impact of wind power on power system operation

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


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


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


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


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


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


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


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


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


Conclusions

12

II. Dynamic modelling of wind power generators

Introduction

Dynamic Modelling


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


Conclusions

14

Detailed turbine model withDetailed turbine model withdoubly fed induction generatordoubly fed induction generator

vwind

uturb

qref

pref

iturb

Introduction

Dynamic Modelling


Conclusions

15

Detailed turbine model: Detailed turbine model: simulation examplessimulation examples

• step-wise wind speed increase

• voltage dip at turbine generator

Introduction

Dynamic Modelling


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


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


Introduction

Dynamic Modelling


Conclusions

18


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


Conclusions

19


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


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


Conclusions

21


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


Conclusions

22

propeller and generator speed during voltage dip, for variable-speed turbine with doubly fed induction generator


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


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


Conclusions

24

III. Aggregated wind power in the Belgian control area

Introduction

Dynamic Modelling


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


Conclusions

26

High voltage grid in BelgiumHigh voltage grid in Belgium

150 kV

220 kV

400 kV

Introduction

Dynamic Modelling


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


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


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


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


Conclusions

31

Introduction

Dynamic Modelling


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


Conclusions

33

Scenario IIScenario IIConcentratedConcentrated

Introduction

Dynamic Modelling


Conclusions

34

Scenario IIIScenario IIIOne offshore farmOne offshore farm

Introduction

Dynamic Modelling


Conclusions

35

Scenario IVScenario IVScen. II + Scen. IIIScen. II + Scen. III

Introduction

Dynamic Modelling


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


Conclusions

37

Introduction

Dynamic Modelling


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


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


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


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


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


Conclusions

43

Introduction

Dynamic Modelling


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


[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


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


H (D ) & H2 (D)

Introduction

Dynamic Modelling


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


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


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


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


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


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


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


Conclusions

52

IV. Conclusions

Introduction

Dynamic Modelling


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


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


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


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

impact of wind power on power system operation

Technology

mw loss of wind power

requirement wind turbine

pitch control turbine

interaction wind turbine

generator speed fixed

turbine speedvariable

art installed power

extra reactive power