enbw trading gmbh energy & meteo systems gmbh dr. bernhard graeberdr. matthias lange

Energie braucht Impulse

Immediate Horizontal Wind Energy Exchange between TSOs in Germany since September 2004

Practical Experiences

EWEC 2006, 28 February 2006, Athens, Greece

EnBW Trading GmbH energy & meteo systems GmbH

Dr. Bernhard Graeber Dr. Matthias Lange

Clemens Krauss Dr. Ulrich Focken

28.02.2006 Dr. Graeber, Krauß, EnBW Trading; Lange, e & m systems2

Content

›Horizontal exchange of wind power in Germany

›Balancing concepts

›Wind power predictions (forecasts)

›Operative experiences

›Conclusions


Renewable energy act (EEG) - delivery of renewable energy to customers

TSO

Distribution Network


EEG Plant (e.g. Wind

Park)

EEG Plant (e.g. Wind

Park)

Sales CompanySales Company

Final CustomerFinal Customer

EEG-Quota

TSO

EEG-energy is passed on from the producers to the final customers. Payment (feed in tariffs) are passed on as well.


Spatial distribution of wind energy in Germany

7164 MW (39%)

256 MW

(1%)

3288 MW

(18%)

7628 MW (42%)

Transmission System Operators:E.ON Vattenfall EnBWRWE

Installed capacity in Germany: 18336 MW

Installed capacity in Germany: 18336 MWas of 31.01.2006


Characteristic wind energy production pattern

0

3000

6000

9000

12000

15000

01

.01

.20

04

08

.01

.20

04

15

.01

.20

04

22

.01

.20

04

29

.01

.20

04

05

.02

.20

04

12

.02

.20

04

19

.02

.20

04

26

.02

.20

04

04

.03

.20

04

11

.03

.20

04

18

.03

.20

04

25

.03

.20

04

MW

Source: ISET

Example: January to March 2004, Germany, hourly values

changing levelssteep gradients


Immediate horizontal exchange between TSOs

E Extrapolation

The example shows the data signals of only one TSO

EON transmission zone

EnBW TZ3

VET TZ2

RWE TZ4

WERZ1 x ARZ2

x ARZ4 x ARZ3

x ARZ1

+

_

Grid control

from TZi

EE

E E

x ARZ2

x ARZ4 x ARZ3

x ARZ1

x ARZ2

x ARZ4 x ARZ3

x ARZ1

x ARZ2

x ARZ4 x ARZ3

x ARZ1

___

factors:

Grid control

Grid control Grid control

› Upscaling of production based on measurements at representative wind farms

› Exchange of wind power production every 15 min


Balancing of wind energy prediction deviations

TSO



EEGplant

EEGplant

TraderTrader

Sales CompanySales Company

EEG-Quota

TSO

Power Market

Power Market

Balancing power stations

Balancing power stations

Final CustomerFinal Customer

TSO is responsible for converting fluctuating wind energy into baseload (EEG-Quota)


Balancing concepts

› There are two main approaches for managing differences between prediction and actual production

wind power fluctuations

conventional fluctuations e.g. load, power plants

1. Separate balancing

› Benefits from short-term predictability and limited gradients

› Contracted reserve / intra-day market

› High transparency

› Benefits from uncorrelated fluctuations

› Flexible pool of power plants / trading

› Lower additional costs for balancing

2. Combined balancing

conventional fluctuations

wind power fluctuations


Wind Energy Prediction Systems - Requirements of TSOs

Requirements:

›Predictions of nationwide wind power production

›Required time-horizons: 0 – 96 h (until next working day, power exchange closed at weekends)

›High time resolution (hourly or 1/4 hourly)

System Providers:

› Increased demand due to new EEG (renewable energy act)

›Strong competition among providers

›Three main system providers with scientific background in Germany


Wind Energy Prediction System: Previento (energy & meteo systems) as an example

Previento

Physical Model:

• Spatial refinement

• Thermal stratification

• Regional upscaling

• Forecast uncertainty


Achievable prediction accuracy

›Prediction for all of

Germany

›Evaluation period Y2005

›Daily operational

predictions

›Root mean square error

(RMSE) normalized to

installed capacity

0%

2%

4%

6%

8%

10%

intraday day-ahead (-2d)

Previento

System 2

System 3

RMSE [% inst. capacity]

›Expected prediction quality in normal wind years:

›4 – 6 % intra-day (3 to 10 h)

›6 – 8 % day-ahead

›8 – 10 % 2 day-ahead


Prediction quality of day-ahead forecasts: monthly reporting

0%

2%

4%

6%

8%

10%

Sep04

Okt04

Nov04

Dez04

J an05

Feb05

Mrz05

Apr05

Mai05

J un05

J ul05

Aug05

Sep05

Okt05

Nov05

Dez05

J an06

Previento

System 2

System 3

RMSE [% inst. capacity]day-ahead forecast

Significant changes in prediction quality from month to month. Ranking of quality changes as well.


Measures for reducing balancing costs

›Use of several prediction systems

›Frequent intra day updates of predictions

›Meteorological training for operators

›Meteorological hotline

› Intra day trading

›Explicit consideration of changing wind

power uncertainty for power plant dispatch


Prediction examples (1)

Day-ahead forecast Latest (intraday) forecast

Black: Actual wind production

Blue: Previento

Green: System 2

Purple: System 3

Shaded area: planning schedule

Predictions for Thursday, February 9, 2006 - in MW; EnBW Share (13%)


Prediction examples (2)

Predictions for Monday, 20. September 2004

0

200

400

600

800

1000

1200

1400

1600

0 2 4 6 8 10 12 14 16 18 20 22

Actual wind

Prediction (-1d)

Prediction (-2d)

Prediction (-3d)

Prediction (-4d)

hours; September 20, 2004

Win

d p

roduct

ion E

nB

W-q

uota

(1

3,6

8%

)

[MW

]

Basis for

planning

(Friday)

Day-ahead

prediction

(Sunday)

Actual wind

production

Predictions can change significantly from day to day


Operational experiences:Adjustment of plant dispatch

0

200

400

600

800

1000

1200

1400

1600

1800

Actual wind productionPrediction day-ahead

-300

-200

-100

0

100

200

300

400

500

Delta

24.12.2004 25.12.2004

[MW] MW

24.12.2004 25.12.2004

At the same time: Load forecast for the 25.12.2004 too high -> strong reduction of nuclear power plants necessary

0

200

400

600

800

1000

1200

1400

1600

1800

Nuclear powerplant 2

Nuclear powerplant 1

24.12.2004 25.12.2004

MW


Conclusions

Experiences since September 2004

›18 GW of wind energy have been integrated successfully

› Immediate horizontal exchange is manageable

›Flexible park of power plants is advantageous for integrated

balancing of fluctuations

›Competition between prediction systems increases

prediction accuracy

›Huge prediction errors still occur in specific weather

conditions


Outlook

›Additional wind energy is manageable

›But specific balancing costs will increase (wind prediction

errors will be higher than errors of other fluctuations)

›Wind power will have to participate partly in the balancing

task

›Wind power predictions have to be improved to reduce huge

predictions errors


Company Profiles

EnBW Trading GmbH:

›Trading division of EnBW AG

›Provides balancing services to TSOs

›EnBW AG is third largest energy company in Germany

energy & meteo systems GmbH:

›Operator of wind power prediction system Previento

›provides dispatcher training, meteorological hotline

›R&D: e.g. combination of meteorological weather data

Talk on Thursday Session DT1:

Dr. Ulrich Focken (energy & meteo systems)

OPTIMAL COMBINATION OF DIFFERENT NUMERICAL WEATHER PREDICTIONS FOR IMPROVED WIND POWER PREDICTIONS

enbw trading gmbh energy & meteo systems gmbh dr. bernhard graeberdr. matthias lange

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