Flexibilizing Demand and

Renewable Supply

1

Experiences&

Challenges

Dr Maximilian Kloess

oekostrom Handels GmbH

Laxenburger Straße 2

1100 Wien

2

Overview

• About oekostrom AG

• Flexibilizing renewable supply• Hydro power

• Wind power

• Flexibilizing demand• Large consumers

• Small consumers

• summary and outlook

3

About oekostrom AG

4

about oekostrom AG

Pioneer in renewable power in Austria

• Founded in 1999

• First to provide 100% renewable power to end consumers (2000)

• First to buy excess feed-in from PV producers (2003)

• Pioneer in wind generation in Austria

• 2015: market introduction of Plug-in PV Module

5

about oekostrom AG

oekostrom AG today

• 55 000 customers in Austria

• 1200 small excess feed-in suppliers (mostly PV)

• 30 MW of wind power in Austria and neighbouring countries

• 2 MW large-scale PV

• 1500 simon® Plug-in PV Modules sold to customers in Europe

6

about oekostrom AG

oekostrom AG

(public limited company)

oekostrom Produktions GmbH

oekostrom Handels GmbH

oekostrom GmbH

Production Trading Sales

homemade energyGmbHsimon®

7

Flexibilizing renewable supply

8

Flexibilizing renewable supply

challenges of renwable supply integration:

• Seasonal fluctuations

• Daily fluctuations

• Spot price interaction

(merit-order-effect)

• Forecasting errors �Balancing costs

0%

2%

4%

6%

8%

10%

12%

jan feb mar apr may jun jul aug sep oct nov dec

Base

Wind Feed-In

0%

2%

4%

6%

8%

10%

12%

14%

16%

jan feb mar apr may jun jul aug sep oct nov dec

Base

Hydro Feed-In

9

Flexibilizing renewable supply

challenges of renwable supply integration:

• Seasonal fluctuations

• Daily fluctuations

• Spot price interaction

(merit-order-effect)

• Forecasting errors �Balancing costs0

5

10

15

20

25

30

1

89

17

7

26

5

35

3

44

1

52

9

61

7

70

5

79

3

88

1

96

9

10

57

11

45

12

33

13

21

14

09

14

97

15

85

16

73

17

61

18

49

19

37

20

25

21

13

22

01

22

89

23

77

24

65

25

53

26

41

27

29

28

17

29

05

[MW

]

Forecast

Generation

wind forecast vs. generation:

10

Flexibilizing renewable supply

challenges of renwable supply integration:

• Seasonal fluctuations

• Daily fluctuations

• Spot price interaction

(merit-order-effect)

• Forecasting errors �Balancing costs

11

The effect of wind & PV on the power price

Quelle: Agora Energiewende

-60

-40

-20

0

20

40

60

80

100

120

140

EEX Phelix

[€/M

Wh]

Generation & Load

Spotmarket-Price

12

wind site & market value

• correlation with total wind feed-in

determines market valuehigh correlation � low value

Geringe Korrelation � high value

• Sites in Austria are less correlated than

sites in Germany

• Regional differences within Austria

• Differences due to different turbine types

Source: Fraunhofer IWES

Quelle: IG Windkraft

Wind-centreAustria

Wind-centreGermany

Installierte capacity:

Germany: >40 GW

Austria: 2,4 GW

Profile value wind:

Germany: ca. 90% of Base

Austrian: ca. 95% of Base

13

Flexibilizing renewable supply

challenges of renwable supply integration:

• Seasonal fluctuations

• Daily fluctuations

• Spot price interaction

(merit-order-effect)

• Forecasting errors �Balancing costs

14

Wind Forecasting & Balancing Energy Costs

Balancing Costs

-5.000

-

5.000

10.000

15.000

20.000

25.000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

DA ID

0

5

10

15

20

25

30

1

47

93

13

9

18

5

23

1

27

7

32

3

36

9

41

5

46

1

50

7

55

3

59

9

64

5

69

1

73

7

78

3

82

9

87

5

92

1

96

7

10

13

10

59

11

05

11

51

11

97

12

43

12

89

13

35

13

81

14

27

14

73

15

19

15

65

16

11

16

57

17

03

17

49

17

95

18

41

18

87

19

33

19

79

20

25

20

71

21

17

21

63

22

09

22

55

23

01

23

47

23

93

24

39

24

85

25

31

25

77

26

23

26

69

27

15

27

61

28

07

28

53

Wind IST

Prognose day ahead

Prognose intraday IST

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

1

47

93

13

9

18

5

23

1

27

7

32

3

36

9

41

5

46

1

50

7

55

3

59

9

64

5

69

1

73

7

78

3

82

9

87

5

92

1

96

7

10

13

10

59

11

05

11

51

11

97

12

43

12

89

13

35

13

81

14

27

14

73

15

19

15

65

16

11

16

57

17

03

17

49

17

95

18

41

18

87

19

33

19

79

20

25

20

71

21

17

21

63

22

09

22

55

23

01

23

47

23

93

24

39

24

85

25

31

25

77

26

23

26

69

27

15

27

61

28

07

28

53

AE Preis AE PreisBalancing Energy Price

Forecast vs. Feed-In

Balancing Cost

[€/d

ay

][M

W]

[€/M

Wh

]

Forecast vs. Feed-in

Balancing Price

15

Flexibilizing Wind

• Wind parks have to be linked to a control centre

• Live data is used to correct forecasting errors on the

intraday market

� minimizing the lead time is crucial!

• in order to reduce balancing costs wind feed-in can be

curtailed

• curtailment is based on the short-term forecasting error

and the balancing price forecast

Zeit

Leis

tun

g

Abregelungt1 t2

Prognose

Erzeugung

time

loa

d

curtailment

forecastgeneration

16

Flexibilizing Hydro

• hydro power plants have to be linked to a control centre

• Live data is used for forecasting

• in order to reduce balancing costs in the balancing group

hydro feed-in can be curtailed

• Pooling to offer control reserve• Tertiary control reserve

• Secondary control reserve

17

PV power in Austria

• Small-scale PV feed-in • standard load profile

• no smart meters

� �

• Large-scale PV feed-in:• feed-in tarifs

• Have to be marketed by central semi-public

company

�not available for commercial portfolios

� �

0

0,2

0,4

0,6

0,8

1

1,2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

PV standard load profile

18

Flexibilizing demand

19

Large consumersStatus

• Quarterly meter data available

• Market-based pricing is standard

• futures market

• Spot market

• balancing market

• Load shifting and curtailment is allready applied

• Load is pooled for the control reserve market

Barriers to flexibilization:

• no real time data provided by the distribution grid operator

• Seperate metering devices with data connection required

• Economically feasible only for large consumers with shiftable loads (heating & cooling-processes)

0

200

400

600

800

1000

1200

1400

1

79

15

7

23

5

31

3

39

1

46

9

54

7

62

5

70

3

78

1

85

9

93

7

10

15

10

93

11

71

12

49

13

27

14

05

14

83

15

61

16

39

17

17

17

95

18

73

19

51

20

29

21

07

21

85

22

63

23

41

24

19

24

97

25

75

26

53

27

31

28

09

kW

Forecast Load

forecast vs. load

20

small consumers

Current satus

• Standard load profiles

• only few smart meters rolled out so far

in Austria

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93

Standard Business

peak business

Household

forecast vs. load

Barriers to flexibilization:

• no economic incentive to shift loads due to standard load profiles

• no real time data available

• High effort: - metering devices for live data

- variable tarif structure � complex billing

• Low load shifting potential: - mainly heating

21

Summary & Outlook

Renewable Supply: Challenges:

- seasonal & daily fluctuations

- balancing costs (wind)

Flexibilization:

- economically feasible and largely applied

Demand: Challenges/Barriers:

- standard load profiles (small consumers)

- lack of smart meters (small consumers)

- high effort & small potential (small consumers)

Flexibilization:

- economically feasible and applied for large consumers

- economically infeasible for small consumers

22

Thank you for your attention!

Dr Maximilian Kloessoekostrom Handels GmbH

Laxenburger Straße 2 1100 Wien

T +43 5 0575 302 F +43 05 75

9222

Maximilian.Kloess@oekostrom.at

oekostrom.at