest performance all grouped on the west coast

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• Poorest performance all grouped on the west coast

• MERRA’s spatial resolution is an obvious flaw

• But how does this translate to seasonal trends?

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MERRA: 3TIER:

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And now for something

completely different…

• Very limited number of

ground-based stations to

compare against

• GHI appears very good

– R² of 95–98%

• DNI more difficult

– R² of 77–87%

Solar Validation in MENA

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DNI GHI

S. Thalanany, 2014. Challenges In

Accurately Modelling Solar Energy Potential

In The Middle East And North Africa

• Modelling PV panels is relatively good

• 227 panels, 17 locations, summary of individual R²

• Quality more dependent on meta-data than solar data

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Solar Validation in the UK

S. Thalanany, 2014. Challenges In Accurately Modelling Solar

Energy Potential In The Middle East And North Africa

• Failure to represent the wind properly can yield very

wrong results

• The Virtual Wind Farm model can robustly simulate the

half-hourly outputs from UK wind farms

• It has demonstrated how output declines gradually with

age

– This rate is similar to other turbine machinery

– Lost energy adds 9% to the levelised cost of electricity

• There is a lot more to learn about degradation…

• There are many other energy systems modelling tasks

that this could be used for… 29

Conclusions

http://tinyurl.com/wind-decline [email protected]

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Conclusions

http://tinyurl.com/wind-decline



mailto:[email protected]

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Backup slides

• Current UK fleet (~10 GW)

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Evolving National Output

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GW Current + Planned + Offshore

• Approved and under construction (~20 GW)

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• Round 2 and 3 ‘super offshore’ (~50 GW)

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Diversity in Europe

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01-Jan 05-Jan 09-Jan 13-Jan

UK

• Ireland’s weather precedes Britain’s by a few hours…

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Diversity in Europe

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Ireland

• Small smoothing effect by combining their outputs…

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Diversity in Europe

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Ireland

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• France is at lower latitudes and gets different weather…

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Diversity in Europe

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• Germany is a few hours behind Britain

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Diversity in Europe

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Ireland

France

Germany

Combined

• The variance across 5 neighbours is reduced by a third…

– Based on assumed 2020 installed capacities

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Diversity in Europe

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UK

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France

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Norway

Combined

• Tweak the ratio of capacities (lots more in Norway) and

variance is less than half…

– At the expense of some output…

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Diversity in Europe

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UK

Ireland

France

Germany

Norway

Combined

High Quality Wind Data for Energy Systems Models

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