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This presentation is based on a thesis which investigated on various levels the technical impact of wind power on the operation of the electrical power system. First, the state of the art of actual wind turbines is briefly reviewed. Then, the importance of specific ‘grid connection requirements’ is explained. These requirements are generally a set of technical demands that wind turbines have to comply with in order not to cause instability of the electrical power grid. This issue has gained importance since the fast increase of installed wind power in some European countries, e.g. Denmark, Germany and Spain. Whether a wind turbine complies with these technical requirements or not can be examined using detailed dynamic models of wind turbines. This is pointed out in this dissertation. In a next part, all wind power production units in one control zone (i.e. a zone where one power system operator controls the transmission system) are hypothetically considered together as one power plant. The value of this aggregated wind power production is discussed, using three different value indicators: 1) the capacity factor, 2) the capacity credit, and 3) the abatement of carbon dioxide emissions by the other power plants in the regarded control zone. This final value indicator highly depends on the grid considered. The value of wind power is worked out for the specific case of the Belgium, for various scenarios of wind power that can be installed in the future.
Citation preview
1
Impact of Wind Energy on Power Impact of Wind Energy on Power System OperationSystem Operation
Joris Soens
web-eventLeonardo ENERGY16 February 2006
Katholieke Universiteit LeuvenFaculteit IngenieurswetenschappenDepartement Elektrotechniek (ESAT)
Afdeling ELECTA
2
Presentation OutlinePresentation Outline
• Introduction: wind power in Belgium, state of the art installed power, turbine types
interaction with power grid
• Dynamic modelling of wind power generators
• Aggregated wind power in the Belgian control area hourly time series
value of wind power
• Conclusions
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
3
I. Wind power, state of the art
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
4
Levels of installed wind power Levels of installed wind power in Europein Europe
Installed [MW]
end 2003
New [MW]
2004
Installed [MW]
end 2004
Germany 14.609 2.037 16.629
Spain 6.203 2.065 8.263
Denmark 3.115 9 3.117
...
Netherlands 910 197 1.078
...
Belgium 68 2895
(> 160 in 2005)
Europe (EU25) 28.568 5.703 34.205
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
5
Control options for wind turbinesControl options for wind turbines
• Speed control fixed speed
variable speed limited range
variable speed wide range
• Reactive power control
• Blade angle & active power control fixed blade
pitchable blade
• Yaw control
highly dependent on generator type
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
6
Generator types for wind turbines (I)Generator types for wind turbines (I)
squirrel cage induction generator nearly fixed speed always inductive load
Turbine
Grid
shaft &gearbox
wind
SCIG
~
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
7
Turbine generator types (II)Turbine generator types (II)
doubly fed induction generator variable speed – limited range reactive power controllable
shaft &gearbox
DFIG
Converter
~Grid
CrowbarTurbine
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
8
Turbine generator types (III)Turbine generator types (III)
synchronous generator, direct drive variable speed – wide range → no gearbox reactive power controllableIntroduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
SG
Turbine
Converter
~Grid
Permanent MagnetOR
Field Winding
9
Interaction with power gridInteraction with power grid
• Until recently: wind power = negative load
• Now: wind power = actively contributing to power system control
o ride-through capability
o voltage control
o output power control
specific grid connection requirements
development requires dynamic models
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
10
Example: ride-through requirementExample: ride-through requirement
• Wind turbine disconnects at light grid disturbance
• Disconnection causes new grid disturbance
• Cascade-effect may result in major sudden loss of
wind power
• Example:
Spain, February 26, 2004
600 MW loss of wind power due to one grid fault
• Therefore: definition of voltage profiles that must not
lead to disconnection
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
11
Example: ride-through requirement by Example: ride-through requirement by E.ON Netz (Germany)E.ON Netz (Germany)
1) Each voltage dip remaining above red line must not result in disconnection of the generator
2) Within the grey area, extra reactive power is demanded from the wind power generator to deliver voltage support
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
12
II. Dynamic modelling of wind power generators
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
13
Dynamic modelling of wind turbines for Dynamic modelling of wind turbines for use in power system simulationuse in power system simulation
• Power system simulation software: simulate dynamically short-circuits, load steps, switching
event .... interaction wind turbine model and grid model:
gridcontrolled wind turbine
grid dispatch & control
wind speed
injected current
voltage at turbine nodereference
P and Q
controlled grid parameters
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
14
Detailed turbine model withDetailed turbine model withdoubly fed induction generatordoubly fed induction generator
vwind
uturb
qref
pref
iturb
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
15
Detailed turbine model: Detailed turbine model: simulation examplessimulation examples
• step-wise wind speed increase
• voltage dip at turbine generator
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
16
Detailed turbine model:Detailed turbine model:simulation example I (1)simulation example I (1)
400 600 800 1000 1200 1400 1600 18000
10
20
30
time [s]
v win
d [m/s]
Simulated increasing wind speed
simulation input: step-wise increasing wind speed
wind speed at hub height
400 600 800 1000 1200 1600 1800 2000
10
20
[m/s]
time [s]
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
17
400 600 800 1000 1200 1400 1600 18000
0.5
1
1.5
time [s]
p turb
[p
.u.]
Turbine active power for increasing wind speed
case 1case 2case 3 & 4
400 600 800 1000 1200 1600 1800 2000
time [s]
0,5
1
power [p.u.]
variable speed &pitch control
fixed speed & pitch control
fixed speed & no pitch control
turbine power for increasing wind speed
Detailed turbine model:Detailed turbine model:simulation example I (2)simulation example I (2)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
18
Detailed turbine model:Detailed turbine model:simulation example I (3)simulation example I (3)
400 600 800 1000 1200 1400 1600 18000
0.5
1
1.5
time [s]
turb
ine s
pee
d [
p.u
.]Turbine speed for increasing wind speed
case 1 & 2case 3 & 4
400 600 800 1000 1200 1600 1800 2000
time [s]
0,5
1
speed [p.u.]
turbine speed for increasing wind speed
variable speed turbine
constant speed turbine
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
19
Detailed turbine model:Detailed turbine model:simulation example I (4)simulation example I (4)
zoom on turbine speed
995 1000 1005 1010 1015 1020 10250.9
0.95
1
1.05
1.1
time [s]
sp
ee
d [
p.u
.]Turbine speed for increasing wind speed
case 1 & 2, turbine speed
case 1 & 2, generator speed
case 3 & 4, turbine speed
case 3 & 4, generator speed
variable speed: propeller speed
variable speed: generator speed
fixed speed: propeller speed
fixed speed: generator speed
995 1000 1005 1010 1015 1020 1025
0.95
1
1,05
time [s]
speed [p.u.]Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
20
Detailed turbine model:Detailed turbine model:simulation example II (1)simulation example II (1)
999 999.5 1000 1000.5 1001 1001.5 1002 1002.5 10030
0.2
0.4
0.6
0.8
1
Voltage at node 104 during fault at this node
time [s]
u 104 [
p.u
.]
1000 1001 1002
voltage at turbine generator
0.4
0.6
1
[p.u.]
0.8
0.2
time [s]
simulation input: voltage dip at turbine generator
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
21
Detailed turbine model:Detailed turbine model:simulation example II (2)simulation example II (2)
995 1000 1005 1010 1015 10200.8
0.9
1
1.1
1.2
1.3
time [s]
turb
ine
an
d g
en
era
tor
spe
ed
[p
.u.]
Turbine and generator speed during fault at node 104, cases 1 & 2
turbine speed generator speed
1000 1005 1010 1015time [s]
0.9
1
1.1
1.2
speed [p.u.]
propeller speed
generator speed
propeller and generator speed during voltage dip, for fixed-speed turbine with induction generator
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
22
propeller and generator speed during voltage dip, for variable-speed turbine with doubly fed induction generator
Detailed turbine model:Detailed turbine model:simulation example II (3)simulation example II (3)
995 1000 1005 1010 1015 10200.8
0.9
1
1.1
1.2
1.3
time [s]
turb
ine
an
d g
en
era
tor
sp
ee
d [
p.u
.]Turbine and generator speed during
fault at node 104, cases 3 & 4
turbine speed generator speed
1000 1005 1010 1015time [s]
0.9
1
1.1
1.2
speed [p.u.]
propeller speed
generator speed
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
23
Dynamic turbine model:Dynamic turbine model:conclusionsconclusions
• Detailed model allows
examination of interaction between turbine and
grid
electrical & mechanical quantities
good understanding of turbine behaviour
thorough insight in mechanical and electrical
behaviour of turbine/grid
simulation of ‘heavy’ transients
help to set up connection requirements
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
24
III. Aggregated wind power in the Belgian control area
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
25
Wind power in BelgiumWind power in Belgium
95 MW wind power in total installed by end of 2004 (onshore)
One offshore wind farm (216 - 300 MW) permitted and near construction phase (start construction soon)
Legal supporting framework for offshore wind farms ‘established’ in January 2005
Best wind resources are offshore or in the west part (near shore)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
26
High voltage grid in BelgiumHigh voltage grid in Belgium
150 kV
220 kV
400 kV
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
27
Aggregated wind power in the Belgian Aggregated wind power in the Belgian control areacontrol area
• Time series of aggregated wind power
• Value of aggregated wind power
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
28
Time series for aggregated wind power Time series for aggregated wind power
• Research project ELIA - ELECTA
• Research goal estimation of hourly fluctuation of aggregated wind power in
Belgium
• Use estimation of need for regulating power
estimation of value of wind power
• Available data Wind speed measurements at three sites in Belgium
Scenarios for future installed wind power
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
29
Available wind speed dataAvailable wind speed data
Wind speed data from meteo-stations Ostend, Brussels, Elsenborn
Three-year period (2001 – 2003), hourly resolution
Anemometer height: 10 m
Complementary to data from European Wind Atlas (turbulence, landscape roughness…)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
30
Available wind speed dataAvailable wind speed data
Ostend
140 km
Brussels110 km
Elsenborn60
km
140
km
preva
iling
wind d
irect
ion
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
31
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
Scenarios for installed wind turbinesScenarios for installed wind turbines
• Turbine type parameters:
power curve
hub height
• Developed algorithm allows arbitrary number of types
• In following application: two turbine types
0 5 10 15 20 25 30 35 400
0.2
0.4
0.6
0.8
1
wind speed [m/s]
po
we
r [p
.u.]
Power curve for variable-speed pitch-controlled turbine
0 5 10 15 20 25 30 35 400
0.2
0.4
0.6
0.8
1
wind speed [m/s]
Po
we
r [p
.u.]
Power curve for fixed-speed stall-controlled turbine
32
Scenario I Scenario I Evenly distributedEvenly distributed
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
33
Scenario IIScenario IIConcentratedConcentrated
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
34
Scenario IIIScenario IIIOne offshore farmOne offshore farm
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
35
Scenario IVScenario IVScen. II + Scen. IIIScen. II + Scen. III
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
36
Algorithm output:Algorithm output:aggregated wind power time seriesaggregated wind power time series
1 2 3 4 50
20
40
60
80
100
120
Day (January 2001)
Ag
gre
ga
ted
Win
d P
ow
er
Ou
tpu
t [%
of
ins
tall
ed
]
Estimated Aggregated Wind Power Output as Function of Scenario (2001, January 1-5)
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
37
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
Quantization of power fluctuations:Quantization of power fluctuations:power transition matricespower transition matrices
• Number of occurrences that a power value in hour H is in given range
• As a function of power value in hour H – 1, H – 4….
• Example: H vs. H-1 matrix for Scenario 1
0 - 10 % 10 - 20 % 20 - 30 % 30 - 40 % 40 - 50 % 50 - 60 % 60 - 70% 70 - 80 % 80 - 90 % 90 - 100%
0 - 10 % 10244 1247 166 28 6 2 0 0 0 010 - 20 % 1261 2272 826 187 41 8 0 0 0 020 - 30 % 160 856 1163 586 172 33 4 3 0 030 - 40 % 23 167 589 794 476 113 17 4 1 040 - 50 % 4 44 185 435 623 358 94 15 2 050 - 60 % 2 8 39 133 343 482 209 49 3 060 - 70 % 0 1 7 18 83 216 360 178 14 070 - 80 % 0 0 1 1 12 54 175 318 101 080 - 90 % 0 0 0 2 4 2 18 95 142 0
90 - 100% 0 0 0 0 0 0 0 0 0 0
Rel
ativ
e W
ind
Po
wer
P
rod
uct
ion
in
Ho
ur
-1
SCENARIO 1Relative Wind Power Production in the Actual Hour
38
H vs. H-1 matrices for all scenariosH vs. H-1 matrices for all scenarios
10 20 30 40 50 60 70 80 90 100
10
20
30
40
50
60
70
80
90
10010 20 30 40 50 60 70 80 90 100
10
20
30
40
50
60
70
80
90
100
10 20 30 40 50 60 70 80 90 100
10
20
30
40
50
60
70
80
90
100
Scenario I Scenario II
Scenario III Scenario IV
10 20 30 40 50 60 70 80 90 100
10
20
30
40
50
60
70
80
90
100
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
39
Value of aggregated wind powerValue of aggregated wind power
• Possible indicators for value of wind power
Capacity factor
Capacity credit
Potential reduction of CO2-emission by total
power generation park in Belgium
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
40
• Calculated for separate turbine or for aggregated park
• Most important parameter for turbine exploiters, when
money income ~ produced energy
Capacity factorCapacity factor
capacity factor =annual energy production [MWh]
installed power [MW] x 8760 [h]
Scenariocapacity factor
[%]equivalent full-
load hours
I 20 1752
II 26 2278
III 31 2715
IV 29 2540
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
41
Capacity credit:Capacity credit:definitiondefinition
• reliable capacity
amount of installed capacity in a power system, available with
given reliability to cover the total power demand
• loss of load probability (LOLP)
probability that total power demand exceeds the reliable
capacity
• capacity credit of wind power
Amount of conventional power generation plants that can be
replaced by a given level of wind power, without increase of
the LOLP
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
42
Capacity credit:Capacity credit:calculationcalculation
( ) (0) exppeak
DH D H
Q
2 ( )plant
plant plantP
H D H D P p P
H( 0 ) = LOLP = 4 h/year
Assumption: probability that
Total power demand > (reliable capacity + D MW )
Impact of additional power generator (park), with
production probability p( Pplant )
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
43
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
-500 0 500 1000 1500 20000
1
2
3
4
5
6
7
D (Demand not served) [MW]
H(D
) [h
ou
rs/y
ea
r]
Estimated LOLP for Belgium
0 500
4
3
2
1
0
D (Demand not served) [MW]
[hour/year]
= 30
Qpeak = 13.5 GW
H(0) = 4 h/year
LOLP graphicalLOLP graphical
LOLP
H (D )
44
-500 0 500 10000
1
2
3
4
5
D (Demand not served) [MW]
H(D
) [h
ours
/yea
r]
Influence of wind park on H(D)
capacitycredit
extra conventionalpower plants
LOLP improvement
H (D)
H2 (D)
0 500
4
3
2
1
0
Capacity credit graphicalCapacity credit graphical
D (Demand not served) [MW]
H (D ) & H2 (D)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
[hour/year]
45
Absolute capacity credit for Absolute capacity credit for wind power in Belgiumwind power in Belgium
0 1000 2000 3000 4000 50000
100
200
300
400
500
Installed wind power [MW]
Ca
pa
city c
red
it [
MW
]Wind power capacity credit for all scenarios
scen I scen II scen III scen IV
1000 2000 3000 40000
100
200
300
400
5000
Installed wind power [MW]
Capacity credit [MW]
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
46
Shortcomings of capacity factor/credit Shortcomings of capacity factor/credit as value indicatoras value indicator
• Moment of energy production? Instantaneous demand for electrical energy?
Energy production in next time sample?
• True value indicator must reflect difference of a chosen paramater, between case with and without wind power
• This requires Knowledge of entire power system
Dynamic simulation of entire power system
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
47
Dynamic simulation of entire Dynamic simulation of entire power system (1)power system (1)
• Simulation tool PROMIX (‘Production Mix’)
• Input data:
Parameters for all power plants in control area
o Power range
o Costs of start-up and continuous operation
o Time for start-up and power regulation
o Fuel consumption, gas emissions... for various operating regimes
Time series of aggregated load in control area (resolution: 1 hour)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
48
Dynamic simulation of entire Dynamic simulation of entire power system (2)power system (2)
• Output: Optimal power generation pattern for every hour Fuel consumption, emissions, costs... for every plant &
hour
• Integrating wind power time series in input data As equivalent reduction of aggregated load For large values: ‘reliable’ wind power required
• Results: CO2-emission abatement for various
levels of installed wind power
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
49
Relative annual abatement of Relative annual abatement of COCO22-emission-emission
0 5 10 15 200
2
4
6
8
10
Installed wind power [% of system peak demand]
CO
2-em
issi
on
ab
ate
me
nt
[%]
Relative annual CO2 emission abatement as
function of installed wind power - scenario I
no reliability 1 h reliability 6 h reliability 12 h reliability 24 h reliability
Scenario I
5 10 15 200
2
4
6
8
Installed wind power [% of peak demand]
CO2 emission abatement [% of reference case]
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
50
0 5 10 15 200
2
4
6
8
10
Installed wind power [% of system peak demand]
CO
2-em
issi
on
ab
ate
me
nt
[%]
Relative annual CO2 emission abatement as
function of installed wind power - scenario III
no reliability 1 h reliability 6 h reliability 12 h reliability 24 h reliability
5 10 15 200
2
4
6
8
Installed wind power [% of peak demand]
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
Relative annual abatement of Relative annual abatement of COCO22-emission-emission
Scenario IIICO2 emission abatement
[% of reference case]
51
ConclusionsConclusionsValue of wind powerValue of wind power
• Capacity factor: 20 - 31 % (spreading)
• Capacity credit: 30 -10 % (installed power)
• CO2 emission abatement:
Optimum: 4% reduction for installed wind power equal to 5% of peak demand ( = 700 MW)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
52
IV. Conclusions
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
53
Conclusions (1)Conclusions (1)
• Technical challenges for wind power integration are identified
• Dynamic models are developed responding to needs of quantifying higher electrical &
mechanical demands towards wind turbines
detailed dynamic models, assessing all mechanical/electrical quantities
simplified dynamic models, allowing rough estimates of wind power absorption potential at busbar
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
54
• Hourly fluctuations of aggregated wind power in Belgium are quantified
• Value of wind power in Belgium assessed with three indicators Capacity factor
Capacity credit
Abatement of CO2-emission by total power generation park
• > 700 MW installed power: wind power ≠ negative load
Conclusions (2)Conclusions (2)
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
55
Recommendations for Recommendations for further researchfurther research
• Accurate wind speed forecasting
• Integrating forecast updates in implementation of electricity market
• Electricity storage
• Demand side management
• Impact of wind power on European border-crossing power flows
Introduction
Dynamic Modelling
Aggregated Wind Power
Conclusions
Impact of wind energy in a future power gridPh.D Joris Soens – 15 december 2005, K.U.Leuven
http://hdl.handle.net/1979/161