impact wind system

7/28/2019 impact wind system

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1

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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3

I. Wind power, state of the art

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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4

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

~

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Dynamic

Modelling

Aggregated

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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II. Dynamic modelling of wind power

generators

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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Detailed turbine model:

simulation examples

step-wise wind speed increase

voltage dip at turbine generator

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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]

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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



Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

D t il d t bi d l


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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



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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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III. Aggregated wind power in the

Belgian control area

Introduction

Dynamic

Modelling

Aggregated

Wind Power

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)

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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)

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions



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Ostend

Brussels

Elsenborn

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Scenarios for installed wind turbines


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Introduction

Dynamic

Modelling

Aggregated

Wind Power

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Scenario II


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Scenario II

Concentrated

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Scenario III


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Scenario III

One offshore farm

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Scenario IV


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Scenario IV

Scen. II + Scen. III

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Quantization of power fluctuations:


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Introduction

Dynamic

Modelling

Aggregated

Wind Power

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions



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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

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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)

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

LOLP graphical


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Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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)

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

[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]

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Dynamic simulation of entire


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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)

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions



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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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

Relative annual abatement of


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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]

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions



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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]

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


CO2-emission

Scenario IIICO2 emission abatement

[% of reference case]

Conclusions


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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)

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions


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IV. Conclusions

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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

Introduction

Dynamic

Modelling

Aggregated

Wind Power

Conclusions

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 ( )

Introduction

Dynamic

Modelling

Aggregated

Wind Power

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

impact wind system

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