AEP Prediction of a Wind Farm in Complex Terrain - WindPRO Vs. WindSim
Woo,Jae-kyoon* Kim,Hyeon-gi* Kim,Byeong-min Paek,In-su** andYoo,Neung-soo**
*Dept.ofMechanicalandMechatronicsEngineering,KangwonNationalUniversity,GraduateSchool,(venimaru@kangwon.ac.kr),
*Dept.ofMechanicalandMechatronicsEngineering,KangwonNationalUniversity,GraduateSchool,(kimhk@kangwon.ac.kr),
*Dept.ofMechanicalandMechatronicsEngineering,KangwonNationalUniversity,GraduateSchool,(rhapsodist@kangwon.ac.kr),
**Dept.ofMechanicalandMechatronicsEngineering,KangwonNationalUniversity,Assistantprofessor,Ph.D(paek@kangwon.ac.kr),
**Dept.ofMechanicalandMechatronicsEngineering,KangwonNationalUniversity,Professor,Ph.D(yoonesoo@kangwon.ac.kr)
복잡지형에 위치한 풍력발전단지의 연간발전량 예측 비교 연구
우재균*,김현기*,김병민*,권일한*,백인수**,유능수**
*강원대학교 대학원 기계메카트로닉스공학과(venimaru@kangwon.ac.kr),
*강원대학교 대학원 기계메카트로닉스공학과(kimhk@kangwon.ac.kr),
*강원대학교 대학원 기계메카트로닉스공학과(rhapsodist@kangwon.ac.kr),
*강원대학교 대학원 기계메카트로닉스공학과(rhapsodist@kangwon.ac.kr),
**강원대학교 기계메카트로닉스공학과 조교수,공학박사(paek@kangwon.ac.kr),
**강원대학교 기계메카트로닉스공학과 정교수,공학박사(yoonesoo@kangwon.ac.kr)
Abstract
TheannualenergyproductionofGangwonwindfarm waspredictedforthreeconsecutiveyearsof2007,2008 and2009usingcommercialprograms,WindPRO andWindSim whichareknowntobeusedthemostforwind resourcepredictionintheworld.Thepredictionsfrom thelinearcode,WindPRO,werecomparedwithboththe actualenergypredictionpresentedintheCDM (CleanDevelopmentMechanism)monitoring reportofthewind farm andalsothepredictionsfrom theCFD code,WindSim.Theresultsfrom WindPRO wereclosetotheactual energyproductionsandtheerrorswerewithin11.8% unliketheexpectation.Thereasonforthelow prediction errorswasfoundtobeduetothefactthatalthoughthewindfarm islocatedinhighlycomplexterrain,theterrain steepnesswassmallerthanacriticalangle(21.8°)infrontofthewindfarm inthemainwinddirection.Therefore noflow separationwasfoundtooccurwithinthewindfarm.Theflow separationofthemainwindwasfound tooccurmostlybehindthewindfarm.
Keywords:WindPower,AutomaticWeatherStation,AEP(AnnualEnergyProduction),Complexterrain
submitdate:2011.12.27, judgmentdate:2012.9.18, publicationdecidedate:2012.11.15 communicationauthor:Paek,Insu(paek@kangwon.ac.kr.)
1.Introduction
Sincetheendofthe19th century,wind energyhasbeenusedforanenvironmentally benignelectricitygeneration[1].Inorderto usethisrenewableandgreenenergyfrom windmoreeffectively,alotofwindfarms are being constructed alloverthe world thesedays[2].
Becauseconstructingawindfarm requires hugeamountofbudget,researchactivities on optimizing a wind farm layout and predicting the annual energy production (AEP)from awindfarm havebeenwidely performed.ForapredictionofAEP,alinear codebasedonBZmodel,WindPRO (WAsP solver)andanonlinearcodebasedonthe ReynoldsaveragedNavier-Stokesequation, WindSim,havebeenmainlyused.
WindPROusesWAsP(WindAtlasAnalysis andApplicationProgram)asasolver.Based on thetheory,WindPRO isknown thatit could yield large prediction errors when used for a wind resource prediction in complexterrainbecauseitdoesn'tconsider nonlineareffectsofwindflow suchasflow separation and vortex generation thatare oftenoccurredincomplexterrain.Therefore, theRISO laboratories,whichhaddeveloped WAsP,presents a method to reduce its predictionerrorincomplexterrainbasedon the,so called,RIX (Ruggedness Index) method[3-5].
Unlike WindPRO,WindSim is a CFD (ComputationalFluid Dynamics)program.
WindSim solves the RANS equation,and findsathreedimensionalsteadyflow field includingallthenonlinearflow effectsthat can be generated by the topography in
complex terrain. Therefore WindSim is expectedtobemoreproperby theory for predicting wind resources in complex terrain[6,7].
However,there exists a controversy in prediction accuracies oftwo programs in complex terrain and a lot of research articles containing opposite results have beenpresentedintheliterature.Someinsist thatWindSim predictionsaremoreaccurate than those from WindPRO [8-10], but othersinsistthatWindPRO predictionsare moreaccurateoraboutthesameasthose from WindSim [11-13].
In Korea, most of the wind farms constructedsofarisbasedontheprediction results from WindPRO [14-16],and the application of WindSim is very rare. Recently,the application of WindSim to complex terrain in Korea has been tried.
Based on the research results recently presented in the literature,the errors of wind speed predictionsin complex terrain using nearby AWS wind data from WindSim werewithin15% [17].Alsofora research article on the prediction ofthe AEP ofawindfarm inacomplex terrain using WindSim,it was found that the prediction errors of the AEP for three consecutive years were within 8% [18]. However,researcharticlesontheprediction accuracies ofWindPRO for the AEP on working wind farms are very rare.Also, thecomparisonofthepredictionerrorsby WindPRO andWindSim on theAEP ofa realwindfarm isverylimited.
Therefore,in thisstudy,aprediction of AEP ofthe same wind farm in complex terraininKorea[18]isperformedusingthe
AWS datawith WindPRO.Theprediction results willbe compared with both the measured AEP and the results from WindSIM,andthepredictionaccuracywill beanalyzed.Astheresult,thefeasibilityof using WindPRO to predictthe AEP ofa wind farm in complex terrain will be investigated.
2.ANALYSISPROGRAM ANDMODELING
2.1Analysisprogram
Theanalysisprogramsusedinthisstudy areWindPRO andWindSim.WindPRO isa program developedbyEMD andiswidely usedforwindresourceanalysis.WindPRO usesWAsP asacalculationengine,which isaprogram developedbyRISO in1987. Theprocedureforwindspeedestimation by WAsP canbesimplydescribedbythe windatlasmethod.Thewindatlasmethod convertstheWeibullprobabilitydistribution ofthemeasuredwinddataintoageneralized windstatisticsbyeliminatingtheeffectsof the obstacles,roughness lengths of the ground,and the topography.WAsP then assumesthatthemeasurementsiteandthe predictionsitearewithinthesameclimate region,and applies the generalized wind statistics to the prediction site.As the result,theeffectsofthetopography,obstacles, androughnesslengthsofthegroundofthe predictionsiteareappliedtothegeneralize windstatistics.Finally,theWeibullprobability distributionofthepredictionsiteiscalculated.
WindSim wasdevelopedbyWindSim AS in1997.ItisaCFDprogram thatnumerically solvestheReynoldsAveragedNavier-Stokes equationwiththestandardk-epsilonturbulence
model to find out a three dimensional nonlinearflow fieldintheatmosphere. As wellasthewindspeedpredictions,WindSim cancalculatetheannualenergyproduction from avirtualwindfarm designedbyusers ifproperwind turbine powercurves are provided. DetaileddescriptionofWindSim canbefoundintheliterature[17].
2.2Modeling
Inordertoinputthetopographicinformation intothetwoprograms,adigitalmapwitha size of30 km x 30 km was used. The contourintervalwas25m.Tominimizeany effectfrom themapboundary,theboundary ofthemapwaskepttobeatleast5km awayfrom theclosestwindturbinelocation.
For the roughness information of the map,asshowninFig.1,aroughnessclass of3.0wasusedformostoftheregionsin the map and forfarmlands,villages and rivers (sea),values of1.0,2.8,0.0 were usedrespectively[19,20].
The detailed information about the WindSim modeling can be found in the preliminaryresearch[18].
Fig.1ModelingoftheGangwonwindfarm
3.INFORMATION ON THE WIND
3.1SiteInformation
Inthisstudy,Gangwonwindfarm,which hasatotalcapacity of98MW located in complexterraininKoreawasanalyzed. It iscurrentlythelargestwindfarm inKorea.
Gangwon wind farm has a totalofforty nine2MW windturbines.Therotordiameter and the hub height are 80m and 60m, respectively[22].
In orderto predictthewind speed and theenergyproductionfrom thewindfarm, the wind data measured from a nearby automatic weather station (AWS) were used. The detailed information of the measurementsiteisgiveninFig.2.
Fig.2Measurementsiteinformation
Themeasurementsitehasaruggedness index(RIX)of15.4% whenevaluatedwith aradiusof3.5km andacriticalslopeof 0.3.TheRIX valueofhigherthan10% is normally considered as complex terrain.
Theoperationalenvelopofthelinearcode, WAsP,which isthecalculation engineof WindPRO isknowntobeclosetoanRIX of0% correspondingtoflatterrain[23].
The AEPs (AnnualEnergy Production) ofGangwonwindfarm werepredictedfor
threeconsecutiveyearsof2007,2008and 2009usingcommercialprograms,WindPRO and WindSim.The preliminary resultof WindSim waspublishedintheliterature[18].
3.2MeasurementData
The10minuteaveragedAWSwinddata from 2007to2009wereusedinWindPRO andWindSim fortheAEPprediction.Inthe simulation,themeasuredtime-serieswind data is converted in the programs to its Weibull probability distribution function, and then theWeibullfunction isused for the predictions. Although the Weibull probabilitydistributionisnotexactlysame asthemeasuredtimeseries,itisknownas an accurate representation ofwind in a widevarietyofwindregimes[24].
Thereasonwhythemeasuredwinddata isconvertedintoWeibullprobabilitydistribution function is thatitenables to use simple statisticalrelations to calculate the mean powerdensity as wellas the mean and standarddeviationofwindspeed.Compared with about52,000 measurementdata sets required to describe the annual wind characteristic,Weibullprobabilitydistribution function requiresonly twoparametersfor that.Weibullfunctiondescribestheannual wind dataasthefrequency ofoccurrence forwindspeedbins.AsshownEq.(1),the two parameters in Weibull probability densityfunction,Candk,areknownasthe scalefactorandtheshapefactor,respectively,
exp
