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FLEXIBLE DIMENSIONING OF CONTROL RESERVE BY MEANS OF
A STOCHASTIC MODEL – A FUTURE APPLICATION
Stefan Kippelt | INREC Essen, 06.03.2012
2
Outline
Introduction
Analysis of Historical Tertiary Control Reserves Activation
Flexible Dimensioning of Control Reserves
Demand for Control Reserves in Future Scenarios
Summary and Outlook
Stefan Kippelt | INREC Essen, 06.03.2012
Introduction
Generation Load
Utility Frequency
50Hz
Load Noise
Load Forecast Error
Plant Failure
Control Reserves
? ? ? ? Forecast Error Renewables
What is the influence of the increasing share of renewable energy
generation on the demand for Tertiary Control Reserves?
How does this influence the German Tertiary Control Reserve Market?
MW
3 Stefan Kippelt | INREC Essen, 06.03.2012
4
Outline
Introduction
Analysis of Historical Tertiary Control Reserves Activation
Flexible Dimensioning of Control Reserves
Demand for Control Reserves in Future Scenarios
Summary and Outlook
Stefan Kippelt | INREC Essen, 06.03.2012
Analysis of Historical Tertiary Control Reserves Activation
Changes can largely be explained by regulatory framework and special
conditions
No explicit correlation to renewable energy generation
1 6 12 [h] 240
10
[GWh]
30
Uhrzeit
Ein
gesetz
te M
RL
+
2008
1 6 12 [h] 24Uhrzeit
2009
1 6 12 [h] 24Uhrzeit
2010
1 6 12 [h] 24Uhrzeit
2011
1 6 12 [h] 240
20
40
[GWh]
80
Uhrzeit
Ein
gesetz
te M
RL
-
2008
1 6 12 [h] 24Uhrzeit
2009
1 6 12 [h] 24Uhrzeit
2010
1 6 12 [h] 24Uhrzeit
2011
5 Stefan Kippelt | INREC Essen, 06.03.2012
Time of Day Time of Day Time of Day Time of Day
Acti
vate
d T
C -
A
cti
vate
d T
C +
Time of Day Time of Day Time of Day Time of Day
6
Outline
Introduction
Analysis of Historical Tertiary Control Reserves Activation
Flexible Dimensioning of Control Reserves
Demand for Control Reserves in Future Scenarios
Summary and Outlook
Stefan Kippelt | INREC Essen, 06.03.2012
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Flexible Dimensioning of Control Reserves – Model Structure
Stefan Kippelt | INREC Essen, 06.03.2012
Energy Scenarios Historical Data
Residual Load
Installed Capacity Renewables
Market Model
Power Plant Fleet
Renewable Feed-in Profiles
Hourly need for tertiary control reserves
Hourly Power Plant Schedule (incl. failure probabilities)
Scaled Time Series (Load, Wind, PV)
Load Profil Max. Load
Dynamic Model Need and activation of tertiary reserves
Hourly activation of tertiary control reserves
Forecast Errors
5618 5642 5666 5690 5714 5738 [h] 57860
4
8
12
[GW]
20
Zeit
Leis
tun
g
5618 5642 5666 5690 5714 5738 [h] 57860
15
30
45
60
75
Netz
las
t
Wind Photovoltaik Netzlast
Interim Results I - Scaled Time Series
Scaled Time Series Hourly Power Plant
Schedule Hourly demand for
control reserves
Mo Tu We Th Fr Sa Su
8 Stefan Kippelt | INREC Essen, 06.03.2012
Time
Load Photovoltaics
[GW]
Lo
ad
Po
wer
3400 3410 3420 3430 3440 3450 3460 3470 3480 3490 35000
10
20
30
40
50
60
Zeit [h]
Kra
ftw
erk
sle
istu
ng
[G
W]
Sonstige Wind PV Nuklear Braunkohle Steinkohle GuD Gas GT PSW
Interim Results II - Power Plant Schedule
5618 5642 5666 5690 5714 5738 [h] 57860
10
20
30
40
50
[GW]
70
Zeit
Le
istu
ng
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Scaled Time Series Hourly Power Plant
Schedule Hourly demand for
control reserves
Po
wer
Time
Other Wind Nuclear Lignite Hard Coal CCP PS
Mo Tu We Th Fr Sa Su
5618 5642 5666 5690 5714 5738 [h] 57861.400
1.600
1.800
2.000
2.200
2.400
2.600
[MW]
3.000
Zeit
Leis
tun
g
MRL+ MRL-
Interim Results III – Hourly Demand for Control Reserves
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Scaled Time Series Hourly Power Plant
Schedule Hourly demand for
control reserves
Mo Tu We Th Fr Sa Su
TC + TC -
Po
wer
Time
0 2.000 4.000 6.000 [h] 10.0001.000
1.500
2.000
2.500
[MW]
3.500
Zeit
geo
rdn
ete
Au
ssch
reib
un
gsm
en
ge
MRL+
0 2.000 4.000 6.000 [h] 10.0001.000
1.500
2.000
2.500
[MW]
3.500
Zeit
geo
rdn
ete
Au
ssch
reib
un
gsm
en
ge
MRL+
Modell 2010
Historisch 2010
Modell 2010
Historisch 2010
-
Model Validation
Average amount of tendered reserves almost identic
Model enables a more flexible tendering of control reserves
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Model 2010
Historical 2010
Model 2010
Historical 2010
Positive Capacity Negative Capacity
Tert
iary
Co
ntr
ol
Reserv
es in
Ord
er
Tert
iary
Co
ntr
ol
Reserv
es in
Ord
er
Time Time
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Outline
Introduction
Analysis of Historical Tertiary Control Reserves Activation
Flexible Dimensioning of Control Reserves
Demand for Control Reserves in Future Scenarios
Summary and Outlook
Stefan Kippelt | INREC Essen, 06.03.2012
Used Scenarios: BMU Leitstudie 2009
2010 Szenario 2020 Szenario 20200
20
40
60
80
[GW]
120
Leis
tun
g
Spitzenlast konventionelle Kraftwerke Wind Photovoltaik
79,9
67,0 64,4
53,1
92,6
102,0
27,2
44,7
63,7
28,4
17,323,2
2010 Szenario 2020 Szenario 20300
20
40
60
80
100
120
Leis
tun
g
konventionelle Kraftwerke Wind Photovoltaik
High increase of generation from wind and photovoltaics
Decreasing Peak Load
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Po
wer
Peak Load Conventional Generation Photovoltaics
2010 Scenario 2020 Scenario 2030
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Influence of Scenario Parameters
Stefan Kippelt | INREC Essen, 06.03.2012
Decreasing demand for Tertiary Control Reserves in 2020
Main Cause: Decreasing peak load
0
500
1000
1500
2000
2500
3000
2010 P.Plants2020
Renewables2020
ImprovedForecast
DecreasedLoad 2020
2020Combined
Ave
rage
De
man
d o
f TC
+
-109 MW +282 MW +2 MW -406 MW
-490 MW
[MW]
MRL+ MRL- SRL+ SRL-0
500
1.000
1.500
2.000
2.500
[MW]
3.500L
eis
tun
g
2010 Historisch 2010 Modell 2020 Modell 2030 Modell
MRL+ MRL- SRL+ SRL-0
500
1.000
1.500
2.000
2.500
[MW]
3.500
Leis
tun
g
2010 Historisch 2010 Modell 2020 Modell 2030 Modell
Result Overview
Until 2020: decreasing demand of Tertiary Control Reserves
From 2020: increasing demand of Tertiary Control Reserves
Increasing need for a more flexible tendering practice
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2010 historic 2010 Model 2020 Model 2030 Model
Tertiary Control + Tertiary Control -
Po
wer
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Outline
Introduction
Analysis of Historical Tertiary Control Reserves Activation
Flexible Dimensioning of Control Reserves
Demand for Control Reserves in Future Scenarios
Summary and Outlook
Stefan Kippelt | INREC Essen, 06.03.2012
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Summary and Outlook
Model development
Development of a convolution-based model for the flexible dimensioning of
control reserves
Combination of historic data and future scenario
Model results
Enabling a more flexible tendering of secondary and tertiary control reserves
Allows better adaption of control reserves to system conditions
Further Investigations
Analysis of extreme load situations
Comparison of needed control reserves with active and available
power plant fleet
Perspectives
Consideration of other system failures (e.g. busbar failures)
Stefan Kippelt | INREC Essen, 06.03.2012
Stefan Kippelt | INREC Essen, 06.03.2012 18
Thank you for your attention!
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References
Model for estimating future power plant schedules
D. Waniek, C. Rehtanz, and E. Handschin, “Analysis of market coupling based on a combined network
and market model,” in PowerTech, 2009 IEEE Bucharest, 2009, pp. 1‐6.
Convolution-based dimensioning of control reserves:
Kays, J. Schwippe, C. Rehtanz: "Dimensioning of reserve capacity by means of a multidimensional
method considering uncertainties", 17th Power System Computation Conference, 2011 PSCC
Stockholm, 2011.
Future Energy Scenarios
Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), „Langfristszenarien
und Strategien für den Ausbau erneuerbarer Energien in Deutschland – Leitszenario 2009“, Berlin,
August 2009
Stefan Kippelt | INREC Essen, 06.03.2012