I/En 2006-02 The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects Cornel Ensslin Alexander Badelin Yves-Marie Saint-Drenan

I/En 2006-02

The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects

Cornel Ensslin

Alexander Badelin

Yves-Marie Saint-Drenan

ISET Institut für Solare Energieversorgungstechnik Kassel, Germany

[email protected]

„Capacity Credit“ in National Studies

Comparison of Methodologies

Empirical Investigation „Germany 2000“

Conclusions, outlook to future studies

I/En 2006-02

Concept and New Developments

National Wind Power Integration Studies

ILEX 2002: ILEX Energy Consulting & UMIST: “Quantifying the System Costs of Additional Renewables in 2020”, A report of Department of Trade & Industry and Manchester Centre for Electrical Energy, UMIST, October 2002.

GH 2003: P. Gardner, H. Snodin, A. Higgins, S. McGoldrick (Garrad Hassan and Partners); The Impacts Of Increased Levels Of Wind Penetration On The Electricity Systems Of Republic Of Ireland And Northern Ireland ; Scotland, February 2003.

DTI 2003: The Carbon Trust, DTI: “Renewables Network Impacts Study”, 2003

NOVEM 2003: Jaap `t Hooft, Novem: “Survey of integration of 6000 MW offshore wind power in the Netherlands electricity grid in 2020“, NOVEM, 2003.

DENA 2005: Konsortium DEWI / E.ON Netz / EWI / RWE Net / VE Transmission: Energiewirtschaftliche Planung für die Netzintegration von Windenergie in Deutschland an Land und Offshore bis zum Jahr 2020; Berlin 2005

PSE 2003: Gdańsk division of Institute of Power Engineering :“Study of impact of wind energy development on operation of the Polish power system”, 2003

I/En 2006-02


PhD work on Wind Power Integration

Holttinen 2004: "The Impact of Large Scale Wind Power Production on the Nordic Electricity System. Engineering Physics and Mathematics." December 2004

Sontow 2000: Sontow, Jette: "Energiewirtschaftliche Analyse einer großtechnischen Windstromerzeugung." Dissertation an der Fakultät Energietechnik der Universität Stuttgart, Juli 2000

Giebel 2000: G. Giebel, "On the Benefits of Distributed Generation of Wind Energy in Europe", Dissertation Carl von Ossietzky Universität, Oldenburg, 2000.

Dany 2001: Dany, Gundolf: "Kraftwerksreserve in elektrischen Verbundsystemen mit hohem Windenergieanteil"

Focus on:

Wind Power Capacity Credit Balance Management

I/En 2006-02Questions arising from comparing different studies …

What are the methodologies applied in integration

studies?

Which parameters and input data are used?

How can study results be transferred?

What is the sensitivity to parameter changes?

How to represent country-specific characteristics?

I/En 2006-02

Cacacity credit definition applied (here: dena):

The amount of conventional power plant capacity that can

be replaced with wind power, without decreasing the level

of the security of supply for the power system.

Referring to the moment of peak demand.

Risk level: probability of the power system under investigation not to be able to cover its peak demand without electricity import into the system of 1 %, 9 % respectively.

„Capacity Credit” issues in national studies

I/En 2006-02


„Capacity Credit” issues in national studies: Critical issues

Dany 2001: Dany, Gundolf: "Kraftwerksreserve in elektrischen Verbundsystemen mit hohem Windenergieanteil"

DENA 2005: Konsortium DEWI / E.ON Netz / EWI / RWE Net / VE Transmission: Energiewirtschaftliche Planung für die Netzintegration von Windenergie in Deutschland an Land und Offshore bis zum Jahr 2020; Berlin 2005

15%?Capacity credit8%?

Explanation: Dany had assumed 62(!) % capacity factor (‚Winter‘, German Offshore-Windfarms)

Dena used evaluation of 10 historic wind years leading to much lower CF values

I/En 2006-02

ILEX 2002: ILEX Energy Consulting & UMIST: “Quantifying the System Costs of Additional Renewables in 2020”, A report of Department of Trade & Industry and Manchester Centre for Electrical Energy, UMIST, October 2002.

Historic UK wind farm data (1 year)

Transfer of results

„Capacity Credit” issues in national studies Critical issues

I/En 2006-02

Results may not be simply transferred! (here: Capacity Credit, ILEX/UMIST Study)

Source: ILEX2002

Depending of „Level of Supply Security“ and Input data: wind data

!

„Capacity Credit” issues in national studies

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Map of statistical and chronological approaches

Capacity credit calculation / Comparison of methodologies

I/En 2006-02

“Model path” followed by Giebel for assessing a European wind power capacity credit [Giebel 2000]

Capacity credit calculation

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“Model path” for capacity credit calculation applied in the ‘dena study’


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Different estimators for wind power in the moment of peak demand

Case study ‘Germany 2000’

0

10

20

30

40

50

60

70

80

90

100

01.11.2000 08.11.2000 15.11.2000 22.11.2000 29.11.2000 06.12.2000 13.12.2000 20.12.2000 27.12.2000

Cumulative wind powerGermany,15min values

Hour of peak demandGermany 2000:14/11/2000 18:00

mean wind powerall values 2000

wind power at hour of annualpeak demand

mean wind power20 coldest days

in 2000

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“Model path” for capacity credit calculation applied in the ‘dena study’


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Wind power capacity credit in the dena-study


Source: dena-studyProbability

Power

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Probabilistic combination of wind / conventional power


iiconv b y :P iiwind a x:P

i

ia 1 i

ib 1

I/En 2006-02Capacity credit calculation / Comparison of methodologies

iiSyst c z :P

i

ic 1convwindSyst P P P

xix

xi bac





Effect of bias in wind power time series

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Wind power probability density





I/En 2006-02Capacity credit calculation

Model path for capacity credit calculation applied in the ‘ILEX/UMIST study’

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Reference case “Germany 2000”: Geographic distribution of installed capacity

Empirical Investigation: Case study ‘Germany 2000’

For Germany (year 2000), we know the true geographical distribution of wind capacity

Motivation for the case study:

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Cumulative wind power time series Germany, by ISET / SepCaMo


We have a reliable approximation of wind power feed-in time series in 2000

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Power probability density of total wind power feed-in, Germany 2000


0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

16,0%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Momentary Power / Rated Capacity

Pe

rcen

tag

e o

f T

ime

of

the

Yea

r

Cumulative Wind Power, Germany 2000

I/En 2006-02Empirical Investigation: Case study ‘Germany 2000’

Parameter variation:

Input wind regime (wind years)

Roughness length z0

Wind turbine hub height

Regional distribution of wind farms

Level of supply security




Roughness length z0




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Variation of mean annual wind resource in different German regionsbetween 1993 and 2003


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Sensitivity of wind power capacity credit to different input wind years


0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Installed wind capacity [GW]

Ca

pa

city

cre

dit

(i

n %

of

the

ins

talle

d w

ind

cap

acit

y) 1996

1994

2002

2000

1998

2003

Calculation based on wind year

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Sensitivity of wind power capacity credit Here: Variation of input wind regime


1994 1996 1998 2000 (ref)

2002 2003

Capacity credit

687 MW

520 MW

581 MW

579 MW

565 MW

535 MW

Deviation from reference value [%]

+18.6 - 10.2 + 0.4 0.0 -2.4 -7.6




Roughness length z0




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0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12 14 16

u2

h2

z0 = 0,001

z0 = 0,06

z0 = 0,12

z0 = 1

Here: Variation of roughness length assumption


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0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12 14 16

u2

h2

z0 = 0,06

Reihe5

Here: Variation of hub height assumption


I/En 2006-02

Sensitivity of wind power capacity credit Here: Variation of hub height and roughness length


Z0 0.001 m 0.06 m

(reference)

0.12 m 1 m

Capacity

credit


+12.2 % 0.0 % -6.2 % -21.3 %

Hub height 40m

50m

54m

(ref)

60m

80m

Capacity

credit


-6.3 % -1.4 % 0.0 +2.2 % +7.9%




Roughness length z0




I/En 2006-02Geographical allocation of wind capacity (Scenarios)

ISET (Germany) Balea, Kariniotakis (France)

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Variation of mean annual wind resource in different German regionsbetween 1993 and 2003


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Influence of variation in geographical distribution of wind farm sites


Regional distribution

Reference: Correct distribution

All wind capacity equally distributed

Maximum error (inversely distributed)

Capacity credit Deviation from reference value [%]

0.0 % - 7.5 % - 15.8 %




Roughness length z0




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Dependency of capacity credit on ‘security of supply” level applied (case study ‘Germany 2000’)


I/En 2006-02Summary

Results of national integration study may not be simply transferred.

Aggregated wind power time series are key factor for modelling

accuracy.

Bias comes from biased samples (statistically insufficient number

samples) and biased estimator (systematical deviations): e.g. from

using as indicator specific months only, temperature, ….

The sensitivity analysis described in this work for the case study

‘Germany 2000’ showed capacity credit deviations for the factors of

influence:

wind regime: -7.6% (2003) … +18.6 %(1994)

roughness length: ~- 6% (12cm)

hub height: ~+-3% / 10m deviation

distribution of sites: -15.8% (max. capacity shifted to inland)

Level of security of supply: +6.9% (91% instead of 99%)

I/En 2006-02Summary / Outlook to future studiesResults of capacity credit calculations are more accurate, if the

following requirements are respected:

Best-possible sample of wind data.

The variation in probability densities of wind power in different

wind years is covered by a sufficient number of data;

Offshore installation scenarios are treated with extra efforts in

order to take the special boundary layer conditions into account;

Sufficient number and distribution of reference sites for spatial

extrapolation;

Best possible scenario assumptions for regional distribution of

wind farm sites,

I/En 2006-02Thank you for your attention!

Applications-oriented Research and Development Wind Energy Photovoltaics Use of Biomass Energy Conversion and Storage Hybrid Systems Energy Economy Information and Training

Systems Technology for the Utilisation of Renewable Energies and for the Decentral Power Supply

Institut für Solare Energieversorgungstechnik e.V.

Contact: [email protected]

www.iset.uni-kassel.de

http://reisi.iset.uni-kassel.de

Documents

I/En 2006-02 The Influence of Modelling Accuracy on the Determination of Wind Power Capacity Effects Cornel Ensslin Alexander Badelin Yves-Marie Saint-Drenan