Short-term forecasting of national PV production

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Short-term forecasting of national PV production Challenges regarding the (partial) solar eclipse over north/western Europe 03-20-2015

Transcript of Short-term forecasting of national PV production

Page 1: Short-term forecasting of national PV production

Short-term

forecasting of

national PV

production Challenges regarding

the (partial) solar eclipse

over north/western Europe

03-20-2015

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Outline

we are enercast

Solar eclipse

EU/German renewables and forecasting

Solar eclipse in the forecast

Conclusion & lessons learned

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enercast GmbH

‣ Online forecast for renewable

energy sources since 2009

‣ Team with a broad range of

backgrounds

‣ Cooperation with:

Fraunhofer-IWES, University of Kassel

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Solar Eclipse 2015-03-20

‣ "SE2015Mar20T" by A. T. Sinclair - NASA (http://eclipse.gsfc.nasa.gov/SEgoogle/SEgoogle2001.html). Licensed under Public

Domain via Wikimedia Commons - https://commons.wikimedia.org/wiki/File:SE2015Mar20T.gif#/media/File:SE2015Mar20T.gif

Example Values for

Berlin, Germany

(51.51°N, 13.38°E)

Obscuration: 72%

(80-65%)

1st Contact 9:37am

Mid eclipse 10:46am

End: 11:57am

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NWP Simulation of the solar eclipse

Köhler, Carmen; Steiner, Andreas; Lee, Daniel; Thieler, Jens; Ritter, Bodo (2014): Case studies of the effects of a

total solar eclipse on weather with varying degrees of cloud cover using a local numerical weather predition model.

Deutscher Wetterdienst/Seewetteramt, Offenbach/Hamburg

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PV production ‣ Installed PV Capacity in Germany: 36 GW

‣ (Europe: 90GW)

‣ Expected production on a clear day:

(e.g. 03-20-2014: was 23GW peak)

Gradient: -400MW/min +700MW/min

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Area forecast (germany) distribution of energy production

NWP weather data

DB of installed facilities

statistical angle distribution

production per TSO

his

toric

al p

rod

uctio

n

final power forecast

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Forecasting scheme/production ‣ Forecast cycle usually 4x/day, most important is dayahead –

but some require up to one week ahead

‣ Reduction applied for the 03-20-2015 based on NWP

simulation

‣ During the final week before the SE:

‣ Special model runs by DWD since day-3

‣ Increased cycles

‣ Timeshift checks/adjustments

‣ Manual inspection of all NWPs used, telcos...

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Actual situation at 2015-03-20

High fog, till noon, in western Germany

Reduced temperature (avg -2.2°C)

No clouds in S/E

„Sonnenfinsternis 2015 Einstrahlung“ von Carsten Pietzsch –

licensed CC0 via Wikimedia Commons -

https://commons.wikimedia.org/wiki/File:Sonnenfinsternis_2015_

Einstrahlung.svg

Situation for whole germany (by TransnetBW)

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What really happened ‣ Managing this event on the world's largest interconnected grid is an

unprecedented challenge for European TSOs. Solar eclipses have

happened before but with the increase of installed photovoltaic energy

generation, the risk of an incident could be serious without appropriate

countermeasures [...] — ENTSO-E Press release from 2015-02-23

Nothing!?

- 8GW of primary control reserve

power (12% used)

- Net frequency variance <0.05Hz

- Marked prices ‚moderate‘

- Some aluminium production was

shut down Mathias Dalheimer: monitored net frequency (Karlsruhe, Germany)

http://gonium.net/blog/2015/03/26/sonnenfinsternis-stromnetz/

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Conclusion

‣ Special, rare event for Enercast

‣ Everyone was well-prepared

‣ “Nothing to report”

‣ Well planned event, next one could be unforeseen?

‣ (fog, cold front and more renewables … )

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Thanks for listening,

more information can be

found at:

‣ http://www.enercast.de

[email protected]

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Forecast vs. actual – by TSO

Yves-Marie Saint-Drenan, Rafael Fritz, Dominik Jost: Auswertung des Effekts der Sonnenfinsternis vom 20.03.2015 auf das

deutsche Energieversorgungssystem, Fraunhofer IWES, Institutsteil Energiesystemtechnik, Kassel, 2015

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19th 03/06 run, actual (11:30)

DWD, Sat24

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Control reserve power @ 03-20-2015

‣ Secondary control power: 4.3GW (+110%)

‣ Tertiary control power: 3GW/-3.7GW (+15%)

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Datenbank: Ausrichtung PV

‣ Eigene Datenbank der

statistischen

Winkelverteilung.

‣ Clusterbildung nach

Nennleistung der Anlage.

‣ Geografische

Besonderheiten werden

brücksichtigt.

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