15a energy yield calculations

7/30/2019 15A Energy Yield Calculations

1/16

1

Deutsches Windenergie - Institut GmbH http://www.dewi.de

Some comments to results ofEnergy yield Calculations

Peter Busche

Deutsches Windenergie-Institut GmbH,DEWI Wilhelmshaven


Content

Considerations on roughness and wind speed profiles

Accuracy of the orographic model

Handling of Weibull-data

Wind farm calculations

Reduction margins


2/16

2


meteorologicallong term data

distance: several 10 km

meteorologicalcomputer model

energy yield prognosis

in situ windmeasurements

long term correlation

energy yield evaluation

micro - siting - model

meteorologicallong term data

Methods of energy prognosis


Roughness values

Class: 0 1 2 3 z0

z0 [m] 0.0002 0.03 0.10 0.40 [m]

3 1 0.001

3 1 0.002

3 1 0.003

2 2 0.004

2 1 1 0.006

2 1 1 0.010

2 2 0.009

2 1 1 0.015

2 2 0.025

1 3 0.011

1 2 1 0.017

1 2 1 0.027

1 1 2 0.024

1 1 1 1 0.038

1 1 2 0.059

1 3 0.033

1 2 1 0.052

1 1 2 0.079

1 3 0.117

3 1 0.042

3 1 0.064

2 2 0.056

2 1 1 0.086

2 2 0.127

1 3 0.077

1 2 1 0.113

1 1 2 0.163

1 3 0.232

3 1 0.146

2 2 0.209

1 3 0.292


3/16

3

Deutsches Windenergie - Institut GmbH http://www.dewi.deRef.: IEC 61400-1 ed 2; 3.65

Wind profile

( )( )

V z V zz/z

z /zr

r

( ) ( )ln

ln=

0

0

V z V zz

zr

r

a

( ) ( )=

where

V(z) is the wind speed at height z

z is the height above ground

zr is a reference height above ground used for fi tting the profile

zo is the roughness length

is the wind shear (or power law) exponent

Wind Profile

Wind shear law


Determination of roughness from two wind speeds

The roughness lengths can be derived from wind measurements at two heights, aswell: From the logarithmic wind profile, wind speed u for a given height:

0

ln*)(z

z

k

uzu = (1)

withz height above groundu* friction velocityk Krmn constant (0.40)z0 roughness length.

one gets for two heights h1 and h2the relation

=

0

1

0

2

ln

ln

12

z

z

z

z

uuhh (2).

After some modifications it results for the roughness length:

=

12

12)ln()ln(

exp21

0

hh

hh

uu

huhuz (3).

For high measurement heights, the roughness gained in this way, might be far toohigh.


4/16

4


Determination of roughness from turbulence

Another possibility for the determination of the roughness lengths derives from theturbulence of the wind. The turbulence Iis the relative variation of the wind speed:

uI

u

= (4)

with the standard deviation of the wind uand the average of the windu . Usually, theeasy relationship

=

0

ln

1

z

zI (5)

is assumed. Thus, for the roughness and known turbulence values, this means:

=

I

zz

1exp

0 (6)

From our experience, this formula leads to roughness', being slightly lower thanrealistic.


Prediction of wind speeds for different heights

Results for the WAsP-calculations for the referring height

10 m measured 98 m calculated 98 m measured

data: measuring mast Falkenberg, period 2001 - 2002. Source: DWD

measurements


5/16

5


Askervein Hill


Experience at Exemplary Complex Terrain Site 1

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

1500

16001700

1800

1900

2000


6/16

6


Measurement Masts at the Sites

Site 1:

measuringheights 11 m, 22 m

measuring periodmore than one year

orographic height1110 m

Site 2:

measuringheights 19 m, 39 m

measuring periodmore than one year

orographic height1240 m


Terrain (approx. 6 km x 6 km)


7/16

7


N

W E

S

Mast 1 measured (22 m height)

N

W E

S

Mast 2 measured (39 m height)

N

W E

S

Mast 1 calculated with WASP based on Mast 2 data

wind atlas

based on site 1

wind atlas

based on site 2

mean wind speed [m/s] 5.9 6.4

weibull A parameter [m/s] 6.6 7.2

weibull k parameter [-] 2.23 1.86

energy yield [MWh/y] 1063 1385

energy yield relative 100 % 130 %

Comparison of results based on the wind atlas data

from site 1 and site 2, identical measuring period, fora common 600 kW wind turbine, 46 m hub height,

located at site 1

Measured and Calculated Wind Conditions


Experience at Exemplary Complex Terrain Site 2

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

1500

16001700

1800

1900

2000


8/16

8

Deutsches Windenergie - Institut GmbH http://www.dewi.deRight [km]

North[km]

2

1

3

3.2

3.4

3.6

3.8

4

4.2

4.4

4.64.8

5

5.2

5.4

5.6

5.8

6

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

Location of 2 High-Quality Wind Measurement

Masts


calculated

mea

sured

Mast 1 (50 m) Mast 2 (50 m)

Measured and Calculated Wind Conditions


9/16

9


Hufigkeitsverteilung Windgeschwindigkeit

0

50

100

150

200

250

300

350

400

450

500

0 5 10 15 20 25

Windgeschwindigkeit v inNabenhhe [m/s]

ZeittproJahr[h]

Vm=7.0 m/s

Rayleigh-Verteilung

Summe:

8760Stunden

t(i) =275h

Leistungskurve

0

100

200

300

400

500

600

0 5 10 15 20 25

Windgeschwindigkeit v in Nabenhhe [m/s]

elektr

.Le

istung

P[kW]

Jahresenergieertrag

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

Windgeschwindigkeit v in Nabenhhe [m/s]

Ja

hresenerg

ieertrag

E[MWh]

Summe:

1440MWh

E(i) = 95MWh

Wind speed distribution

Power curve

Energy output distribution

Determination of energy yield of a wind turbine

Annual Energy Production

AEP

Annual average wind speed


Comparison of calculation methods (1)

Wind turbine

Nordex N-50P_Tab P_Tab_sec P_Weibull P_Weibull_sec

Weibull/Tab

(sgesamt)

Weibull/Tab

(sector)01_meas10 3'320 3'337 3'323 3'234 100% 97%

04_meas10 2'059 2'069 2'289 2'058 111% 99%

04_meas30 2'130 2'142 2'379 2'134 112% 100%

04_meas2_10 1'992 1'997 2'153 2'010 108% 101%

04_meas2_30 2'202 2'206 2'375 2'225 108% 101%

07_Ca20m 1'862 1'872 1'955 1'835 105% 98%

07_Ca40m 1'922 1'936 2'024 1'919 105% 99%

08_10m 1'325 1'325 1366 1312 103% 99%

08_40m 1'790 1'791 1'853 1'791 104% 100%

JWE011 775 775 786 771 101% 99%

JWE032 1'408 1'408 1'419 1'404 101% 100%

JWE062 1'903 1'904 1'952 1'898 103% 100%

JWE092 2'462 2'464 2'476 2'457 101% 100%

JWE126 2'877 2'882 2'905 2'858 101% 99%

Mean 2'002 2'008 2'090 1'993 104% 99%

Standardev. 631 634 639 618 4% 1%


10/16

10



Wind turbine

GE 1.5 SLP_Tab P_Tab_sec P_Weibull P_Weibull_sec

Weibull/Tab

(total)

Weibull/Tab

(sector)01_meas10 6'401 6'524 6'344 6'373 99% 98%

04_meas10 4'414 4'454 4'754 4'516 108% 101%

04_meas30 4'534 4'575 4'885 4'650 108% 102%

04_meas2_10 4'442 4'469 4'796 4'532 108% 101%

04_meas2_30 4'822 4'862 5'148 4'946 107% 102%

07_Ca20m 3'806 3'864 3'964 3'857 104% 100%

07_Ca40m 3'792 3'861 3'984 3'939 105% 102%

08_10m 3'129 3'139 3255 3124 104% 100%

08_40m 4'109 4'123 4'274 4'138 104% 100%

JWE011 1'882 1'883 1'912 1'876 102% 100%

JWE032 3'261 3'270 3'284 3'260 101% 100%

JWE062 4'280 4'300 4'381 4'279 102% 100%JWE092 5'356 5'393 5'400 5'372 101% 100%

JWE126 6'039 6'097 6'099 6'058 101% 99%

Mean 4'305 4'344 4'463 4'351 104% 100%

Standardev. 1'168 1'191 1'171 1'171 3% 1%



Wind turbine NEG

Micon NM1000/60

Energy Yield

[MWh]

Weibull/Tab

(sector)

Wind distribution 2'079 100%

WASP 2'022 97%

Park model 2'061 99%

Windfarmer, using TAB 2'080 100%

Windfarmer, no TAB 2'030 98%


11/16

11


Wind Farm Calculations

Geometry

Wind Direction

Wind Speed

Power Curve, Thrust Coefficient Curve

Turbulence Intensity (depends on site andatmospheric stratification)

Definition of Park Efficiency:

=

free

park

parkP

P

Deutsches Windenergie - Institut GmbH http://www.dewi.deQuelle: Beyer et.al.: Modelling Tools for Wind Farm Upgrading. Universitt Oldenburg, 1996

Relatively goodaccordance tomeasurements

Simple undexperienced Modell

Normally no high

accuracy require-ments for parkefficiency

No clear improve-ments of the resultsby means of theAinslie-Model (valid

for simple cases)

RisRisRisRis----ParkmodellParkmodellParkmodellParkmodell


12/16

12


AinslieAinslieAinslieAinslie----ModellModellModellModell

Tow-dimensional axis-symmetrical numerical solutionof the equations of motion andcontinuity

Turbulence closure with thehelp of eddy-viscosity model

Incorporation of the ambientturbulence and preceedingwake turbulence

Improved accuracy for high

resolution value and in specialcases

Implemented by:FLaP, Universitt OldenburgWindFarmer, Garrad Hassan

Quelle: Lange et.al.: Improvement of the Wind Farm Model FLAPfor Offshore Applications. Universitt Oldenburg, 2002.


ComparisonComparisonComparisonComparison ofofofof RisRisRisRis---- andandandand AinslieAinslieAinslieAinslie----ModelModelModelModel

Quelle:WindFarmerValidation Report.Garrad Hassan and Partners, 2000

Here: Ris- andAinslie-Model fromWindFarmer,Garrad Hassan

Ris-Model:correspondence of

the integral valuefor normalsituations

Ainslie-Model:Much bettercorrespondence ofthe velocity deficit


13/16

13


RisRisRisRis----ParkmodellParkmodellParkmodellParkmodell

Simple, semi-empiricalmodel

Velocity deficit calculated insimple dependence fromthe rotor thrust

Supposed open angle(slope) of the wake

Linear wake-superposition

u0 u

Quelle: www.wasp.dk


NewNewNewNew VerificationsVerificationsVerificationsVerifications

Quelle: Schlez et.al.: ENDOW: Improvement of Wake Models, 2002.

ENDOW-Projekt:Verification /improvement foroffshore-conditions

Special

conditions, e.g.5-timesoverlapping ofwakes

FLaP: lowerpictures Ainslie-Model activated


14/16

14


WEA

E66

WMT

StandortWEAWTM


NewNewNewNew VerificationsVerificationsVerificationsVerifications (DEWI, wind(DEWI, wind(DEWI, wind(DEWI, wind speedspeedspeedspeed))))

Quelle: Schlez et.al.: ENDOW: Improvement of Wake Models, 2002.

Meas and WF relative single wake velocities (V2/V1) vs Direction

0

0.5

1

1.5

260 280 300 320 340

Azimuth dire ction []

V2

/V1

[-]

Meas V2 / V1

WF V2 / V1

Meas and WF (V3(compound-wake) / V1_inc (WF-derived)) versus Direction

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

100 120 140 160 180

Directi on []

V3/V1[-]

Meas WF

Single wake situation [262.5, 337.5[

Wake Mean = Mean

WF(V2/V1) / Mean

Meas(V2/V1)

1.027

Standard deviation of Wake

Mean0.088

WF'smaximal wind power

over prediction8.3%

WF'smax. WT power over

prediction (100% availability,

cp=16/27)

4.9%

Proportion of WF

inaccuracy over measured

mean wake velocity deficit

11.7%

Compound wake situation [107.5, 162.5[

Wake Mean = Mean

WF(V3/V1) / Mean

Meas(V3/V1))

1.025

Standard deviation of Wake

Mean0.124

WF'smaximal wind power

over prediction7.7%

WF'smax. WT power over

prediction (100% availability,

cp=16/27)4.6%

Proportion of WF

inaccuracy over measured

mean wake velocity deficit

12.8%


15/16

15


ConclusionConclusionConclusionConclusion ParkParkParkPark----ModelModelModelModel

Starting Point

High requirements for accuracy

Relatively large wind farm

High-resolution results required

Conclusion

Ainslie-Model appropriate

FLaP or WindFarmer


Number Type Annual EnergyYield

Free streamMWh/a

Annual En-ergy Yield

In wind farmMWh/a

ParkEfficiency

1 XXX 1677 1660 99.0%

2 XXX 1813 1775 97.9%

3 XXX 1846 1807 97.9%

4 XXX 1784 1741 97.6%

5 XXX 1637 1619 98.9%

6 XXX 1669 1641 98.3%7 XXX 1765 1733 98.2%

8 XXX 1656 1613 97.4%

9 XXX 1704 1667 97.8%

10 XXX 1793 1775 99.0%

11 XXX 1939 1922 99.1%

12 XXX 1848 1831 99.1%13 XXX 1665 1658 99.6%

Sum: 22796 22442 98.4%

Average energy yield per WT 1754 1726

Sum less 3% losses of availability and 1% gridlosses:

21544

Average energy yield per WT 1683 1657 98.4%

Wind farm configuration:

Number of WTs: 13

WT Type: XXXHub Height: 45m

Total installed capacity: 8580kW

Total annual energy yield: 21544

MWh/aWind farm efficiency: 98.4%

Technical availability losses: 3 %Electrical grid losses: 1 %

Typical result of a energy yield prognosis


16/16


Reduction margins for wind farms (example)

Resulting Energy Yield Commentar

Calculated energy yield of farm 32'544 MWh calculation result

Grid and interconnecting station 2.0% assumption

Availability 3.0% assumption

Planned Maintenance 0.3% assumption

Grid availability 0.1% assumption

Cut-out wind speed 0.0%included in calculation

result

Special operating modes 0.0% assumption

Icing / rotor blade degradation 0.5% assumption

Sum 5.9% 30'662 MWh

Reduction Reason

15a energy yield calculations

Documents