大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模研究

大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模研究一、本文概述Overviewofthisarticle隨著全球能源結(jié)構(gòu)的轉(zhuǎn)型和清潔能源的大力發(fā)展，風(fēng)電作為一種可再生、無污染的能源形式，在全球能源結(jié)構(gòu)中的比重逐漸增大。然而，風(fēng)電的出力波動(dòng)性給電力系統(tǒng)的穩(wěn)定、安全和經(jīng)濟(jì)運(yùn)行帶來了巨大挑戰(zhàn)。因此，對(duì)大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模研究具有重要的理論和實(shí)際應(yīng)用價(jià)值。Withthetransformationoftheglobalenergystructureandthevigorousdevelopmentofcleanenergy,theproportionofwindpowerasarenewableandpollution-freeformofenergyintheglobalenergystructureisgraduallyincreasing.However,thefluctuationofwindpoweroutputposessignificantchallengestothestability,safety,andeconomicoperationofthepowersystem.Therefore,thestatisticalmodelingresearchonthemulti-scaleoutputvolatilityoflarge-scalewindpowerhasimportanttheoreticalandpracticalapplicationvalue.本文旨在通過深入研究大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)特性，建立符合實(shí)際運(yùn)行規(guī)律的統(tǒng)計(jì)模型。文章首先介紹了風(fēng)電出力波動(dòng)性的基本概念和產(chǎn)生原因，然后從時(shí)間序列分析的角度，詳細(xì)探討了風(fēng)電出力在不同時(shí)間尺度上的波動(dòng)性特征。在此基礎(chǔ)上，文章提出了基于概率統(tǒng)計(jì)理論的風(fēng)電出力波動(dòng)性建模方法，并通過實(shí)際風(fēng)電場(chǎng)數(shù)據(jù)驗(yàn)證了所提建模方法的有效性和準(zhǔn)確性。Thisarticleaimstoestablishastatisticalmodelthatconformstotheactualoperatingrulesbyconductingin-depthresearchonthestatisticalcharacteristicsofmulti-scaleoutputfluctuationsoflarge-scalewindpower.Thearticlefirstintroducesthebasicconceptandcausesofwindpoweroutputvolatility,andthenexploresindetailthevolatilitycharacteristicsofwindpoweroutputatdifferenttimescalesfromtheperspectiveoftimeseriesanalysis.Onthisbasis,thearticleproposesawindpoweroutputfluctuationmodelingmethodbasedonprobabilityandstatisticstheory,andverifiestheeffectivenessandaccuracyoftheproposedmodelingmethodthroughactualwindfarmdata.本文的主要內(nèi)容包括：對(duì)風(fēng)電出力波動(dòng)性的產(chǎn)生機(jī)理進(jìn)行深入分析，明確風(fēng)電出力波動(dòng)性的主要影響因素；通過時(shí)間序列分析，提取風(fēng)電出力在不同時(shí)間尺度上的波動(dòng)特征，為后續(xù)建模提供數(shù)據(jù)支持；然后，基于概率統(tǒng)計(jì)理論，構(gòu)建風(fēng)電出力波動(dòng)性的統(tǒng)計(jì)模型，并對(duì)模型的參數(shù)進(jìn)行估計(jì)和檢驗(yàn)；利用實(shí)際風(fēng)電場(chǎng)數(shù)據(jù)對(duì)所建模型進(jìn)行驗(yàn)證，評(píng)估模型的適用性和預(yù)測(cè)精度。Themaincontentofthisarticleincludes:in-depthanalysisofthemechanismofwindpoweroutputvolatility,clarifyingthemaininfluencingfactorsofwindpoweroutputvolatility;Extractthefluctuationcharacteristicsofwindpoweroutputatdifferenttimescalesthroughtimeseriesanalysis,providingdatasupportforsubsequentmodeling;Then,basedonthetheoryofprobabilityandstatistics,astatisticalmodelofwindpoweroutputvolatilityisconstructed,andtheparametersofthemodelareestimatedandtested;Verifythemodelusingactualwindfarmdata,evaluateitsapplicabilityandpredictionaccuracy.通過本文的研究，不僅可以為風(fēng)電場(chǎng)的規(guī)劃、設(shè)計(jì)和運(yùn)行提供理論依據(jù)和技術(shù)支持，還可以為電力系統(tǒng)的穩(wěn)定、安全和經(jīng)濟(jì)運(yùn)行提供決策參考。本文的研究方法和成果也可為其他可再生能源的出力波動(dòng)性建模提供借鑒和參考。Throughtheresearchinthisarticle,notonlycantheoreticalbasisandtechnicalsupportbeprovidedfortheplanning,design,andoperationofwindfarms,butalsodecision-makingreferencescanbeprovidedforthestability,safety,andeconomicoperationofthepowersystem.Theresearchmethodsandresultsofthisarticlecanalsoprovidereferenceandguidanceformodelingtheoutputvolatilityofotherrenewableenergysources.二、風(fēng)電出力波動(dòng)性的理論基礎(chǔ)Theoreticalbasisforwindpoweroutputvolatility風(fēng)電出力波動(dòng)性是大規(guī)模風(fēng)電并網(wǎng)后電力系統(tǒng)面臨的主要挑戰(zhàn)之一。風(fēng)電出力受多種因素影響，包括風(fēng)速的隨機(jī)性、風(fēng)向的不確定性、地形和氣候條件的復(fù)雜性等。因此，對(duì)風(fēng)電出力波動(dòng)性的深入理解和建模是研究風(fēng)電并網(wǎng)問題的基礎(chǔ)。Thefluctuationofwindpoweroutputisoneofthemainchallengesfacedbythepowersystemafterlarge-scalewindpowerintegrationintothegrid.Windpoweroutputisinfluencedbyvariousfactors,includingtherandomnessofwindspeed,uncertaintyofwinddirection,complexityofterrainandclimateconditions,etc.Therefore,adeepunderstandingandmodelingofthefluctuationofwindpoweroutputisthebasisforstudyingtheproblemofwindpowergridconnection.在理論基礎(chǔ)方面，風(fēng)電出力波動(dòng)性的研究主要依賴于概率論與數(shù)理統(tǒng)計(jì)、時(shí)間序列分析、隨機(jī)過程等數(shù)學(xué)工具。概率論與數(shù)理統(tǒng)計(jì)用于描述風(fēng)電出力的統(tǒng)計(jì)特性，如均值、方差、偏度、峰度等，以及不同時(shí)間尺度下風(fēng)電出力的概率分布。時(shí)間序列分析則用于捕捉風(fēng)電出力隨時(shí)間變化的趨勢(shì)和周期性規(guī)律。隨機(jī)過程理論則提供了對(duì)風(fēng)電出力隨機(jī)性的建模方法，如馬爾可夫過程、自回歸積分滑動(dòng)平均模型（ARIMA）等。Intermsoftheoreticalfoundations,thestudyofwindpoweroutputvolatilitymainlyreliesonmathematicaltoolssuchasprobabilitytheoryandmathematicalstatistics,timeseriesanalysis,andstochasticprocesses.Probabilitytheoryandmathematicalstatisticsareusedtodescribethestatisticalcharacteristicsofwindpoweroutput,suchasmean,variance,skewness,kurtosis,etc.,aswellastheprobabilitydistributionofwindpoweroutputatdifferenttimescales.Timeseriesanalysisisusedtocapturethetrendandperiodicpatternsofwindpoweroutputovertime.Thetheoryofstochasticprocessesprovidesmodelingmethodsfortherandomnessofwindpoweroutput,suchasMarkovprocesses,autoregressiveintegralmovingaveragemodels(ARIMA),andsoon.在風(fēng)電出力波動(dòng)性的建模過程中，需要綜合考慮風(fēng)電場(chǎng)的地理位置、氣象條件、風(fēng)電機(jī)組類型等因素。通過收集風(fēng)電場(chǎng)的歷史運(yùn)行數(shù)據(jù)，可以運(yùn)用上述數(shù)學(xué)工具對(duì)風(fēng)電出力進(jìn)行統(tǒng)計(jì)分析，建立適用于不同風(fēng)電場(chǎng)和不同時(shí)間尺度的出力波動(dòng)性模型。這些模型可以為風(fēng)電場(chǎng)的規(guī)劃、設(shè)計(jì)、運(yùn)行和控制提供理論支持，有助于實(shí)現(xiàn)風(fēng)電的可靠并網(wǎng)和高效利用。Inthemodelingprocessofwindpoweroutputvolatility,itisnecessarytocomprehensivelyconsiderfactorssuchasthegeographicallocationofthewindfarm,meteorologicalconditions,andwindturbinetype.Bycollectinghistoricaloperationaldataofwindfarms,theabovemathematicaltoolscanbeusedtostatisticallyanalyzewindpoweroutputandestablishoutputfluctuationmodelssuitablefordifferentwindfarmsandtimescales.Thesemodelscanprovidetheoreticalsupportfortheplanning,design,operation,andcontrolofwindfarms,helpingtoachievereliablegridconnectionandefficientutilizationofwindpower.風(fēng)電出力波動(dòng)性的理論基礎(chǔ)涉及多個(gè)數(shù)學(xué)領(lǐng)域的知識(shí)和方法。通過綜合運(yùn)用這些知識(shí)和方法，可以建立準(zhǔn)確、有效的風(fēng)電出力波動(dòng)性模型，為風(fēng)電并網(wǎng)問題的研究提供有力支持。Thetheoreticalbasisofwindpoweroutputvolatilityinvolvesknowledgeandmethodsfrommultiplemathematicalfields.Bycomprehensivelyapplyingtheseknowledgeandmethods,anaccurateandeffectivewindpoweroutputfluctuationmodelcanbeestablished,providingstrongsupportfortheresearchofwindpowergridconnectionproblems.三、大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模Statisticalmodelingofmulti-scaleoutputvolatilityinlarge-scalewindpower隨著全球能源結(jié)構(gòu)的轉(zhuǎn)型和風(fēng)電技術(shù)的快速發(fā)展，大規(guī)模風(fēng)電并網(wǎng)已成為現(xiàn)代電力系統(tǒng)的重要組成部分。然而，風(fēng)電出力具有顯著的多尺度波動(dòng)性，這對(duì)電力系統(tǒng)的穩(wěn)定運(yùn)行和調(diào)度管理帶來了挑戰(zhàn)。因此，建立準(zhǔn)確的風(fēng)電出力統(tǒng)計(jì)模型，對(duì)理解和預(yù)測(cè)風(fēng)電出力波動(dòng)性具有重要意義。Withthetransformationofglobalenergystructureandtherapiddevelopmentofwindpowertechnology,large-scalewindpowergridconnectionhasbecomeanimportantcomponentofmodernpowersystems.However,windpoweroutputexhibitssignificantmulti-scalefluctuations,whichposechallengestothestableoperationandschedulingmanagementofthepowersystem.Therefore,establishinganaccuratewindpoweroutputstatisticalmodelisofgreatsignificanceforunderstandingandpredictingthevolatilityofwindpoweroutput.在大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模研究中，我們主要關(guān)注兩個(gè)層面：短期波動(dòng)性和長(zhǎng)期趨勢(shì)性。短期波動(dòng)性通常與天氣條件、風(fēng)速變化等因素密切相關(guān)，而長(zhǎng)期趨勢(shì)性則受到技術(shù)進(jìn)步、政策調(diào)整等宏觀因素的影響。Inthestatisticalmodelingresearchoflarge-scalewindpowermulti-scaleoutputvolatility,wemainlyfocusontwolevels:short-termvolatilityandlong-termtrend.Shorttermvolatilityisusuallycloselyrelatedtofactorssuchasweatherconditionsandwindspeedchanges,whilelong-termtrendisinfluencedbymacrofactorssuchastechnologicalprogressandpolicyadjustments.為了準(zhǔn)確描述這兩個(gè)層面的波動(dòng)性，我們采用了多種統(tǒng)計(jì)建模方法。對(duì)于短期波動(dòng)性，我們采用了時(shí)間序列分析的方法，通過構(gòu)建自回歸移動(dòng)平均（ARIMA）模型或指數(shù)平滑模型，對(duì)風(fēng)電出力進(jìn)行短期預(yù)測(cè)和波動(dòng)性分析。這些模型能夠有效地捕捉風(fēng)電出力的時(shí)間序列特性，并對(duì)短期內(nèi)的出力波動(dòng)性進(jìn)行準(zhǔn)確刻畫。Toaccuratelydescribethevolatilityofthesetwolevels,wehaveemployedvariousstatisticalmodelingmethods.Forshort-termvolatility,weadoptedthemethodoftimeseriesanalysis,byconstructinganautoregressivemovingaverage(ARIMA)modelorexponentialsmoothingmodel,tomakeshort-termpredictionsandvolatilityanalysisofwindpoweroutput.Thesemodelscaneffectivelycapturethetimeseriescharacteristicsofwindpoweroutputandaccuratelycharacterizetheshort-termoutputvolatility.對(duì)于長(zhǎng)期趨勢(shì)性，我們采用了回歸分析的方法，通過構(gòu)建多元線性回歸模型或時(shí)間序列回歸模型，對(duì)風(fēng)電出力的長(zhǎng)期趨勢(shì)進(jìn)行預(yù)測(cè)和分析。這些模型能夠綜合考慮多種影響因素，如風(fēng)速、溫度、氣壓等氣象因素，以及技術(shù)進(jìn)步、政策調(diào)整等宏觀因素，從而對(duì)風(fēng)電出力的長(zhǎng)期趨勢(shì)進(jìn)行準(zhǔn)確預(yù)測(cè)。Forlong-termtrends,weusedregressionanalysismethodstopredictandanalyzethelong-termtrendofwindpoweroutputbyconstructingmultiplelinearregressionmodelsortimeseriesregressionmodels.Thesemodelscancomprehensivelyconsidervariousinfluencingfactors,suchasmeteorologicalfactorssuchaswindspeed,temperature,andpressure,aswellasmacrofactorssuchastechnologicalprogressandpolicyadjustments,inordertoaccuratelypredictthelong-termtrendofwindpoweroutput.為了更全面地描述風(fēng)電出力的多尺度波動(dòng)性，我們還采用了小波分析的方法。小波分析能夠?qū)L(fēng)電出力信號(hào)分解為不同尺度的子信號(hào)，從而更精細(xì)地刻畫風(fēng)電出力的波動(dòng)性特征。通過小波分析，我們可以同時(shí)獲得風(fēng)電出力的時(shí)域和頻域信息，為風(fēng)電出力的預(yù)測(cè)和管理提供更加全面的依據(jù)。Inordertocomprehensivelydescribethemulti-scalefluctuationsofwindpoweroutput,wealsoadoptedthemethodofwaveletanalysis.Waveletanalysiscandecomposewindpoweroutputsignalsintosubsignalsofdifferentscales,therebymorefinelydepictingthefluctuationcharacteristicsofwindpoweroutput.Throughwaveletanalysis,wecanobtainbothtime-domainandfrequency-domaininformationofwindpoweroutput,providingamorecomprehensivebasisforpredictingandmanagingwindpoweroutput.大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模研究是一個(gè)復(fù)雜而重要的課題。通過綜合運(yùn)用時(shí)間序列分析、回歸分析和小波分析等多種統(tǒng)計(jì)建模方法，我們可以更準(zhǔn)確地描述和預(yù)測(cè)風(fēng)電出力的波動(dòng)性特征，為電力系統(tǒng)的穩(wěn)定運(yùn)行和調(diào)度管理提供有力支持。Thestatisticalmodelingresearchonmulti-scaleoutputvolatilityoflarge-scalewindpowerisacomplexandimportanttopic.Bycomprehensivelyapplyingvariousstatisticalmodelingmethodssuchastimeseriesanalysis,regressionanalysis,andwaveletanalysis,wecanmoreaccuratelydescribeandpredictthevolatilitycharacteristicsofwindpoweroutput,providingstrongsupportforthestableoperationandschedulingmanagementofthepowersystem.四、案例研究Casestudy為了驗(yàn)證所提出的多尺度出力波動(dòng)性統(tǒng)計(jì)建模方法的有效性，我們選擇了中國(guó)某大型風(fēng)電基地作為案例研究對(duì)象。該風(fēng)電基地裝機(jī)容量達(dá)到數(shù)百兆瓦，擁有大量的風(fēng)電機(jī)組，并且長(zhǎng)期運(yùn)行積累了豐富的實(shí)測(cè)數(shù)據(jù)。Toverifytheeffectivenessoftheproposedmulti-scaleoutputfluctuationstatisticalmodelingmethod,weselectedalargewindpowerbaseinChinaasthecasestudyobject.Thewindpowerbasehasaninstalledcapacityofhundredsofmegawatts,alargenumberofwindturbines,andhasaccumulatedrichmeasureddatathroughlong-termoperation.我們收集了該風(fēng)電基地近五年的實(shí)測(cè)風(fēng)速和風(fēng)電出力數(shù)據(jù)，數(shù)據(jù)采樣間隔為10分鐘。然后，利用提出的多尺度出力波動(dòng)性統(tǒng)計(jì)建模方法，對(duì)風(fēng)電出力數(shù)據(jù)進(jìn)行處理和分析。Wecollectedmeasuredwindspeedandwindpoweroutputdatafromthewindpowerbaseoverthepastfiveyears,withasamplingintervalof10minutes.Then,usingtheproposedmulti-scaleoutputfluctuationstatisticalmodelingmethod,thewindpoweroutputdataisprocessedandanalyzed.在案例研究中，我們主要關(guān)注風(fēng)電出力的日尺度和季節(jié)尺度波動(dòng)性。通過計(jì)算風(fēng)電出力的自相關(guān)函數(shù)和偏自相關(guān)函數(shù)，我們確定了合適的模型階數(shù)，并建立了ARMA模型和SARIMA模型。同時(shí)，考慮到風(fēng)電出力數(shù)據(jù)的非高斯性和非線性特征，我們還引入了基于核密度估計(jì)的非參數(shù)方法，對(duì)風(fēng)電出力概率分布進(jìn)行建模。Inthecasestudy,wemainlyfocusonthedailyandseasonalfluctuationsofwindpoweroutput.Bycalculatingtheautocorrelationfunctionandpartialautocorrelationfunctionofwindpoweroutput,wedeterminedtheappropriatemodelorderandestablishedARMAandSARIMAmodels.Meanwhile,consideringthenonGaussianandnonlinearcharacteristicsofwindpoweroutputdata,wealsointroducedanonparametricmethodbasedonkerneldensityestimationtomodeltheprobabilitydistributionofwindpoweroutput.通過對(duì)模型的參數(shù)估計(jì)和驗(yàn)證，我們發(fā)現(xiàn)所建立的ARMA模型和SARIMA模型能夠較好地描述風(fēng)電出力的日尺度和季節(jié)尺度波動(dòng)性。模型的預(yù)測(cè)結(jié)果與實(shí)際風(fēng)電出力數(shù)據(jù)吻合度較高，且具有較高的預(yù)測(cè)精度?；诤嗣芏裙烙?jì)的非參數(shù)方法也能夠準(zhǔn)確地捕捉風(fēng)電出力的概率分布特征，為風(fēng)電出力的不確定性分析和風(fēng)險(xiǎn)評(píng)估提供了有力支持。Throughparameterestimationandvalidationofthemodel,wefoundthattheARMAandSARIMAmodelsestablishedcanbetterdescribethedailyandseasonalfluctuationsofwindpoweroutput.Thepredictedresultsofthemodelarehighlyconsistentwiththeactualwindpoweroutputdata,andhavehighpredictionaccuracy.Nonparametricmethodsbasedonkerneldensityestimationcanalsoaccuratelycapturetheprobabilitydistributioncharacteristicsofwindpoweroutput,providingstrongsupportforuncertaintyanalysisandriskassessmentofwindpoweroutput.通過案例研究，我們驗(yàn)證了所提出的多尺度出力波動(dòng)性統(tǒng)計(jì)建模方法在實(shí)際應(yīng)用中的有效性。該方法能夠綜合考慮風(fēng)電出力的多個(gè)時(shí)間尺度波動(dòng)性和非高斯性特征，為風(fēng)電場(chǎng)的規(guī)劃、運(yùn)行和控制提供了重要的理論依據(jù)和技術(shù)支持。未來，我們將進(jìn)一步完善該方法，并推廣到其他地區(qū)和類型的風(fēng)電場(chǎng)中，為風(fēng)電行業(yè)的可持續(xù)發(fā)展做出更大的貢獻(xiàn)。Throughcasestudies,wehavevalidatedtheeffectivenessoftheproposedmulti-scaleoutputfluctuationstatisticalmodelingmethodinpracticalapplications.ThismethodcancomprehensivelyconsiderthemultipletimescalefluctuationsandnonGaussiancharacteristicsofwindpoweroutput,providingimportanttheoreticalbasisandtechnicalsupportfortheplanning,operation,andcontrolofwindfarms.Inthefuture,wewillfurtherimprovethismethodandpromoteittootherregionsandtypesofwindfarms,makinggreatercontributionstothesustainabledevelopmentofthewindpowerindustry.五、結(jié)論與展望ConclusionandOutlook本文深入研究了大規(guī)模風(fēng)電多尺度出力波動(dòng)性的統(tǒng)計(jì)建模問題，通過對(duì)風(fēng)電出力數(shù)據(jù)的細(xì)致分析，揭示了風(fēng)電出力在不同時(shí)間尺度下的波動(dòng)特性。在研究中，我們采用了多種統(tǒng)計(jì)方法和技術(shù)手段，如時(shí)間序列分析、小波變換、功率譜分析等，對(duì)風(fēng)電出力數(shù)據(jù)的時(shí)序特性和頻率特性進(jìn)行了全面探索。同時(shí)，我們還建立了多種統(tǒng)計(jì)模型，包括時(shí)間序列模型、概率分布模型等，對(duì)風(fēng)電出力的波動(dòng)性進(jìn)行了定量描述和預(yù)測(cè)。研究結(jié)果表明，風(fēng)電出力的波動(dòng)性具有顯著的多尺度特性，不同時(shí)間尺度下的波動(dòng)特性差異明顯。我們還發(fā)現(xiàn)風(fēng)電出力的概率分布具有一定的非高斯性，這對(duì)于風(fēng)電出力預(yù)測(cè)和電力系統(tǒng)調(diào)度具有重要意義。Thisarticledelvesintothestatisticalmodelingproblemofmulti-scaleoutputvolatilityinlarge-scalewindpower.Throughdetailedanalysisofwindpoweroutputdata,thefluctuationcharacteristicsofwindpoweroutputatdifferenttimescalesarerevealed.Inourresearch,weadoptedvariousstatisticalmethodsandtechnicalmeans,suchastimeseriesanalysis,wavelettransform,powerspectrumanalysis,etc.,tocomprehensivelyexplorethetemporalandfrequencycharacteristicsofwindpoweroutputdata.Atthesametime,wehavealsoestablishedvariousstatisticalmodels,includingtimeseriesmodels,probabilitydistributionmodels,etc.,toquantitativelydescribeandpredictthevolatilityofwindpoweroutput.Theresearchresultsindicatethatthefluctuationofwindpoweroutputhassignificantmulti-scalecharacteristics,andthefluctuationcharacteristicsvarysignificantlyatdifferenttimescales.WealsofoundthattheprobabilitydistributionofwindpoweroutputhascertainnonGaussiancharacteristics,whichisofgreatsignificanceforwindpoweroutputpredictionandpowersystemscheduling.雖然本文在風(fēng)電出力波動(dòng)性的統(tǒng)計(jì)建模方面取得了一定的研究成果，但仍有許多問題值得進(jìn)一步探討。風(fēng)電出力波動(dòng)性的多尺度特性需要進(jìn)一步深入研究，以便更好地理解風(fēng)電出力在不同時(shí)間尺度下的變化規(guī)律和影響因素。需要進(jìn)一步完善風(fēng)電出力預(yù)測(cè)模型，提高預(yù)測(cè)精度和可靠性，以滿足電力系統(tǒng)調(diào)度的實(shí)際需求。隨著風(fēng)電裝機(jī)容量的不斷增加和風(fēng)電場(chǎng)的不斷擴(kuò)展，風(fēng)電出力波動(dòng)性的空間特性也需要引起關(guān)注。未來的研究可以考慮將多個(gè)風(fēng)電場(chǎng)的風(fēng)電出力數(shù)據(jù)進(jìn)行聯(lián)合建模和分析，以揭示風(fēng)電出力波動(dòng)性的空間相關(guān)性和傳播特性。隨著大數(shù)據(jù)和技術(shù)的發(fā)展，我們可以將這些先進(jìn)技術(shù)應(yīng)用于風(fēng)電出力波動(dòng)性的統(tǒng)計(jì)建模和預(yù)測(cè)中，以提高建模的準(zhǔn)確性和效率。Althoughthisarticlehasachievedcertainresearchresultsinthestatisticalm