wind measurements and data analysis - kthagenda •why do we need wind measurements? •why are...

Wind measurements and data analysis EG2340 Wind Power Systems

Agenda

• Why do we need wind measurements?• Why are accurate wind measurements so important?• Importance of long-term wind measurements• Wind measurements• Data analysis• Wind farms, wake effect and siting• Software and Example

KTH - EG2340 Wind Power Systems - Camille Hamon - 2014

Why do we need wind measurements?


Cost distribution over the project’s lifetime


Source: EWEA, ”The Economics of Wind Energy”, 2009




Larger costs upfront

Compared to conventional energy sources





Smaller O&M costs (zero fuel costs)







Wind Power Planners: minimize the cost of energy Smaller O&M

costs (zero fuel costs)




Wind Power Planners: minimize the cost of energy

Feasibility studies very important!

Feasibility studies: assess the economical feasibility



Wind measurements crucial for making accurate feasibility studies

Agenda



Why are accurate wind measurements so important?


Power in the wind • Power in the wind


Wind speed U Assumed constant

2

22

12

1 ( ) 12 2

kin

kin

E mU

dE d mU dmP Udt dt dt

=

= = =



Udt


Swept area

A

Mass of air going through the turbine per ”dt”:

dmm Volume A U dt AUdt

ρ ρ ρ= × = × × × ⇒ =




2

22 3

12

1 ( ) 1 12 2 2

kin

kin

E mU

dE d mU dmP U AUdt dt dt

ρ

=

= = = =

Power in the wind

• Standard condition: density = 1.225 kg/m3

• A: Swept area = 𝜋𝑅2 , R: radius of the blades• U: Wind speed


312

P AUρ=

Power proportional to the cube of the wind speed: 10% error on the wind speed => 33% error on the power available in the wind

Generated power

• Betz limit: Max extracted power: 16/27 (=0.59) of the power inwind

• In general: less than that (additional aerodynamical losses,losses in the drive train)

- Cp(U): power coefficient = Protor/Pwind <16/27- η: Drive train efficiency= Pgen/Protor

- ηCp(U): overall efficiency = Pgen/Pwind • Cannot be higher than the rated power


31 162 27maxP AUρ=

31 ( )2gen pP C U AUη ρ=

Extracted power


R=30m

Power curve


0

1

2

3

4

5

6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Pow

er (

MW

)

Wind Speeds

Generated power

About proportional to the cube of the wind speed

Power curve

• The power curve is measuredunder standard conditions.

• Ex: air density in standardconditions: 1.225 kg/m3

• Air density varies with- Altitude - Temperature - => assignment 1

• Other local factors important:orography, turbulence, …


Source: Eric Hau, ”Wind turbines. Fundamentals, Technologies, Application, Economics”, Chapter 14, 2013

Agenda



Importance of long-term measurements


Wind variations


Wind variations

• The shorter the time horizon, the larger the variationsin average, because there is an averaging effect whenconsidering large time horizons.

• Important to get long-term data.


Agenda



Wind measurements


Do we need measurements?

Existing measurements from - Meteorological data close to the site: must be processed

with care: • Assumptions when the data was collected?• Height?• Roughness length, obstacles, contour of the local terrain:

must be taken into account when processing the data.• => necessity of making calculations which compensate for

the local conditions under which the meteorologicalmeasurements were made.

- Data from existing turbines close to site: excellent guide of the local wind conditions


Where to measure? Which site to choose?

• Wind atlas and maps:- provide estimates of wind

energy resource - Indicate general areas where

high wind resource might exist - Allow wind energy developers

to choose a general area for more detailed examination


Source: Hans Bergström, “Wind resource mapping of Sweden using the MIUU method”, 2007

Wind atlas and maps


Source: European Wind Atlas, http://www.windatlas.dk/europe/Index.htm

Instruments

• Wind speed- Anemometers: they rotate with the wind and,

hence, can give a measure of the wind speed at a given height. Problem with ice/dust that can lodge in the bearing.

- LIDAR/SODAR: use the Doppler effect to measure wind speeds: • Need not be put at a given height.• No problem with ice or dust.• But more costly and less reliable.

• Wind direction: wind vane.

• If possible: 10 minute averages – for severalyears


Agenda



Data analysis


What to do with the measurements?

• Analyze the data to identifymeasurement errors (usuallystrange values such as -999).

• Scale the measurements- Remember what the wind shear

looks like (wind profile with height) - What if the measurements are

made at 20m and the hub height will be at 70m?


0 2 4 60

20

40

60

80Wind shear

Wind speed (m/s)

Hei

ght (

m)

Wind speed at 20m Wind speed at 70m

Scaling

• The wind shear depends on theroughness length, z0, of theterrain

• Examples- Lawn, water: z0 = 0.01 m - Bushland: z0 = 0.1 m - Towns, forests: z0 = 1m

• z0 is the height above groundat which the wind speed iszero, due to friction with theterrain


Scaling – logarithmic profile

• Has its origins in boundary layer flow in fluidmechanics and atmospheric research

• zref: altitude at which we know the wind speed• Uref: wind speed at zref

• z: altitude at which we want to calculate the windspeed

• U: wind speed at z (to be calculated)


0

0

ln( / )ln( / )re rf ef

z zUU z z

=

Logarithmic profile

0 2 4 60

20

40

60

80Wind shear

Wind speed (m/s)

Hei

ght (

m)

z0 = 1 mz0 = 0.1m


Scaling – Power law

• The power law is used by many wind energyresearchers.

• α: power law coefficient. Two ways to calculate it- From the reference values

- From the roughness length


ref ref

U zU z

α

=

( )0.37 0.088ln( )1 0.088ln /10

ref

ref

Uz

α−

=−

210 0 10 00.24 0.096log 0.016(log )z zα = + +

Power law

2 3 4 5 6 70

20

40

60

80

Wind speed (m/s)

Hei

ght (

m)

Wind shear

z0 = 1mz0 = 0.1m


Comparison

0 2 4 60

20

40

60

80

Wind speed (m/s)

Hei

ght (

m)

Wind shear

Power lawLogarithmic law


After scaling

• Draw the wind distribution:- Gather the wind speed measurements in classes ( 0-1

m/s, …, 24-25 m/s,…) - Draw an histogram showing the frequency of occurrence

of each class versus the wind speeds

• Distribution = probability of occurrence of each windspeed


( )?f U

Wind speed distributions

• From time series to frequency distributions:


Source: Robert Gasch and Jochen Twele, “Wind Power Plants. Fundamentals, Design, Construction and Operation”. Chapter 4, 2012.



• Different sites have differentcharacteristics.

• You would not choose the sameturbines for different sites.



Something is wrong!



• Larger wind speeds in Visby• Distribution shifted to the

right (compared toBromma/Malmö)

• => Need a turbine thatwithstands these wind speeds



• Bromma/Malmö: Smaller windspeeds

• Distribution shifted to the left

• => Need a turbine that canoptimally extract power fromthe wind at these low windspeeds.

-5 0 5 10 150

0.05

0.1

0.15

0.2

0.25

Wind speed (m/s)

Prob

abili

ty

Limited information about the site

• Suppose that you onlyhave access to themean wind speed atthe site, and possiblyto the standarddeviation.

• How can you predictthe wind speeddistribution from this limited information?


Mean wind speed

Rayleigh distribution


2

2( ) exp2 4mean mean

U Uf UU U

π π = −

Weibull distribution


1

( ) expk kk U Uf U

c c c

− = −

k>0: shape parameter c>0: scale parameter

k=2 => Rayleigh distribution


• Calculating k and A from the mean wind speed andthe standard deviation

• The Gamma function is included in Matlab and Excel.


1.086

mean

UkUσ

−

=

(1 1/ )meanUc

k=Γ +

Comparison measurements vs. Rayleigh and Weibull


p

Data (Bromma airport)WeibullRayleigh

Power curve vs. Wind distribution


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Extracted power

Distribution = f(U)

Power Curve = P(U)

Energy yield


• How to assess how much energy a wind turbine willproduce each year?

• From the wind speed distribution, f(U), and the powercurve P(U), we can calculate the expected powerproduction.

• h(U): probability of occurence of wind class U

0

( ) ( )

( ) ( )

U cut out

U

wind class

mean prodP E P P U f U dU

P U h U

=

=

= =

≈

∫

∑

Energy yield

• Units:- μ: availability < 1 - h(U): probability < 1 - P(U): W / kW / MW (power) - T: usually hours (8760 hours per year for example) - E[Eprod] = expected energy yield under T: Wh, kWh,

MWh (energy)

• Example: expected yearly energy yield, assumeavailability of 98%


( ) ( )prod me

wind classanE E P time availability T P U h Uµ = × × = ∑

0.98 8760 ( ) ( )

wind classprodE E P U h U = ⋅ ⋅ ∑

Availability

• Accounts for stops due to maintenance, failure, …

• Onshore: Usually around 98-99%. 98% = 7.3 daysper year when the turbine is unavailable. But can havea high impact on the energy yield if these are windydays.

• Offshore: 93-95%.

• Larger than conventional sources:- Nuclear/coal: 70 – 90 % - Gas turbines: 80 - 99%

• But wind turbines do not produce at rated power. Seecapacity factor.


Energy yield



Energy yield - Bromma


Capacity factor

• Wind turbines do not always produce at rated power.• Capacity factor:

• Higher wind speeds => higher capacity factor• Can be used to compare different sites.

• Usually between 0.15 and 0.3


0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Pow

er (

MW

)

Wind Speeds

Generated power

Actual Energy ProductionHypothetical Energy Production at Rated Power

prod

rated

CF

E EP T

=

=

Capacity factor

• Bigger is not always better

• Ex: smaller rated power- => larger capacity factor but smaller energy production

• Criteria often used to assess how good a project is:- costs of produced energy: $/MWh, €/MWh, …


Capacity credit

• Measure the contribution of wind power to systemsecurity.

• Two ways of calculating it- How much conventional capacity can we replace while

maintaining the same level of system security?

- How much load can we add while maintaining the same level of system security?


Different coefficients, different definitions

• Be careful about the following notions; they aredifferent (click on the words to go to the slide ofinterest):- Availability - Power coefficient - Drive train efficiency - Capacity factor - Capacity credit


Agenda

• Why do we need wind measurements?• Why are accurate wind measurements so important?• Importance of long-term wind measurements• Wind measurements• Data analysis• Wind farm, wake effect and siting• Software and Example


Wind farms, wake effect and siting


Wind direction

• Wind rose: plot withinformation sorted by winddirection.

• ”Information” can be- Frequency of winds ->prevailing wind direction

- Frequency*cube of wind speeds

->prevailing direction from which most wind power comes


Wind rose

• Can be used to design layouts of wind farms: makemost of the wind turbines face the prevailing direction(wake effect)


Prevailing wind direction

Other issues than good wind conditions

• Looking for good locations: no obstacles; makeuse of speed-up effect (ex: hill effect)

• Space:- Wake effect: Wind turbines reduce wind speeds as

they extract energy from the wind => shading effect for the turbines standing behind => Put the turbines as far apart as possible

- Grid connection: the longer the distance between the turbines, the higher the cost

=> compromise between grid connection costs and wake effect. - Rule of thumb: 5 to 9 diameters apart in the

prevailing direction, and 3 to 5 diameters apart in the directioni perpendicular to wind direction


Other issues than good wind conditions

• Soil conditions- Must be able to build foundations. - Accessibility of the construction site (shipment of material

with heavy trucks).

• Electrical grid- Connection point to the transmission grid as close as

possible. - Strong grid; otherwise, may need reinforcement.

• Distance to neighbouring houses

• …


Energy yield of a wind farm

• Wake effect: Behind a turbine the air flow is affected=> other turbines standing behind other ones do notproduce as much as the first one.

• Different wake effects for different terrains (differentroughness length).

• Different onshore and offshore.


Wake effect



Wake effect

• Many different ways of handling this effect:

- Simple coefficient:

- Energy per sector, different wake effect per sectors, sum up over the sectors

- CFD: Computational fluid dynamics

- …


( ) ( )wake

wind clasr d

sp oE E N T P U h Uµ µ =

∑

330 MW

1004 MW

1409 MW

1227 MW

Map

– s

ourc

e: S

kelle

fteå

Kra

ft’s

web

pag

e

SE 1

SE 2

SE 3

SE 4

Wind power in Sweden

• 1 October 2013: 3970 MWinstalled wind power capacity

• Peak Load = 27 000 MW• Others:

- Hydropower: 16 000 MW - Nuclear power: 9 360 MW - From fossil fuel: 4 666 MW - Biomass: 3 000 MW

Source: https://www.entsoe.eu/publications/statistics/yearly-statistics-and-adequacy-retrospect/Pages/default.aspx


Wind power variations

Year 2011

SE 1

SE 2

SE 3

SE 4

SE


Agenda

• Why do we need wind measurements?• Why are accurate wind measurements so important?• Importance of long-term wind measurements• Wind measurements• Data analysis• Wind farms, wake effect, siting.• Software and example


Software and Example


Software

• RETScreen• WAsP• WindPro• WindFarmer• Homer• …


Example – where to start?

• Where would you install awind farm in Sweden?


Example – where to start?

• Where would you install awind farm in Sweden?


Let’s choose Malmö

We now need to analyze the wind conditions there.

Example – local wind conditions

• In Sweden, SMHI publisheswind measurements fromits weather stations ->www.smhi.se


http://www.smhi.se/

Example – getting the data

• Download the data http://opendata-download-metobs.smhi.se/explore/

• Import it to analyze it (to Matlab, Excel, …)• Identify the wind speeds and directions in the data• Remove bad values (negative values, usually -999

indicates bad measurements)• Scale the data• Plot the distributions and wind rose.


http://www.smhi.se/klimatdata/meteorologi/dataserier-2.1102

http://www.smhi.se/klimatdata/meteorologi/dataserier-2.1102

Example – distribution


Example - scaling


Distribution shifted to the right because wind speed increases with altitude.

( ) ( )rr

zU z U zz

α

=

Example – comparison: wind speed


Example – comparison: power

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3 x 104

Wind speed (m/s)

Pow

er (W

att)

Power distribution (Watt)

Data (Bromma airport)WeibullRayleigh


Example – comparison: yearly energy yield


Getting the electricity price


The electricity price varies!


Example: Yearly revenue


wind measurements and data analysis - kthagenda •why do we need wind measurements? •why are...

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