FLEXIBLE DIMENSIONING OF CONTROL RESERVE BY · PDF fileflexible dimensioning of control reserve by means of a stochastic model – a future application stefan kippelt | inrec essen,

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


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




Summary and Outlook



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


2010


2011

1 6 12 [h] 240

20

40

[GWh]

80

Uhrzeit

Ein

gesetz

te M

RL

-

2008


2009


2010


2011


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




Summary and Outlook


7

Flexible Dimensioning of Control Reserves – Model Structure


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


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




control reserves

Po

wer

Time

Other Wind Nuclear Lignite Hard Coal CCP PS


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




control reserves


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


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

12

Outline

Introduction




Summary and Outlook


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


Po

wer

Peak Load Conventional Generation Photovoltaics

2010 Scenario 2020 Scenario 2030

14

Influence of Scenario Parameters


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


2010 historic 2010 Model 2020 Model 2030 Model

Tertiary Control + Tertiary Control -

Po

wer

16

Outline

Introduction




Summary and Outlook


17

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 18

Thank you for your attention!

19

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


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FLEXIBLE DIMENSIONING OF CONTROL RESERVE BY · PDF fileflexible dimensioning of control reserve by means of a stochastic model – a future application stefan kippelt | inrec essen,