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Radar@Sea - Towards improving short-term wind power forecasts

Trombe, Pierre-Julien ; Pinson, Pierre; Thomsen, Sven Creutz; Madsen, Henrik

Publication date:2011

Document VersionPublisher's PDF, also known as Version of record

Link back to DTU Orbit

Citation (APA):Trombe, P-J., Pinson, P., Thomsen, S. C., & Madsen, H. (2011). Radar@Sea - Towards improving short-termwind power forecasts. Poster session presented at European Geosciences Union General Assembly 2011,Vienna, Austria.

Radar@Sea - Towards improving short-term wind power forecastsPierre-Julien Trombe (pjt@imm.dtu.dk), Pierre Pinson, Sven Creutz Thomsen and Henrik Madsen

Department of Informatics, Technical University of Denmark

BackgroundLarge-scale offshore wind farms will provide sig-nificant amounts of energy in the near future.Nowadays, in the north of Europe, the produc-tion of a single of these offshore wind farms canalready meet the yearly consumption of about200000 households. However, the high volatilityof the power generation of these wind farms andits lack of predictability make that:• power producers can not optimally value the

wind ressource,• Transmission System Operators face growing

challenges in balancing production andconsumption.

Accurate wind power forecasts could helpin overcoming these problems.

MotivationIntegrating wind power into electricity grids is an operation that takesplace at time-scales from a few minutes up to half an hour. At such time-scales, and for individual wind farms, offshore wind power fluctuationsof an amplitude larger than 60% of the nominal power of the wind farmhave already been observed [1]. Extreme wind speed variability has beenidentified as a key factor [2], but the origins of such severe events arestill not clear. It is assumed that wind power generation is influenced bymore complex meteorological phenomena.Offshore wind power data insights provided by statistical models [3]highlight frequent and almost instantaneous changes in the behaviour ofwind farms. However, forecasts generated by the same modelshave a relatively limited accuracy which calls for extra inputs.Ideally, these inputs would be generated at high frequencies and wouldcontain spatio-temporal information of the meteorological conditions inthe neighborhood of offshore wind farms.

Question: (How) Can we improve the predictability ofsuch wind power fluctuations?

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Radar@SeaRadar@Sea is an applied research project in whichit is assumed that rain is a marker for weatherconditions generating complex wind powerfluctuations.

Goals:

• to demonstrate the interest of detecting rainfronts at high frequencies to prevent unex-pected wind power fluctuations,

• to evaluate the influence of rain on windpower fluctuations,

• to improve wind power predictability byintegrating new inputs,

• to propose operational solutions for windpower production management.

Partners:

• wind farm operators and energy producers suchas DONG Energy and Vattenfall,

• DHI, a research organisation with strong com-petences in weather radars,

• the Technical University of Denmark (theNational Laboratory for Sustainable Energy -Risø - and the Department of Informatics)with many years of experience in wind powerand meteorology.

Experimental designA Local Area Weather Radar (LAWR) device is installed on thetransformer platform of the Horns Rev 2 wind farm, approximately 30kmoff the west coast of Denmark, in the North Sea. It can detect rainfallsin a radius of 60km. The wind farm of interest is Horns Rev 1 (see imagebelow) and consists of 80 turbines with a total capacity of 160MW.

Project structure

1. The experimental part:

• radar installation and calibration,• onsite maintenance,• continuous data collection,

DHI has experience in these fields and al-ready operated a radar at Horns Rev, in theframe of a bird watch study.

2. Data mining:

• extraction of informative spatio-temporalvariables,

• highlighting the influence of rain on thedifferent regimes of wind power fluctua-tions (creation of a catalogue of events)from a statistical perspective,

• extending the understanding of the windpower climatology at Horns Rev and re-veal the importance of rain along withthermal stability and wind direction/speed.

3. Improving the methodology for veryshort-term wind power predictability:

• developement of regime-switching mod-els able to account for rain events andgenerate forecasts accordingly,

• elaboration of new data assimilation tech-niques for short-term updates of local me-teorological models (potential application:correction of phase errors),

• characterization of spatio-temporal aspectsof wind power forecasts errors.

4. Applications for improving wind farmcontrollability:

• wind power fluctuation reduction throughregime-switching approaches based on rainfront detection and high resolution windpower forecasts from WP3 (limit ramp-down strategies),

• storm-ride-through control, potentially in-volving a cascading approach as the frontgoes through the wind farm in order toavoid cutting out wind turbines all atonce.

Timeline

2010 2011 2012 2013

Official start ofthe Radar@Seaproject, for aduration of 3.5years.

May 2009

Installation of theLAWR device on thetransformer platformof the Horns Rev 2wind farm.

March 2010

Relative X position to the LAWR [in km]

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Heavy rain

First data col-lection campaign(radar in rainmode).

April-November 2010

First maintenance operationdue to a technical problem withthe LAWR device (gear box bro-ken). Images are missing over aperiod of 20 days.

Data collection campaign with the LAWR devicein wave mode. This mode is used for measuringwave heights. A potential application is to deter-mine when sea conditions are suitable for offshoremaintenance operations on wind turbines.

January-March 2011

The second data collection cam-paign starts in April 2011. TheLAWR device is expected to be inrain mode for the remaining part of2011.

Project comple-tion is expectedfor the 4th quar-ter of 2012.

December 2012

Further campaigns for 2012have not been planned yet.They will depend on theneeds for extra data.

2012

November 2010 April-December 2011

Rain or not?Operating a LAWR device in an operational environment aschallenging as offshore conditions raises some issues.

Relative X position to the LAWR [in km]

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2010/05/30 07:32

−36−60 −12 12 36 60

60

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−60 No rain

Heavy rain

Sea clutter refers to(undesirable) echoesfrom the waves.Since the LAWRdevice is sometimesused for measuringwave heights, thefence usually used forblocking potentialwave echoes wasremoved from theinstallation settings.

Sea Clutter

The idea of installing a radar on the transformer platformof Horns Rev 2 came up after the latter was built up. Thisnon informative area is the result of physical obstacles onthe platform preventing the LAWR device from scanningin the south west directions.

Blind area

These echoes are linked to rain cells.Rain can occur in different ways. It can then becharacterized with respect to its intensity, timeduration and spatial scale.

Rain cells

This clutter isdue to the windturbines of theHorns Rev 1 windfarm.

Horns Rev 1

Ground clutterrefers to (unde-sirable) echoesfrom groundobstacles. Inthat case, thewest coast ofDenmark.

Ground clutter Example of rain events (30/05/2010)

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References[1] Akhmatov V (2007). Influence of wind direction on intense power fluctuations in large offshore wind farms on the North Sea.

Wind Engineering, 31: 59-64.

[2] Vincent C, Giebel G, Pinson P and Madsen H (2010). Resolving nonstationary spectral information in wind speed time seriesusing the Hilbert-Huang transform. Journal of Applied Meteorology and Climatology, 49: 253-269.

[3] Pinson P and Madsen H (2011). Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switchingautoregressive models. Journal of Forecasting (In Press).

AcknowledgementThis work is supported by the Danish Public Service Obligation (PSO) fund project ’Radar@Sea’ (under contract PSO 2009-1-0226) which is gratefully acknowledged. We thank DHI for assistance with the weatherradar images and DONG Energy for sharing them. We also thank Vattenfall and DONG Energy for providing wind power data from the Horns Rev 1 wind farm.