Gelsenkirchen 30-10-06 page 1 Wuppertal Institute

First International Renewable Energy Conference (IRES I)

Energy storage technologies –a way to cope with the intermittend supply of

Renewable Energies

Dipl.-Ing. Vanessa Grimm

October 30 and 31, 2006 Science Park Gelsenkirchen

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Agenda

• Introduction and background

• Model for the economic best operation of balancing options

- Technologies

- Supply costs of generation/balancing options

- Results: · Operation plan for peak load sector

· Costs of peak load and balancing power

• Summary and conclusion

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Introduction and background

Electricity generation today (Germany): 11% RES [VDEW]2020: at least 20% [coalition agreement Nov. 2005]2050: 50% [long-term scenario UBA]

⇒ mitigation of the intermittend supply (PV, wind)

Thermal power plants designed for rated power,decreasing efficiency with part load. No frequent start-up

Influencing the demand side ⇒ demand side management

Uhrzeit [h] Uhrzeit [h]

Leis

tung

[kW

]

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Developement of the RES electricity generation in TWhscenario „Wahrscheinliche Entwicklung“ (DLR, WI, ZSW 2005)

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 20200

20

40

60

80

100

120

140

160

Wasser WindOnshore

WindOffshore

Biomasse,biog. AbfŠlle Fotovoltaik Geothermie Europ.

Verbund

oeko/projekt2020/str20var; 7.6.05

18

39

86

151

2000 2005 2010 2015 20200

40

80

120

160

REF nach Energiereport IV (2005)

AUSBAU (Bandbreite)

REF Band width in 2020:139 bis 177 TWh/a53,2 bis 66,7 GWel (without biological waste)

intermittend:60% of RES resp15 % of total generation

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Structure of the model

Remaining load curve

Electricity demand

Intermittend supply of RES_

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Intermittend supply of RES (year 2020)fluktuierende regenerative Einspeisung Deutschland Winter 2020

0

5

10

15

20

25

30

35

40

Leis

tung in G

W

Wind

PV

Mo. Di. Mi. Do. Fr. Sa. So.

fluktuierende regenerative Einspeisung Deutschland Sommer 2020

0

5

10

15

20

25

30

35

Leis

tung in G

W

Wind

PV

Mo. Di. Mi. Do. Fr. Sa. So.

Deviation of wind energy forecast: day ahead: 5,7 % PnennIntraday (4h ahead): 3,6 % Pnenn

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Max. power gradient of wind energy [GW/h] per dayto identify 4 typical load days for the model

/ h

> 5.7 GW/h

< 1.5 GW/h 3 - 5.7 GW/h

1.5 - 3 GW/h

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Demand for balancing options differentiated into different time zones (response time/duration)

< 30 sec → Primärregelung

30 sec - 15 min → Sekundärregelung

15 min - few hours → Minutenreserve

> few hours → Tertiärregelung, Kaltreserve

15 min - few hours → Minutenreserve

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Generation/balancing options considered for the model

sort of energy

mechanical/static

electric-chemical

chemical

electrical

mechanical/dynamic

- SMES

- Supercap

- pumped hydro

- CAES

- hydrogen

- flywheel

- (flow)battery

fossil - Hard coal power plant

- Combined cycle plant

- Gas turbine

demand related - Demand side management 2000 MW

6 á 600 MW

10 á 300 MW

13 á 150 MW

Number of units

10 á 50 MW

4 á 1060 MW

6 á 290 MW

15 á 1.2 MW

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Mathematical optimization model for the economic bestoperation of balancing technologies (EmSAr-Modell)

• Programming with Matlab/Simulink©

• Linear optimization with technical restrictions (start-up time/costs of thermal power plants, rated power, minimum power, part load efficiency, State of charge)

• Time horizon: 2020+2030, electricity demand Germany

• sample rate: 1 hour

Marginal note:

• 1-point-model/no division into different supply areas, no import/export of electricity

• Feed-in tarif for RES (EEG) until 2020

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Remaining load curve (electricity demand – feed-in of RES)

focussing on peak load of the remainig demand→ define demand sector <600h/year

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Parameter for calculating the supply costs

hard coal

combined cycle

gas turbine

lead acid battery

flow-battery

pumped hydro

CAES (adiabat)

power related [€/kW] 800 500 300 -

capacity related [€/kWh] 0 0 0 1000 290 15 60discount rate [%] 10 10 10 10 10 10 10lifetime [a] 15 15 15 5 15 25 20

variable [ct/kWh] 0,1 0,07 0,05 0,01 0 0,0032 0,003

fixed [ct/kW a] 2.287 1.356 1.000 1,55 15 3,8 1,42fuel cost [ct/kWh Hu] 0,57 1,6 1,6 - - - -

fuel input per start [ct/kW] 3,53 5,60 1,76 - - - -

wear cost per start [ct/kW] 0,48 1,00 1,00 - - - -warm start coefficient [%] 50 50 - - - - -

hot start coefficient [%] 30 30 - - - - -

rated power [MW] 600 300 150 1,2 50 1.060 290operating hours [h/a] 2.000 515 1.760 153 1.244 3.999 1.395discharge duration [h] - - - 2 8 8 2lower limit SOC [%] - - - 50 80 - -efficiency [-] 0,40 0,55 0,38 0,8 0,7 0,8 0,70

capital

co

stO

&M

-cost

star

t-up

cost

tech

nic

al

dat

a

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Calculated supply cost of generation/balancing technologies(without detailed investment plan for new power plants/storage systems)

[Source: own calculation]

sort of technology without includingcapital costs capital costs

Hard coal power plant 4,68 9,93

Combined cycle 8,91 21,68

Gas turbine 7,59 9,83

Lead-acid battery >100 >100

Flowbattery 5,79 40,01

Pumped hydro 4,17 4,50

CAES 4,69 5,70

Demand side management 7,00 14,00

supply costs [ct/kWhel]

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Results: operation plan for typical load days

Optimization input parameter: O&M-costs, neglecting capital costs

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Operational hours

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Balancing power∆ (wind prognosis – prognosis+intraday wind energy prognosis deviation)

Positive balancing power per day: 1.7 - 3.2 GWh/day

Negative balancing power per day: 1.9 - 3.6 GWh/day

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Costs of peak load and balancing power ∆ (wind prognosis – prognosis+intraday wind energy prognosis deviation)

Costs of peak load power:

4,8 ct/kWh

Costs of balancing power:

5.7 - 9.2 ct/kWh (no capital costs)

7.7 - 41.9 ct/kWh (incl. capital costs)

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Pilot projects – storage technologies already in use

• 6 MWh Vanadium-Flowbattery - 30 MW wind farm (Japan)

12 MWh VRB-ESS in fall 2007 - 38 MW wind farm (Ireland)

• 1,6 MW Lead-Acid battery (Herne, Bocholt)

• 200 kW flywheel – 2 wind turbines a 600 kW

• planned: 200 MW CAES – 100 MW wind farm (Iowa)

• EU research project: adiabatic CAES

source: KBB Crotogino

source: Beacon Power

source: VRB Power Systemsr

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Summary and conclusion

Exemplary results of the model show that high amounts of RES can be integrated in the electricity system if sufficient storage/balancing options are available.

Electricity demand coincided despite intermittend supply of wind+photovoltaics.

Next steps to prove:

• Detailed investment plan for new power plants/ new stoarge systems considering capital investment costs.

• Identifying benchmarks to place new technologies on the market.

• Verifying + modifying the model for different power plant systems (type and numberof base load plants etc.).

• Considering the electricity grid + regional feed-in of RES.

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Thank you for your attention!

Kontakt:

Dipl.-Ing. Vanessa GrimmWuppertal InstitutDöppersberg 1942103 Wuppertal0202-2492-3060202-2492-109 (Sekretariat)vanessa.grimm@wupperinst.org