impact wind system
TRANSCRIPT
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Impact of Wind Energy on Power
System Operation
Joris Soens
web-event
Leonardo ENERGY
16 February 2006
Katholieke Universiteit Leuven
Faculteit Ingenieurswetenschappen
Departement Elektrotechniek (ESAT)
Afdeling ELECTA
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Presentation 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
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I. Wind power, state of the art
Introduction
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Wind Power
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Levels of installed wind power
in 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
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Control 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
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Generator types for wind turbines (I)
squ irrel cage induct ion generator near ly f ix ed sp eed
always induc t ive load
Turbine
Gridshaft &
gearboxwind
SCIG
~
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Turbine generator types (II)
doubly fed indu ct ion generator variable speed l imited range
react ive pow er control lable
shaft &
gearbox
DFIG
Converter
~Grid
CrowbarTurbineIntroduction
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Turbine generator types (III)
syn ch ronous generator , direct dr ive variable speed wide range no gearbox
react ive pow er control lableIntroduction
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Wind Power
Conclusions
SG
Turbine
Converter
~Grid
Permanent Magnet
ORField Winding
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Interaction 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
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Example: 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
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Example: ride-through requirement by
E.ON Netz (Germany)
1) Each vo ltage d ip rema in ing above red l ine must no t
resul t in disco nnect ion o f the generator
2) Wi th in the grey area, extra react ive power is demanded
from the wind p ower generator to del iver vol tage suppor t
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II. Dynamic modelling of wind power
generators
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Dynamic modelling of wind turbines for
use 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 atturbine nodereference
P and Q
controlled grid
parameters
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Detailed turbine model with
doubly fed induction generator
speedcontroller
turbinerotor
model
pitchcontroller
shaftcoupling
generatorshaftTturb
gen
rotational
transformation(voltage)
sdu
squ
,em ref T
current
controller
gen
rotor converter (2)
and
rotationaltransformation
(current)
rotorconverter
(1)
sdi
sqi
rdi
rqi
,rd ref u
,rq refu
rdu
rqu
rdu
rqu
gen
gen
shaftT
parksqu
park
vwind
uturb
qref
pref
iturb
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Detailed turbine model:
simulation examples
step-wise wind speed increase
voltage dip at turbine generator
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Detailed turbine model:
simulation example I (1)
simulation input: step-wise increasing wind speed
wind speed at hub height
400 600 800 1000 1200 1600 1800 2000
10
20
[m/s]
t ime [s]
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case 1case 2
case 3 & 4
400 600 800 1000 1200 1600 1800 2000
t ime [s]
0,5
1
power
[p.u.]
var iable speed &
pi tch cont ro l
f ixed speed &
pi tch cont ro l
f ixed speed &
no p i tch cont ro l
turb ine power for increasing w ind speed
Detailed turbine model:
simulation example I (2)
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Detailed turbine model:
simulation example I (3)
case 1 & 2
case 3 & 4
400 600 800 1000 1200 1600 1800 2000
t ime [s]
0,5
1
speed
[p.u.]
turbine speed for inc reasing w ind speed
variable speed
turb ine
cons tant s peed
turb ine
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Detailed turbine model:
simulation example I (4)
zoom on turbine speed
case 1 & 2, turbine speed
case 1 & 2, generator speed
case 3 & 4, turbine speed
case 3 & 4, generator speed
var iable speed:
pro pel ler speed
var iable s peed:
generator sp eed
f ixed speed:
pro pel ler speed
f ixed speed:
generator sp eed
995 1000 1005 1010 1015 1020 1025
0.95
1
1,05
t ime [s]
speed
[p.u.]Introduction
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Detailed turbine model:
simulation example II (1)
1000 1001 1002
voltage at turb ine generator
0.4
0.6
1
[p.u.]
0.8
0.2
t ime [s]
simulation input: voltage dip at turbine generator
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D t il d t bi d l
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Detailed turbine model:
simulation example II (2)
turbine speed
generator speed
1000 1005 1010 1015
t ime [s]
0.9
1
1.1
1.2
speed
[p.u.]
propel ler speed
generator speed
prop eller and generator speed dur in g vo ltage dip, forfixed-speedturb ine wi thinduct ion generator
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D t il d t bi d l
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prop eller and generator speed dur in g voltage dip, forvariable-speedturb ine wi thdoub ly fed induc t ion generator
Detailed turbine model:
simulation example II (3)
turbine speed
generator speed
1000 1005 1010 1015
t ime [s]
0.9
1
1.1
1.2
speed
[p.u.]
prop el ler speed
generator sp eed
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D i t bi d l
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Dynamic turbine model:
conclusions
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
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III. Aggregated wind power in the
Belgian control area
Introduction
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Conclusions
Wi d i B l i
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Wind power in Belgium
95 MW wind power in total installed byend 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)
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A t d i d i th B l i
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Aggregated wind power in the Belgian
control area
Time series of aggregated wind power
Value of aggregated wind power
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Conclusions
Ti i f t d i d
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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
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A ailable ind speed data
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Available wind speed data
Wind speed data from meteo-stationsOstend, Brussels, Elsenborn
Three-year period (2001 2003), hourly
resolution
Anemometer height: 10 m
Complementary to data from European
Wind Atlas (turbulence, landscape
roughness)
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Available wind speed data
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Available wind speed data
Ostend
Brussels
Elsenborn
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Scenarios for installed wind turbines
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Introduction
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Conclusions
Scenarios 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]
power[p.u.]
Power curve for variable-speedpitch-controlled turbine
0 5 10 15 20 25 30 35 400
0.2
0.4
0.6
0.8
1
wind speed [m/s]
Power[p.u.]
Power curve for fixed-speedstall-controlled turbine
Scenario I
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Scenario I
Evenly distributed
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Scenario II
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Scenario II
Concentrated
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Scenario III
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Scenario III
One offshore farm
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Scenario IV
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Scenario IV
Scen. II + Scen. III
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Algorithm output:
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Algorithm output:
aggregated wind power time series
1 2 3 4 50
20
40
60
80
100
120
Day (January 2001)
AggregatedWindPowe
rOutput
[%o
finstalled]
Estimated Aggregated Wind Power Outputas Function of Scenario (2001, January 1-5)
Scenario 1
Scenario 2
Scenario 3
Scenario 4
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Quantization of power fluctuations:
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Introduction
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Conclusions
Quantization of power fluctuations:
power transition matrices
Number of occurrences that a power value in hour His in given range
As a function of power value in hour H 1, H4.
Example: H vs. H-1 matrix for Scenario 1
0 - 10 % 10 - 20 % 2 0 - 3 0 % 3 0 - 40 % 4 0 - 5 0 % 50 - 6 0 % 6 0 - 70 % 70 - 8 0 % 8 0 - 90 % 90 - 10 0%
0 - 10 % 10244 1247 166 28 6 2 0 0 0 0
10 - 20 % 1261 2272 826 187 41 8 0 0 0 0
20 - 30 % 160 856 1163 586 172 33 4 3 0 0
30 - 40 % 23 167 589 794 476 113 17 4 1 0
40 - 50 % 4 44 185 435 623 358 94 15 2 0
50 - 60 % 2 8 39 133 343 482 209 49 3 0
60 - 70 % 0 1 7 18 83 216 360 178 14 0
70 - 80 % 0 0 1 1 12 54 175 318 101 0
80 - 90 % 0 0 0 2 4 2 18 95 142 0
90 - 100% 0 0 0 0 0 0 0 0 0 0RelativeWind
Power
Productionin
Hour-1
SCENARIO 1Relative Wind Power Production in the Actual Hour
H vs H 1 matrices for all scenarios
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H vs. H-1 matrices for all scenarios
Scenario I Scenario II
Scenario III Scenario IV
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Value of aggregated wind power
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Value 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
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Capacity factor
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Calculated for separate turbine or for aggregated park
Most important parameter for turbine exploiters, when
money income ~ produced energy
Capacity factor
capacity factor = annual energy production [MWh]installed power [MW] x 8760 [h]
Scenariocapacity factor
[%]
equivalent full-
load hoursI 20 1752
II 26 2278
III 31 2715
IV 29 2540
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Capacity credit:
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Capacity credit:
definition
rel iable capacit yamount of installed capacity in a power system, available with
given reliability to cover the total power demand
loss of load prob abi l i ty(LOLP)
probability that total power demand exceeds the reliable
capacity
capaci ty credi t 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
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Capacity credit:
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Capacity credit:
calculation
( ) (0) exppeak
D
H D H Q
2 ( )plant
plant plant
P
H D H D P p P
H( 0 ) = LOLP = 4 h/year
Assumption: probability thatTotal power demand > (reliable capacity + D MW )
Impact of additional power generator (park), with
production probabilityp( Pplant)
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LOLP graphical
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Introduction
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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 graphical
LOLP
H (D )
Capacity credit graphical
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capacity
credit
extra convention al
pow er plants
LOLP improvement
H (D)
H2(D)
0 500
4
3
2
1
0
Capacity credit graphical
D (Demand not served) [MW]
H (D ) & H2 (D)
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[hour/year]
Absolute capacity credit for
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Absolute capacity credit for
wind power in Belgium
scen I
scen II
scen III
scen IV
1000 2000 3000 40000
100
200
300
400
5000
Installed wind power [MW]
Capacity credit[MW]
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Shortcomings of capacity factor/credit
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Shortcomings of capacity factor/credit
as value indicator
Moment of energy production? Instantaneous demand for electrical energy?
Energy production in next time sample?
True value indicator must reflect difference of achosen paramater, between case with and without
wind power
This requires
Knowledge of entire power system
Dynamic simulation of entire power system
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Dynamic simulation of entire
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Dynamic simulation of entire
power system (1)
Simulation tool PROMIX (Production Mix) Input data:
Parameters for all power plants in control area
o Power rangeo 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)
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Dynamic simulation of entire
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Dynamic simulation of entire
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 variouslevels of installed wind power
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Relative annual abatement of
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Relative annual abatement of
CO2-emission
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]
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Relative annual abatement of
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no reliability
1 h reliability
6 h reliability
12 h reliability24 h reliability
5 10 15 200
2
4
6
8
Installed wind power [% of peak demand]
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Relative annual abatement of
CO2-emission
Scenario IIICO2 emission abatement
[% of reference case]
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Conclusions
Value 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)
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IV. Conclusions
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Conclusions (1)
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Conclusions (1)
Technical challenges for wind power integrationare 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 ofwind power absorption potential at busbar
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Conclusions (2)
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Hourly fluctuations of aggregated wind power inBelgium 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
Co c us o s ( )
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Conclusions
Recommendations for
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further research
Accurate wind speed forecasting
Integrating forecast updates in implementation of electricitymarket
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 grid
Ph.D Joris Soens 15 december 2005, K.U.Leuven
http://hdl.handle.net/1979/161