Merit Order of Energy Storages by 2030 The Impact of ... · EV bis 2030 aus Number Year of electric...

1 1

Merit Order of Energy Storages by 2030

The Impact of Storage Technologies and Market

Regulation on Future Electricity Prices and the

Value of Flexibility

Berlin, June 14, 2013 2nd Annual Electricity Price & Load Forecasting Forum 13-14 June

Tim Buber

2 2

Agenda

Research Association for Energy Markets and Technologies

Motivation and Future Challenges

The Concept of the „Functional Energy Storage“

Storage Technologies and Demand Response

Limitations in Price Fluctuations

3 3

Research Association for Energy Markets and Technologies

Urban Energy

Management Industrial Energy

Management

Reserach Center for Energy Economics

Independent Research in Energy Economics since 60 years

Cooperation with the Technische Universität München

Expertise in all fields of energy economics

Foundation of Research Association for Energy Markets and Technologies in 2001

Smart Energy &

Smart Markets

4 4


5 5


Feed-In Management – Deliberate RES Cut-Offs

Su

m o

f L

os

t E

l. G

en

era

tio

n

fro

m R

ES

[GW

h]

To

tal C

om

pe

ns

ato

ry C

os

ts

[mil

lio

n €

]

Source: Abschätzung der Bedeutung des Einspeisemanagements nach § 11 EEG und § 13 Abs. 2 EnWG BWE 2012

Feed-In Management

Target:

High share of RES on

total el. generation

Contradiction

6 6


RES in Germany Historical Development & Political Target

Annual predictions of the increase of photovoltaics and wind power capacity

published by the Federal Ministry for the Environment, Nature Conservation and Nuclear

Safety:

0 GW

10 GW

20 GW

30 GW

40 GW

50 GW

60 GW

70 GW

2010 2015 2020 2025 2030

Leis

tun

g

Jahr

2007 2008 2009 2010

©FfE BMWi-0002 Flexibilisierung von DEA_00561

0 GW

5 GW

10 GW

15 GW

20 GW

25 GW

30 GW

35 GW

40 GW

45 GW

2010 2015 2020 2025 2030

Leis

tun

g

Jahr

2007 2008 2009 2010

©FfE BMWi-0002 Flexibilisierung von DEA_00567

Ph

oto

vo

lta

ics

Pre

dic

ted

Ca

pa

cit

y

Win

d (

On

sh

ore

)

Pre

dic

ted

Ca

pa

cit

y

Year Year

Increase of capacity strongly exceeds

the predictions

7 7

The Concept of the „Functional Energy Storage“

8 8

Electromobility

Pumped Storage CHP + Heat Storage +

Power2Heat

Flexibilization of

Load

+ -

Power2Gas

Further Technologies

FfE Region Model

Expansion Scenarios

Welfare and Market-Analysis

*

**

Functional Energy Storages

Overview of Storage Technologies

* SW Münster

* * EWE

9 9

Functional Energy Storages

Definition „Functional Energy Storage“

10 10


11 11


Flexible CHP Operation - Power to Heat

12 12


Basic Scheme for a Flexible CHP System

Gas Turbine/ Steam Generator

District Heating Grid

Thermal Storage

Peak Load Boiler

Electricity grid / EEX

ElectricalHeating

Turbine

CO2 CO2

Fuel

electricity

hea

t

Electricity

Hea

t

Heatsteam Steam Turbine

Renewables Power Plants

FlexibleCHP System

Renewables

Condenser

Waste Heat

13 13


Operation Modes for Flexible CHP-Systems

Sink

Source

Electricity

Heat

Not operating

Heating D

em

and

Electricity Prices / EEX

Electricity-

Demand/

-Supply

Heating

Demand

Flexible

CHP-

System

low

high

high

Heating

Plant

CHP

Storage

Electr.

Heating

Heating

Plant

CHP

Storage

Electr.

Heating

Heating

Plant

CHP

Storage

Electr.

Heating

Heating

Plant

CHP

Storage

Electr.

Heating

Electricity-

Demand/

-Supply

Heating

Demand

Flexible

CHP-

System

negative

14 14


Functional Energy Storage exemplified by CHP

0

10

20

30

40

50

60

70

80

1344 1368 1392 1416 1440 1464 1488

Le

istu

ng

/La

st

in G

W

Stunde im Jahr

0

10

20

30

40

50

60

70

80

1344 1368 1392 1416 1440 1464 1488

Leis

tun

g/L

ast

in G

W

Stunde im Jahr

0

10

20

30

40

50

60

70

80

1344 1368 1392 1416 1440 1464 1488

Leis

tun

g/L

ast

in G

W

Stunde im Jahr

0

10

20

30

40

50

60

70

80

1344 1368 1392 1416 1440 1464 1488

Le

istu

ng

/La

st

in G

W

Stunde im Jahr

Negative Residual-Load

Residual-Load

Renewable Energies

CHP

Flexibile CHP

Renewable + CHP

0

10

20

30

40

50

60

70

80

1344 1368 1392 1416 1440 1464 1488

Po

wer/

Lo

ad

in

GW

hour of the year Hour of the Year

P

ow

er/

Lo

ad

in

GW

-15

-10

-5

0

5

10

15

1344 1368 1392 1416 1440 1464 1488

Sto

rag

e P

ow

er

in G

W

Hour of the Year

15 15


Power2Heat - Potential

Average secondary control reserve demand in 2012: ~2.500 MW

e-boiler

capacity

max. thermal load

(district heating)

share

SW Flensburg 30 MW 320 MW 9%

↓ ↓ ↓ ↓

Germany 2.700 MW 30.000 MW 9%

collapse of negative control reserve market?

16 16


Power-to-Gas

17 17

Source:

[1] Specht, Michael; Zuberbühler, Ulrich: Power-to-Gas (P2G®): Layout, operation and results of the 25 and 250 kWel research plants. Stuttgart: Zentrum für

Sonnenenergie- und Wasserstoff-Forschung (ZSW), 2012


Power-to-Gas – The Concept

[1]

+ Large

storage&trans-

mission

capacities

available

+ Long-term

storage

possible

− Low

efficiency

− High

Investment

costs

18 18


Power-to-Gas – Hydrogen Production Costs

hours of negative residual load

time of negative

residual load in h/a

full load hours for 1 GW

electrolysis power in h/a

2020 2 1

2020 159 146

2030 371 344

2030 1230 1174

-10 GW power generation for stabilization purposes

( +10 GW power generation for

stabilization purposes)

19 19


Electromobility

20 20


Electromobility – Key Questions

Key Questions:

Where and when are how many vehicles charged?

What is the capacity and energy for charging?

What is the ratio of parking and charging duration?

21 21


Electromobility – Usability Factors

Usability Factors

Hour

of

Day

Mo Tu We Th Fr Sa Su

@ home

par

kin

g p

rob

abili

ty w

ith

in 1

5 m

inu

tes

@ work

Ho

ur

of

Day

Mo Tu We Th Fr Sa Su

pa

rkin

g p

rob

ab

ility

with

in 1

5 m

inu

tes

Ho

ur

of

Day

22 22


Electromobility – Prognosis and Impact on Residual Load

0

5

10

15

20

2010 2020 2030

An

zah

l an

Ele

ktro

fah

rzeu

gen

in M

io.

Jahre

IfE Bundesregierung

DGS conenergy - slow

conenergy - fast RWTH Aachen

Das conenery - fast Szenario geht von 50 Mio. EV bis 2030 aus

Year

Nu

mb

er o

f el

ectr

ic v

ehic

les

in

mill

ion

Scenario: 7 mio EVs in 2030

Flattening of the residual load

only very little benefit from V2G and

DSM

even for 7 Mio EVs

Simplified business approach (pay for

capacity – not for energy)

Average storage capacity of traction

battery in EVs: 26,5 kWh

23 23


Flexibilization of Load

24 24


Demand Side Management

Average DSM-Potential for Commerce, Trade and Services

2.420 MW positive

14.275 MW negative (mainly night storage heating)

Average DSM – Potential in Industry

1.811 MW positive DSM – Potential

410 MW negative DSM – Potential

positive = reduction of load

negative = increase of load

Source: EWI, 2010

What about

temporal availability?

25 25


26 26


Volatility of the Residual Load

0

10

20

30

40

50

60

1 3 5 7 9 11 13 15 17 19 21 23

Re

sid

ual

Lo

ad [

GW

]

Hour of the Day

Scenario PV: 60 GW Wind: 60 GW


©FfE MOS_00064

27 27



• Minimum in the morning and during noon

• Maximum during early noon and evening

Choose timeframes according to this observation in order to analyze the

dynamics of the residual load

0

10

20

30

40

50

60

1 3 5 7 9 11 13 15 17 19 21 23

Re

sid

ual

Lo

ad [

GW

]

Hour of the Day



©FfE MOS_00064

28 28



Minimum of the Residual Load within Timeframe

Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

29 29




Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

30 30



Today


Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

31 31

Today




Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

32 32



Today


Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

33 33



Today


Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

34 34



Today


Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

35 35



Today


Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

36 36



Today


Diffe

ren

ce

be

twe

en

th

e M

axim

imu

m a

nd

Min

imu

m o

f

the

Re

sid

ua

l L

oa

d w

ith

in u

se

d T

ime

fra

me

s

37 37

Today



Power 2 Heat + 2 GW

Power 2 Gas + 0 – 2 GW

Electromobility + 0.5 – 2 GW

Flexibilization of Load +/- 1 – 2 GW

Pumped Hydro Storage +/- 2 - 3 GW

Increased Im-/Export capacities +/- 2 - 3 GW

…educated guessing on how storage technologies can influence the residual load

12 GW – possible shift

by storage technologies

38 38



Though plenty of the points lie

within the grey shaded area we will

have to expect significant price fluctuations! Today

39 39


Day-Ahead-Analysis

Price follows predominantly the residual load

Several occurences of negative prices

y = 0.0014x - 22.575

-250-200-150-100

-500

50100150200250

0 20 40 60 80

Day

-Ah

ead

Pri

ce in

€/M

Wh

Residual Load in GW

©FfE MOS-KOSI_00044

Conclusion:

No predominant influence of RES in price building mechanism visible

40 40


Events of Negative Prices

DA-Price = Day-Ahead Price, Data-Source: transparency.eex.com

Extrema of the day in GW

Solar Wind Coal Lignite Nuclear

Min 0 10.229 1.147 9.186 8.564

Max 4.843 18.416 2.506 12.258 11.121

-250

-200

-150

-100

-50

0

50

100

0

10

20

30

40

50

60

70

Day

-Ah

ead

-Pri

ce in

€/M

Wh

Pro

du

cito

n in

GW

Production Tue., 25.12.2012

Solar

Wind

Others

Coal

Lignite

Nuclear

DA-Price

©FfE MOS-KOSI_00012

41 41


Schemes of Remuneration

The German Renewable Energy Act offers choice of the scheme of

remuneration for feed-in.

* +/- + =

= Earnings by

Feed-In-Tariff

[EUR]

-

+

=

*

Earnings by Direct Selling

and Market Bonus

[EUR]

Remuneration for

Feed-In Rate

[EUR/MWh]

Feed-In

[MWh]

Revenue at European

Energy Exchange

[EUR]

Market Bonus

[EUR/MWh]

Feed-In

[MWh]

Imbalance Energy

Payments

[EUR]

Management Bonus

[EUR/MWh]

Remuneration for

Feed-In Rate

[EUR/MWh]

Monthly Weighted

Average Price

[EUR/MWh]

42 42


Direct Selling and Negative Prices

25.12.2012: Prices should not have fallen below:

-81 €/MWh

12.00 €/MWh 91.00 €/MWh 21.98 €/MWh 81.02

€/MWh

- + Management

Bonus

Remuneration

for Feed-In Rate

Monthly Weighted

Average

Market

Bonus =

- + =

43 43


Development in the capacity of Direct-Selling

Hydro Gas Biomass Wind Offshore Solar Total in MW

Jan 2012 344 67 933 12.062 48 59 13.513

Jun 2012 392 42 1.433 19.884 238 828 22.817

Nov 2012 445 43 1.836 23.409 308 1.961 28.002

Apr 2013 451 57 2.328 24.484 333 3.012 30.670

0

10

20

30

Jan

uar

y 2

01

2

Feb

rura

y 2

01

2

Mar

ch 2

01

2

Ap

ril 2

01

2

May

20

12

Jun

e 2

01

2

July

20

12

Au

gust

20

12

Sep

tem

be

r 2

01

2

Oct

ob

er 2

01

2

No

vem

ber

20

12

Dec

emb

er 2

01

2

Jan

uar

y 2

01

3

Feb

ruar

y 2

01

3

Mar

ch 2

01

3

Ap

ril 2

01

3

Cap

acit

y in

GW

PV Wind (Offshore)

Wind Biomass

Gas Hydro

©FfE MOS_00109

44 44

Limitations in Price Fluctuations - Conclusion

The expected increase of price fluctuations can be limited by

storage technologies to a certain extend

Downwards?

Limited by marginal costs of emerging storage technologies

Depending on available power and capacity

Limited by the remuneration scheme of direct selling

Upwards?

Hard coal as well as gas prices

Decharging capacity of storage technologies

Flexibilization of Load

Electromobility (depending on charging strategy)

45 45

Limitations in Price Fluctuations - Conclusion

Which Markets will have to deal with increasing price fluctuations?

Day-Ahead:

Sufficient capacity

Rare occurrences of extremely low prices chance for DSM?

Control reserve:

Minute Reserve: hardly any revenues possible

Secondary Control Reserve:

Positive: extremely low revenues, going down to zero

Negative: still attractive for some applications

Intraday:

Low online-capacity demand for high flexibility high price volatility

expected.

high uncertainty (grid restrictions, …)

46 46

Thank you for your attention and the support of

Tim Buber: [email protected] / +49-89-158-121-44

Forschungsgesellschaft für Energiewirtschaft mbH

Am Blütenanger 71

80995 München

www.ffegmbh.de

mailto:[email protected]

Merit Order of Energy Storages by 2030 The Impact of ... · EV bis 2030 aus Number Year of electric...

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