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Óbuda UniversityPower System Department
The wind
Dr. Péter Kádár
Óbuda University, Power System Department, Hungary
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Draft
• Wind basics• Drivers of the wind energy application• The energy of the wind• Dynamic simulation• Wind forecast
2
Óbuda UniversityPower System Department
The wind… … destroys the forests
Wind basics - Patra, 2012 7
Óbuda UniversityPower System Department
And the wind… …turns our propeller
Wind basics - Patra, 2012 13
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 17
Wind turbine and measurement system
anemometer
1 nap szélirány-időtartamai (perc)
0
100
200
300ÉSZAK
ÉÉK
ÉK
KÉK
KELET
KDK
DK
DDKDÉL
DDNY
DNY
NYDNY
NYUGAT
NYÉNY
ÉNY
ÉÉNY
Eloszlás sűrűségfüggvény
0
2
4
6
8
10
12
14
16
0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5 6
szélsebesség m/s
Mér
ések
dar
absz
áma
Óbuda UniversityPower System Department
Simple windrose
Wind basics - Patra, 2012 19
1 nap szélirány-időtartamai (perc)
0
100
200
300ÉSZAK
ÉÉK
ÉK
KÉK
KELET
KDK
DK
DDKDÉL
DDNY
DNY
NYDNY
NYUGAT
NYÉNY
ÉNY
ÉÉNY
Óbuda UniversityPower System Department
Speed-Weighted windrose
• Direction?• Average speed?• Energy?
Wind basics - Patra, 2012 20
0
50
100
150
200ÉSZAK
ÉÉK
ÉK
KÉK
KELET
KDK
DK
DDKDÉL
DDNY
DNY
NYDNY
NYUGAT
NYÉNY
ÉNY
ÉÉNY
Átlagos szélsebesség súlyozva 2006.09.07.
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 23
Wind (speed) map for 10 m heights
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Wind (speed) map for 75 m height
24
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Daily wind course in diff. heights
A szélsebesség átlagos napi menete különböző magasságokbanSzeged, SODAR
0
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
óra
m/s
30 m 45 m 60 m 75 m 90 m 105 m 120 m 135 m 150 m
25
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Upscaling
• Measurements or calculations on different heights• Upscaling – continuous formula to define the windspeed in
other heights• e.g. Hellmann equation
26
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 27
Local wind profile
200
175
225
250
275
300
325
350
375
5 10 15 20
T á vo lság [k m ]
Ten
gers
zint
fel
etti
mag
assá
g [
m]
N Y
H eg yh átsá l
200
225
250
275
300
350
375
325
Ten
gers
zint
fel
etti
mag
assá
g [
m]
3 m s- 1
3 m s- 1
4 m s- 1
4 m s- 1
5 m s- 1
5 m s- 1
6 m s- 1
6 m s- 1
É
H eg yh átsá l
Óbuda UniversityPower System Department
Historical data(http://www.met.hu/megfigyelesek/index.php?v=Budapest)
Wind basics - Patra, 2012 31
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 32
Global models
• Supercomputing• 27 km -> 2,5 km cubes• Differential equations
system
But• Different measurement points• Different application points
Forecast
Application
measurement
Terrestrial forms
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 33
Professional services
• Numerical weather forecasts• Horizontal and vertical interpolation• Wind Atlas Analysis and Application Program + PARK
modell • Statistical elements• Meteorological models (e.g. ALADIN, MEANDER), other
sources (ECMWF, MM5, HIRLAM, stb.)• Result presentation by heights or by isobar?
Óbuda UniversityPower System Department
Weibull distribution
Wind basics - Patra, 2012 35
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 37
Local speed changes
Speed changes + direction changes = turbulence
Óbuda UniversityPower System Department
Turbulencies
Wind basics - Patra, 2012 38
A szélirány és -nagyság percenkénti változása (turbulencia)
-3,00
-2,00
-1,00
0,00
1,00
2,00
3,00
4,00
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
perc
m/s
és
rad
Szélsebesség változás (m/s)
Szélirány változás (rad)
Óbuda UniversityPower System Department
Local wind speed tower measurements in a wind park, during 4 min, in the range 6-10 m/s
(data from: Mov-R H1 Szélerőmű Kft., Hungary)
Wind basics - Patra, 2012 39
Óbuda UniversityPower System Department
Local wind speed tower measurements in a wind park, during 4 min, in the range 3-6 m/s
(data from: Mov-R H1 Szélerőmű Kft., Hungary)
Wind basics - Patra, 2012 40
Óbuda UniversityPower System Department
Local wind speed tower measurements in a wind park, during 4 min, over 10 m/s
(data from: Mov-R H1 Szélerőmű Kft., Hungary)
Wind basics - Patra, 2012 41
Óbuda UniversityPower System Department
Spread over of the wind energy application
Wind basics - Patra, 2012 42
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Fig.Global cumulative installed wind capacity 1996-2010 Global Wind Energy Council 2010 (GWEC)
MW 43
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Windpower capacity in Europe, 2006, MW
Forrás: www.ewea.org
0
10 000
20 000
30 000
40 000
50 000
60 000
70 000
80 000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
MW
44
Óbuda UniversityPower System Department
Wind energy application in Europe
45
EWEA, 2010
Wind basics - Patra, 2012
Óbuda UniversityPower System Department
Yearly built in wind capacities in Europe
46
EWEA, 2010
20099581MW onshore582MW offshore
Wind basics - Patra, 2012
Óbuda UniversityPower System Department
Some drivers of the windenergy business
• Growing demand for electricity• EU directives• Subventions• Sustainability• Reduction of CO2 emisson
• Green investment boom (ROI 4-5 years)• Employment, etc.
Wind basics - Patra, 2012 48
Óbuda UniversityPower System Department
Simple energy modells
Wind basics - Patra, 2012
1. Tube modelv - speed, V - volume do not changesP – pressure, T – temperature decreeases
Reality: v, V, P, T - changes
50
2. Deccelerating massv - speed, V - volume decreasesP – pressure, T – temperature do not changes
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 51
Power of the wind (moving mass model)
P = 0,5 ρ A v3 ηwhere • P = mechanical (~electrical) power of the wind turbine, • ρ = 1,29 kg/Nm3 – density of the air, • A = r2 π = d2 π / 4 area swept by the rotor blades (r is the
length of the blade, d = 2 r diameter of the rotor), • v = wind speed, • η = efficiency of the rotor (theoretical max. is 60 %,
practically 10-30 % ).
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Obstacle and the flowing air
• a - turbulent• b - laminal• c – turbulent
(stall)
52
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 53
The possible energy conversion
• Fix bladed rotor• TipSpeedRatio:
v blade edge / v air
• 100 %? no
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Different rotors and blades
• P vs wind speed• Different rpm
54
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 55
Practical characteristics of wind turbine with control
Electronic control of• Rotor speed• Pitch
Óbuda UniversityPower System Department
Typical characteristics
Wind basics - Patra, 2012 57
Szélturbina karakterisztikák
-500
0
500
1 000
1 500
2 000
2 500
0 5 10 15 20 25 30
Szélsebesség (m/s)
Kim
enő
tel
jesí
tmén
y (k
W)
V-90-2MW
MM82
G90
V-90-1.8MW
E-48
MD77
E-70
Óbuda UniversityPower System Department
Simulation logic
• Basic questions: „What happened if…”• No yearly averages but• Real wind measurements + • Defined wind park locations + • Wind turbine characteristics• Result: MW curve during a long period (a year)
Wind basics - Patra, 2012 60
Óbuda UniversityPower System Department
Frequency of the output changes
Wind basics - Patra, 2012 62
Szélfarmok összesített kimenő teljesítmény-változásának gyakorisága (10 perces)
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
170
000
150
000
130
000
110
000
90 0
00
70 0
00
50 0
00
30 0
00
10 0
00 0
-20
000
-40
000
-60
000
-80
000
-100
000
-120
000
-140
000
-160
000
Teljesítmény változás 10 percenként
Gy
ak
ori
sá
g
Óbuda UniversityPower System Department
Average wind speed vs monthly production
Wind basics - Patra, 2012 63
Havi szélsebesség átlag, és havi megtermelt energia kapcsolata
0,00
5000,00
10000,00
15000,00
20000,00
25000,00
30000,00
1,50 2,50 3,50 4,50 5,50 6,50 7,50
m/s
MW
h
Agárd
Folyás
Győr
Moson
Túrkeve
Óbuda UniversityPower System Department
Daily energy production vs built in capacity
Wind basics - Patra, 2012 64
A napi átlagteljesítmény gyakorisága a beépített teljesítményre vonatkoztatva
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%84
%
80%
76%
72%
68%
64%
60%
56%
52%
48%
44%
40%
36%
32%
28%
24%
20%
16%
12% 8% 4% 0%
Beépített teljesítmény %-a
Gya
kori
ság
Óbuda UniversityPower System Department
Monthly energy production
Wind basics - Patra, 2012 65
Havonta megtermelt energia
0
20 000
40 000
60 000
80 000
100 000
120 000
Janu
ár
Febru
ár
Már
cius
Április
Máju
s
Júniu
s
Júliu
s
Augus
ztus
Szepte
mber
Októbe
r
Novem
ber
Decem
ber
Hónap
MW
h
Óbuda UniversityPower System Department
Small wind
Wind basics - Patra, 2012 66
Kimenő teljesítmény a minimális energiatermelésű napon - 2005.11.02 (és előtte/utána 1 nappal)
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
0:00
:00
1:40
:00
3:20
:00
5:00
:00
6:40
:00
8:20
:00
10:0
0:00
11:4
0:00
13:2
0:00
15:0
0:00
16:4
0:00
18:2
0:00
20:0
0:00
21:4
0:00
23:2
0:00
1:00
:00
2:40
:00
4:20
:00
6:00
:00
7:40
:00
9:20
:00
11:0
0:00
12:4
0:00
14:2
0:00
16:0
0:00
17:4
0:00
19:2
0:00
21:0
0:00
22:4
0:00
0:20
:00
2:00
:00
3:40
:00
5:20
:00
7:00
:00
8:40
:00
10:2
0:00
12:0
0:00
13:4
0:00
15:2
0:00
17:0
0:00
18:4
0:00
20:2
0:00
22:0
0:00
23:4
0:00
Idő
kW
Túrkeve
Moson
Győr
Folyás
Agárd
Óbuda UniversityPower System Department
Large wind
Wind basics - Patra, 2012 67
Kimenő teljesítmény a maximális energiatermelésű (és átlagteljesítményű) napon - 2005.12.30(és előtte/utána 1 nappal)
0
100000
200000
300000
400000
500000
600000
0:00
:00
1:40
:00
3:20
:00
5:00
:00
6:40
:00
8:20
:00
10:0
0:00
11:4
0:00
13:2
0:00
15:0
0:00
16:4
0:00
18:2
0:00
20:0
0:00
21:4
0:00
23:2
0:00
1:00
:00
2:40
:00
4:20
:00
6:00
:00
7:40
:00
9:20
:00
11:0
0:00
12:4
0:00
14:2
0:00
16:0
0:00
17:4
0:00
19:2
0:00
21:0
0:00
22:4
0:00
0:20
:00
2:00
:00
3:40
:00
5:20
:00
7:00
:00
8:40
:00
10:2
0:00
12:0
0:00
13:4
0:00
15:2
0:00
17:0
0:00
18:4
0:00
20:2
0:00
22:0
0:00
23:4
0:00
Idő
kW
Túrkeve
Moson
Győr
Folyás
Agárd
Óbuda UniversityPower System Department
Meteorological front in and out
Wind basics - Patra, 2012 68
Kimenő teljesítmény a front megérkezésekor
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
Idő
kW
Túrkeve
Moson
Győr
Folyás
Agárd
Kimenő teljesítmény a front elvonulásakor
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
Idő
kW
Túrkeve
Moson
Győr
Folyás
Agárd
Óbuda UniversityPower System Department
The „problematic” days
Wind basics - Patra, 2012 69
Kimenő teljesítmény egy átlagos energiatermelésű napon - 2005.05.17 (és előtte/utána 1 nappal)
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
0:00
:00
1:40
:00
3:20
:00
5:00
:00
6:40
:00
8:20
:00
10:0
0:00
11:4
0:00
13:2
0:00
15:0
0:00
16:4
0:00
18:2
0:00
20:0
0:00
21:4
0:00
23:2
0:00
1:00
:00
2:40
:00
4:20
:00
6:00
:00
7:40
:00
9:20
:00
11:0
0:00
12:4
0:00
14:2
0:00
16:0
0:00
17:4
0:00
19:2
0:00
21:0
0:00
22:4
0:00
0:20
:00
2:00
:00
3:40
:00
5:20
:00
7:00
:00
8:40
:00
10:2
0:00
12:0
0:00
13:4
0:00
15:2
0:00
17:0
0:00
18:4
0:00
20:2
0:00
22:0
0:00
23:4
0:00
Idő
kW
Túrkeve
Moson
Győr
Folyás
Agárd
Óbuda UniversityPower System Department
Correlation analysis of wind measurements
Wind basics - Patra, 2012 70
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
The problem
• Wind production forecast =
wind forecast + turbine caharactereistics
• There is no exact wind forecast for the wind turbine sites• The forecast is crucial for the integration large wind parks
into the network• The question: Can we forecast the generated energy based
on remote wind forecast?
71
Óbuda UniversityPower System Department
Wind basics - Patra, 2012 72
The real task
• Not in that place• Not in that time• Not correct wind forecast
Forecast
Application
measurement
Terrestrial forms
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
The factory characteristics
• In the project we investigate a V-27 turbine at “Bükkaranyos” and wind measurement ant “Folyás” meteorological station.
• The figure shows a typical characteristics of wind turbines (V27). This curve is measured in stationary mode, it does not contain the effect of local turbulences, direction changes and wind speed differences between the upper and lower part of the (spinning) rotor measurements.
73
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Characteristics based on pure measurements
?
74
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Correlation?
The causes of the lack of correlation are• The distance between the wind turbine and wind
measurement• The local wind turbulences that create difference
in the wind blow at the two measurement points.
75
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Other causes: turbulence
• The local wind turbulences that create difference in the wind blow at the two measurement points.
• fast (1-6 sec), the medium (1-6 min) and the slow (1-6 hour) changes.
• The fast and medium wind speed and direction changes are not handled (followed) by the turbine, it causes deviances.
• Turbine dynamics and measurement errors, etc.– Wind speed changes– Wind direction changes– Wind speed changes measurement on minute scale
76
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Distributional reorganization: a functional transformation
An ideal wind speed and power output measurement at the same tower should give the factory characteristics of the wind turbine, the two measurements correlate on the factory curve. If we prepare the cumulative distribution function of both measurements, the previous correlation is still valid and we get the same curve.
77
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Back to the measurements (Bükkaranyos – Folyás: 33km)
78
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Characteristics matching
Based on the above mentioned, the locally differently running curve is substituted by a globally similarly cumulated distribution function. We investigate not the specific synchronized moments but the same period, so we integrate the power into generated energy. This is an energy based characteristics retrieval. Figure shows characteristics similar to the factory characteristics (marked by dots).
79
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Meaurement distances
Name of wind measurement place
Distance of the wind turbine “Bükkaranyos”
Folyás 33 km
Agárd 187 km
Túrkeve 98 km
Mosonmagyaróvár 263 km
Győr 238 km
80
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Wind power generation forecast
See WinDemo!
81
Óbuda UniversityPower System Department
Wind power generation forecast
Rough:• Windspeed• Charactereistic
Fine• + temperature• + pressure• + humidity• + direction
CORRELATION FACTOR!
Wind basics - Patra, 2012 82
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Upscaling
• The previously shown remote upscaling factor is defined by the energy production of a time period. Applying the Hellmann equation (1) for the same tower (height 33 m, measurements height 10 m), the exponent is 0,445, that is a good experimental result.
83
Óbuda UniversityPower System Department
Wind basics - Patra, 2012
Conclusion
• It is not possible to retrieve the vendor given stationary winds speed–generation characteristics of the wind turbine based on the real-time measurements.
• The calculations above show that for real-time generation forecast purposes only close measurement/estimation points could be used.
• The wind forecasts work on worldwide global models, these are theoretically not capable of forecasting local turbulences – which cause the 0,5 - 5 min deviations in the power output.
• In spite of this fact, based on further measurements quite good energy production estimations can be done. We used the cumulative distribution function to define the ratio between remote wind speed measurement and the possible local wind speed at the turbine.
85