风电供电可靠性分析

ABSTRACTTheanalysisonreliabilityaspectsofwindpowerfindsmoresignificanceascomparedtothatinconventionalpowergenerationsystems.Inspiteoftheintermittentandvariablenatureofwindenergy,itcanbeusefullytappedtogenerateelectricalpowerformeetingpartoftheenergydemandofthepopulation.ThepresentpaperundertakesananalysisonthereliableaspectsofwindenergyconversionsystemandappliestosevenwindfarmsinMuppandalregioninIndia.Forthepurposeofanalysis,tworeliabilityindicesareused;oneiscoinedastheperiodduringwhichtheexpectedwindenergyisnotsuppliedandtheotheroneisthelossofloadexpectationindexwhichanalyzesthedegreeofmatchingofwindfarmpowergenerationwiththeloadmodel.Thestudyalsoinvestigatestheeffectofincreasingthehubheightofwindenergyconversionsystems.摘要与传统发电系统相比，风电的可靠性分析更有意义。尽管风电具有间断性和不稳定性，但仍可以利用风电来满足部分人口对电能的需求。本文着重分析风电机组的可靠性并应用于印度Muppanda地区的七个风场。分析中应用了两项可靠性指标：一项是风电不供电期望，另一项是分析载荷模型与风场发电的相匹配时的失载期望指数。并对增加风电机组轮毂长度的影响进行了考查。Keywords:ReliabilityWindpowerLossofloadexpectationEnergydeliveryfactorAvailability关键词：可靠性、风电、失载期望、供电特性、可行性。ContentsIntroductionDescriptionofwindfarmstakenforanalysisAnoverviewofthedefinitionsusedforwindenergyconversionsystemperformanceevaluationEnergydeliveryfactorExpectedwindenergynotsuppliedLossofloadexpectationAvailabilityofawindturbineReliabilityanalysisofwindfarmsEvaluationofexpectedwindenergynotsuppliedEvaluationofexpectedwindenergynotsuppliedLoaddemandcurveAnalysisConclusionsReferences目录前言本文所分析的风场的详细介绍风电机组性能评估所用术语的概述能量传递特性风场不供电期望（时间）失载期望风机的可行性指标风场可靠性分析风场不供电期望（时间）评估风场不供电期望（时间）评估载荷需求曲线分析结论参考文献Introduction前言Theassessmentofthereliabilityofwindfarmsingenerationsystemsisessentialfromtheplanningpointofview.Researchesontheimprovementinperformanceofwindpowerplantsaregettingimportaneeasitsapplicationisincreasedrapid[1—].Thewindturbinegeneratorsinawindfarmwillnothaveafixedcapacitydistribution,owingtotheconstantvariationsintheoutputsofeachwindturbineoutputdespiteallofthembeingfedfromthesamewindenergysource.Hence,awindenergyconversionsystemcannotproducepoweratitsratedcapacitythroughouttheyear.Duetothepartialavailabilityofwindgeneratedpower,theactualcapacityvalueofawindpowerplantinautilitysystemisrelativelylow,whichisequivalenttothewindpowerplant'scapacityfactormultipliedbyitsratedcapac[4it]y.Therelationshipbetweenthewindturbinepoweroutputandwindspeedisnon-linearandhencethepoweroutputcharacteristicsofwindenergyconversionsystemaredifferentfromthatofanyconventionalgenerationsystems.Eventhoughwindenergyisnotconsistent,windpowerplantscontributetomeettheenergydemandrequirements.从计划的角度来说，并网发电风场的可靠性评估至关重要。风场性能改进与其应用同样重要，关于此方面的研究越来越多。尽管风轮处于同样的风能条件，但其输出总是变化的，致使风场中发电机的容量不恒定。由此，风电机组全年不可能按其设定容量发电。由于风力发电不完全可行，在役风场的实际容量相当低，相当于风场特性系数与其设定容量的乘积。风轮的输出与风速之间呈非线性关系，从而风电机组的能量输出特性与其他传统发电系统迥异。但是，虽然风电不持续，风场依然对满足电能需求具有一定贡献。Inrecentyears,windenergybasedgenerationhaswitnessedanexponentialgrowthinmostdevelopingcountries.InIndia,severalstateshaveincreasedtheirwindenergypenetrationintothesystemgridstosupplementtheexistinggenerationfromconventionalsources,namely,hydroandthermaloptions.ItwouldappearthatinviewoftherapidgrowthofwindenergygenerationinIndia,thisformofevaluationstudiescanhelpplannersinunderstandingthescope,limitationsandfutureofsuchsystemsintheIndianenvironment.近年来，许多发展中国家大力发展风能产业。印度的诸多州郡已经实行风电入网作为传统水能、热能的补充。随着风电产业的迅猛发展，本文所提出的研究形式可以帮助决策者了解在现行印度环境中此类系统的发展空间、限制条件及前景展望。Asactualfielddataisneededfortheanalysisofreliableaspectsofwindpower,itisnecessarytoobtainthisdatafromwindfarmsinIndia.FielddatacollectedfromasetofsevenwindfarmsinMuppandalregionofTamilnadu,India,areusedinthisstudy.出于风电可靠性分析的需要，我们有必要从印度风场获取这些数据。本文所用的数据源自Muppanda地区的七座风场。DescriptionofwindfarmstakenforanalysisThestateofTamilnaduisinthesouthernregionofIndiaatlatitudebetween8.5Nand13.35Nandlongitudebetween76.15Eand80.20E.ThegeographicallocationofthestateofTamilnaduisfavourableforwindpowerdevelopmentduetothepresenceofmajormountainpassesalongthewesternborderofthestate.ThisstateisaheadofotherIndianstateswithmorethan50%ofthetotalinstalledwindenergycapacityofIndia.Tamilnadustatehasexperiencedanencouragingresponsefromtheprivatesectoreversincetheinceptionofwindpowerinthecountry.Ithasatotalinstalledwindturbinecapacityof4906.74MW,ofwhich4887.34MWbytheprivatesectorand19.4MWintheformofgovernmentdemonstrationprojects[5,6].分析中所选用的风场介绍Tamilnadu州位于印度南部地区，东经76.15-80.20，北纬8.5-13.35之间。T州西临山脉，地理条件非常适于发展风电。该州风电装机总容量占印度全境的50%多，在诸州中位列第一。建造风电伊始，T州便从民用风电机中获益匪浅。该州风电装机总容量4906.74MW民用部分占4887.34MW政府示范项目占19.4MW。TheMuppandalregioninTamilnaduhasthedistinctionofhavingoneofthelargestconcentrationsofwindturbinesatasinglelocation.Inthisregion,windfarmsarespreadalongthenationalhighwayfromMuppandaltoKanyakumari,attheconfluenceoftheBayofBengal,ArabianSeaandtheIndianOcean.TheAralvaimozhimountainpassstrengthensthewindsofSouth-Westmonsoon,whichblowfromMaytoSeptemberandreachesamaximumwindspeedof30m/s.Theannualaveragewindspeedinthisareais—m/s.T州M地由于拥有单机容量最大的风机而闻名。该地位于印度洋、阿拉伯海和Bengal湾交汇处，风机安装在M-K高速治线。A山脉加强了5月到9月的西南季风,便之最高速度达到30m/s。该地区年平均风速6-7m/s。ThefielddatausedforthisstudyarefromsevenwindfarmsinMuppandal,Tamilnadu.India,foraperiodfromApril2002toMarch2005.Thedatacovers135windturbineswithatotalcapacityof37MWfromthesevenwindfarmsandthedetailsaregiveninTable1.ThedataisintendedtobetypicallyrepresentativeofthatofanIndianwindfarmlocatedinaregionofhighwindpotential.Thecapacityoftheselectedwindfarmsrangesfrom1MWto11.5MW.Energygenerationfromeachturbine,generationhours,machinemaintenanceandbreakdownhours,gridmaintenanceandbreakdownhours,windspeedandeconomicparametersarethemainvariablescollectedfromthesewindfarms.本文所研究的风场数据源自印度T州M地风场从2002年4月至2005年3月的记录。这些数据含概了七座风场中总装机容量 37MW的135组风机运行参数，详见表1。这些数据可以作为印度风能潜藏量丰富地区的典型案例。七座风场装机容量从1MW到11.5MW不等。所选取的主要参数包括：单机发电量、发电时间、机械故障及维护时间、风速、经济指标等。Ubk1Cipadlyv^indfainisLLkeiifarstudy.WF1WF2WF3WF5WFEWF7Capacity(MWi10ED752.251.7511.55.0257.B25Anoverviewofthedefinitionsusedforwindenergyconversionsystemperformaneeevaluation风电机组性能评估所用术语的概述EnergydeliveryfactorThekeyperformaneemeasureofanypowerplantistheelectricenergyitdeliversovertheyear.Thequantityofenergydelivereddependsontheratedpowercapacityoftheplantanddependsonhowfullythatcapacityisutilized.Thenormalizedmeasureofthewindpowerplantperformaneeistheenergydeliveryfactor(EDF)whichisalsocalledascapacityfactor[7,8].Theenergydeliveryfactorreflectshoweffectivelythewindturbinecouldharnesstheenergyavailableinthewindspectra.Theannualenergydeliveryfactorisgivenas:wherePRistheratedcapacityofthewindturbine,Pd(t)isthepowerdeliveredtothegridatanygiventimet.衡量电厂性能的重要数据是年供电量。供电量取决于额定容量及其利用度。常规的手段是检测其供电特性（EDF，也叫容量特性，它反映在可发电风速范围内风机对风能的利用效率。年均供电特性表达为:其中PR是风机的额定容量，Pd(t)是任意给定t时刻其对电网的供电量。Inthepresentstudy,theenergydeliveryfactorisanalyzedondailyaswellasmonthlypowergenerationfromthesevenwindfarms.Thewindgeneratedpowervarieseveryinstantandhenceitisnecessarytoexamineitsreliableproductionforautilityindustry,howmuchtobedependentondailyormonthlybasis.本研究按天或按月分析七座风场所发电量的供电特性。由于风力发电的瞬变性，因此检测工业用电使用风电的可靠性以及每天或每月多大程度上使用风电十分必要。Theenergydeliveryfactorincorporatestheenergyconversionefficienciesofvariouscomponentsofthewindturbineandtheavailabilityofwindandwindturbines.Fromastudyoftheavailabledataonthewindfarms,itisseenthatthewindfarmsconsideredforstudyoperateatanaverageannualEDFofaround21%.ItmustbeunderstoodthatthewindfarmEDFvarieswiththeseason,havinghighervaluesduringthewindyperiodandlowervaluesduringlesswindyperiodsoftheyearFig.1showswidevariationintheenergygeneration,withEDF 'srangingfrom0.297%to66.48%inayear.风电机各部件间的机械效率和风场及风轮的可靠性共同决定了供电特性。从对几座风场的可靠数据的研究中发现， 风场的年均EDF勺为风电机各部件间的机械效率和风场及风轮的可靠性共同决定了供电特性。从对几座风场的可靠数据的研究中发现， 风场的年均EDF勺为21%必须指出，风场的ED与季节息息相关，多风季节数值较高而少风季节则低。图 1表明风电EDF一年中的变化范围，从0.297到66.48%。70n (洋rotsnLU£?小>00更BQUUJDa/(ApriltoMarch)图1随风能波动的日供电特性32ExpectedwindenergynotsuppliedThereliabilityofthewindpowerplantisevaluatedbyanindexcoinedforthepurposecalledexpectedwindenergynotsupplied(EWNS).Thisindexgivestheexpectedperiodduringwhichthetotalgenerationcapacity(takenintermsofdailyormonthlyenergydeliveryfactor)notexceedingagivenpowerlevel(takenintermsofannualenergydeliveryfactor).TheEWNSisgivenby:EWNS=^Pi(EDF(<AEDF) (2)whereiisthetimestep,adayoramonth,nisthenumberofdaysormonth,EDFiistheenergydeliveryfactorintimeperiodi,AEDFistheannualenergydeliveryfactorandPi(EDFivAEDFi)istheprobabilitythattheannualenergydeliveryfactorexceedstheenergydeliveryfactorintimeperiodi.风场不供电期望（时间）风电可靠性的另一项评估指标是风场不供电期望 （EWN），该指标表示某时段发电总量（日或月供电特性参考在内）未达到既定水平（日或月供电特性参考在内）其表达式如下：EWNS （2）其中i表示单位时间，日或月；n表示单位时间的数量；EDFi是第i时间段内的供电特性；AED是年均供电特性；Pi（EDFi<AEDFi）表示单位时间i内的供电特性未达到年均水平的概率。LossofloadexpectationThereliabilityofwindenergyconversionsystemscanbeevaluatedbytheindex,lossofloadexpectation(LOLE).LOLEistheexpectedperiodduringwhichtheloaddemandexceedstheavailablegenerationcapacity-3].TheLOLEisdefinedas:LOLE匸片(PGcPDf}i=lwhereiisthetimestep,anhouroraday,nisthenumberofhoursordays,PGiisthepowergenerationavailableintimeperiodi,PDiistheloaddemandintimeperiodiandPi(PGi<PDi)istheprobabilitythattheloaddemandexceedstheavailablepowergenerationintimeperiodi.失载期望失载期望（LOLE可用来衡量风电机组的可靠性，它表示某时段的电网需求超过额定载荷。定义为：8787LOLB (3)其中i表示单位时间，日或小时；n表示单位时间的数量；PGi表示单位时间i在功率允许范围内；PDi表示单位时间i内电网需求载荷；Pi(PGivPDi)表示单位时间i内所需载荷超过额定载荷。34AvailabilityofawindturbineThe‘availability'ofawindturbinereferstothepercentageoftimeitisinapositionorstateforgeneratingpower.Servicing,inspection,componentfailuresandaccidents,suchaslightningstrokesreducetheavailabilityofwindturbine.Theavailabilityofeachwindturbineinawindfarmmaydifferdependingonthenetperiodoftimeforwhichitisfunctionalinregardtopowergeneration[14].Gridbreakdownandmaintenanceperiodareincludedinthecalculationofavailabilityfactorsincegridfailureresultsinstallingofwindturbine.DatapertainingtotheupanddowntimesofthevariousturbinesinthesevenwindfarmsisusedtoestimatetheavailabilitythatisgivenbyEq.(4).Fig.2showstheaverageavailabilityofthesevenwindfarms.Availabilityi%i-Availabilityi%i-UptimeUptime*Downtime风机的可行性指标这里的可行性指标表示某地区风机可以发电的时间比。 维护、检查、部件损坏及事故(诸如雷击)等原因都将降低该指标。由于风场中各风机净发电时间不同其可行性指标也不同。电网故障及维护造成风机停运，此时间也要考虑到可行性指标中。七座风场各风机运行和非运行时间的相关数据按公式( 4)计算其可行性指标。图2显示了七座风场的平均可行性指标。97969594939288990的这里的可行性指标表示某地区风机可以发电的时间比。 维护、检查、部件损坏及事故(诸如雷击)等原因都将降低该指标。由于风场中各风机净发电时间不同其可行性指标也不同。电网故障及维护造成风机停运，此时间也要考虑到可行性指标中。七座风场各风机运行和非运行时间的相关数据按公式( 4)计算其可行性指标。图2显示了七座风场的平均可行性指标。97969594939288990的(％)oQA\VF1WF2WF3WF4WF5WindWF6WF7ReliabilityanalysisofwindfarmsEvaluationofexpectedwindenergynotsuppliedThereliabilityanalysisiscarriedoutfortwocases.Inthefirstcase,thereliabilityevaluationisdonewithdailyenergygenerationdataandinthesecondcasewithmonthlyenergygenerationdata.Thecapacitiesandannualenergydeliveryfactorofthesevenwindfarmsaregivenin Table2.TableZEnergydeliveryfactorofwindRarrns.WindfannsAnnuj]energydeliveryrhetorf%)WF1ID21.3WF28.07515.0WF22.2515.7WFdL7S22.1WT511,525,2WT&5.02525.9WT77.625212Inthesevenwindfarmstakenforstudy,windfarmWF6showsthehighestannualenergydeliveryfactorof25.9%.Eventhoughthewindfarmsaresitedinthenearbylocations,theenergydeliveryfactordiffers ineachwindfarm.Thismaybeduetositespecificfactorssuchasstrengthofwindspectra,machineefficiency.Thestudyisextendedtoinvestigate theeffectofincreaseinhubheightoftheexistingwindturbineinthetwocases.风场可靠性分析风场不供电期望（时间）评估该可靠性分析有两种情况：一种是以每天的发电数据进行可靠性分析， 另一种是以月发电数据进行的。七座风场的容量及年均供电特性如表 2所示。所研究的七座风场中，风场WF以年均25.9%的供电特性居高。虽然七座风场地理位置相距不远，但供电特性各不相同，这可能是诸如风速及机械效率不同造成的。 本研究探讨了在两种情况下提高现存风轮轮毂高度的影响。Fig.2.Averageavailabilityofwindfarms.Forthefirstcase,thenumberofdaystheexpectedwindenergynotsuppliedisevaluatedandisgiveninTable3

Ublr3EWNSaiididhtiiliiyatdifFereiHhubhei^licswithdailysencrLitieiiJncrcmcminhubheightRdidbilttyindex(旳EWMS{djyS/ycOr)(S)0191.54Z552182350.0041745233a16455.OG8153557,9510147559.59Theresultsobtainedshowthattheexpectedwindenergynotsuppliedinayearis191.5daysandawindenergyconversionsystemis47.55%reliableinmeetingtheexpectedwindenergy.第一种情况中，不供电期望的天数评估如表3所示。所得结果显示，不供电期望时间是191.5天，满足该期望的风电机组的可靠性是47.55%。Toenhancethereliabilityofwindpowerplant,itisnecessarytoimprovetheoverallperformanceoftheplant.Thereliabilityindexcanbeimprovediftheamountofenergygenerationincreases.Achangeofmanyparametersinanexistingwindfarmmaynotbecosteffective.Therefore,theanalysisiscarriedoutbyincreasingthehubheightoftheexistingwindturbinewhichincreasesthepowergeneration.Asthehubheightincreasestherotorbladesareexposedtohigherwindspeeds,sincethewindspeedincreaseswithaltitude.Theaveragewindspeedmeasuredatagivenheightisrelatedtotheaveragewindspeedmeasuredatanotherheightinthesameverticalplaneandisgivenas[15]:V{hir/ii(5)V{hir/ii(5)whereV(h)isthewindspeedattheheighth,V(hr)isthewindspeedatthereferenceheight,hrandasthepowerlawexponentandistakenas0.3.Theexponenitanempiricallyderivedcoefficientthatvarieswithparameterssuchassurfaceroughness,windspeed,temperatur^15].Assumingthatthereisnovariationinairdensityandthebladediameter,thepowergenerationatgivenheight,P(h),canbeexpressedaswhereP(hr),isthepowergenerationatthereferenceheight.bladediameter,thepowergenerationatgivenheight,P(h),canbeexpressedaswhereP(hr),isthepowergenerationatthereferenceheight.为了提高风电机组的可靠性，有必要全方面改良机组性能。通过增加发电量可以提高可靠性指标。现存风场的诸多参数改变未必影响其效率，故本文针对以提高轮毂来增加发电量的方法进行研究。由于风速随着高度的升高而增大，提高轮毂可以使风轮叶片捕获更高的风速。同一地点风速与高度存在一定关系，如下式所示： (5)其中V(h)是高度为h处的风速；V(hr)是同地点参考高度hr的风速；a是幕指数，一般取0.3，它是一个经验系数，与地表粗糙度、风速、温度等参数有关；假设空气密度和叶片半径不变，发电量设为P(h)，则P(h)可表述为：(6)其中P(hr)是在参考高度的发电量。Thehubheightofthewindturbinesinthewindfarmisincrementedinstepsof2mforsimulations.TheobtainedreliabilityindexisgiveninTable3.Theincreaseinhubheightshowsthatthereliabilityindex,EWNSdecreasesineachstep,thusrepresentinganimprovementinreliability.Theanalysisshowsthatthereliabilityimprovestoanaverageof12.04%withincreaseinhubheightof10m.模拟中风场的风轮轮毂高度每次提升2m测得的可靠性指数如表3所示。可以看到随着轮毂高度的递增EWN递减，从而可靠性改善了。分析表明轮毂提升10nW可靠性平均提高12.04%。Inthesecondcase,thenumberofmonthstheexpectedwindenergynotsuppliedisevaluated.Theresultsobtainedshowthatatthebasehubheight,theexpectedwindenergynotsuppliedinayearis6.9monthsandawindenergyconversionsystemis42.2%reliableinmeetingtheexpectedwindenergy.TheincrementinthehubheightanalysisisalsocarriedoutwithmonthlygenerationdataTable4givesthereliabilityindexatdifferenthubheightsTables5and6givetheEWNSindexobtainedforallthesevenwindfarmsseparatelyandtheEWNSindexforthethreeyearsforwhichthedataarecollected.EWNSandreliabibtyjid.if[erenthubheigliLswithmmitlilygeneiJiiandata.Incrementtnhubhe増htEWNS鬼血hility(跆0&942226.843344536&149J85356.1W4/959J口klife*aWTirriiJjrUijyofrmliwind:l^nnji hubhr峪祥龙m国翼引y 3*EiiCT^fnitbnhiliWildiimbHen4BlIWW-5)Iff%)I：(10Ml654291,4*4J3M042显祁WFl7.1*1圧41^70414D和工7441C4盛耳<2*3*737_2範狛HE6.7UMWMJJTJD越勢7JO41閒41JM422^殆qWF57.1&941!.90&1斗*605.9>50.10斗q祁⑷节JO5UB5ZOT5J65A5<B05.15L1：WF7MMM5J5.J42wsolatte&I rrtdbtityr>lrichhnmMrct\rf^yr*prrkvlwnlirmn^h'vflimlMrv山l丄乂-rhdUimhubhn^hl嗣fLc5M.HlEVEWN5MubbljtyftciuljibEyCtWFlfiJS4112U47J«WF24114WRIU曲3血曲7WM4Q9til4H>.I57041.STO41燧71HUBWffiiBL2<12WF77J汕車曲10第二种情况，不供电期望按月评估。所得结果显示，轮毂在基本高度时不供电期望月数是6.9个月，与之相应的风电机组可靠性是42.2%。同样对轮毂高度提高按月发电数所进行了分析。表4给出了不同高度时的可靠性指数，表5和表6分别给出了所测得的七座风场各自的EWN指数和所选取的三年数据中的EWN指数。EvaluationofexpectedwindenergynotsuppliedThechronologicalhourlygenerationdataforeachmonthofthewindfarmsiscomparedwithloaddemandofachronologicalhourlyloaddemandmodel.Thetotalinstalledcapacityofthesevenwindfarmsfromwhichthedataarecollectedis37MW.Themaximumhourlyaveragepowergenerationfromthesesevenwindfarmsis15MW,whichis40%oftheinstalledcapacity.Thispowergenerationissomewhatlowincomparisonwiththeratedwindfarmcapacity,mainlyduetothestochasticnatureofwind,availabilityofwindturbinesandgridetc.4.2不供电期望评估几座风场每月的发电数据与相应的载荷需求逐小时进行比较。采集数据的七座风场总装机容量37MW/七座风场最大时平均发电功率是15M\，占总容量40%此发电量与风场额定容量相比较低，主要由于风的随机性，机组和电网的可行性等。4.2.1.LoaddemandcurveForthereliabilityestimation,informationoftheloadcurveisessential.SincethewindfarmsarelocatedinMuppandalinthestateofTamilnaduandfeedthestategrid,itisappropriatetoapplytheanalysistotheloaddemandcurveforthestategrid.Theactualstategridissuppliedwithpowerfromsourcesofgenerationotherthanwindpower.Hence,toisolatethecontribution/impactofthewindfarmsalonejtbecamenecessarytoscaledownthestategridsuchthatthepeakdemandofthegridcorrespondstothatassociatedwiththewindfarmsTheloadcurveasappliedtotheevaluationofthereliabilityofwindfarmsthereforeisderivedbyscalingdowntheactualloaddemandcurvepattern.Thepeakloaddemandistakenas15MW,whichisequaltothemaximumhourlyaveragepowergeneration.Theobtainedloadcurveforthestudyisofthesimilartrendsofthestategridloadcurve,butwithmagnitudereduction.Fig.3showstheloadcurveforthereliabilityevaluation.TheLOLEindexisobtainedbycomparinghourlyaveragegenerationofeachmonthwiththecorrespondinghourlyloaddemand.4.2.1载荷需求曲线

为了可靠性估计，绘制载荷曲线信息很必要。由于风场座落在T州M4并向电网供电，可以应用电网需求曲线进行分析。实际上，不单单是风场向电网供电，因此，为区分出风场单独的供电量，有必要按比例缩小电网的数据以使电网的峰值需求与风场的峰值相一致。这条载荷曲线用于估计风场的可靠性，因此它只是按比例缩小了实际的载荷需求曲线样版。峰值载荷需求设为15MVV与风场最大的时平均发电功率相同。所得到的研究曲线与电网载荷曲线走势相近，但其坐标要小得多。图3给出了用于可靠性估计的载荷曲线，LOLE旨数的标定按时平均发电量与载荷需求相同一！！■4321111麥巨)PUOUJ6PPCO-J1011一！！■4321111麥巨)PUOUJ6PPCO-J1011131S17102123图3应用于可靠性分析的比例缩小载荷曲线422.AnalysisTable7showstheLOLEinhoursperyearandthereliabilityinpercentage.Thepercentagereliability(R)iscalculatedasgivenbelow:Reliability(7)Whenthepeakloaddemandisassumedequaltothemaximumhourlyaveragepowergeneration,whichis40%ofthetotalinstalledwindpowercapacity;thelossofloadexpectationindexisobtainedas261.77h/year.Thismeansthewindfarmreliabilitycontributionis9.11%inmeetingtheloaddemand.Averylowvalueofpercentagereliabilityisobtained,becausethehourlypowergenerationishigherthanthedemandonlyforafewhoursinthemonthsofJune,JulyandAugust.ThisisshowninFig.4.

3左)JuneJuly5QU17 21 24Hours6642D8O3左)JuneJuly5QU17 21 24Hours6642D8O4^1-^113 17 21 24Hour&Theanalysisisdonefurtherbyscalingdowntheloaddemandprofiletoachieve50%reliabilityinmeetingtheloaddemand.Hencethepeakloaddemandisdecreasedby5%ineachstep.TheLOLEindexandthecorrespondingpercentagereliabilityareshowninTable7.Whenthepeakloaddemandis20%ofthetotalwindpowercapacity,thereliabilityof53.29%isobtained.Theprofilesofwindpowergenerationandloaddemandat20%ofthetotalwindpowercapacityMay,July,SeptemberandDecemberareshownFg.5.分析表7按小时给出了每年的LOLE旨数和可靠性（%。可靠性（%（R）如下式计算：R= （7）这里假定峰载需求与时平均发电量相同，等于风电总装机容量的 40%得到的失载期望指数是261.77h/year。这说明满足载荷需求的供电可靠性是9.11%。这个数值之所以很低，是因为只有在六、七、八月某些时段的时发电量高于需求。如图4所示。此分析又将载荷需求曲线按比例缩小到能有50刑靠性满足载荷需求。此时峰载需求下降了5%失载期望指数和相关可靠性列于表7。当峰载需求为装机总量的20%寸，可靠性是53.29%。图5给出了5月、7月、7月、11月的风电发电量和需求量为总容量的20%寸的曲线。ItcanbeseenfromFig.4,howthehourly,monthlyandseasonalvariationofwindspeedaffectsthewindpowergenerationandtherebyitsreliability.ThehighestwindpowersystemperformanceisduringthemonthofJune,JulyandAugust.Itisnotedthatthroughouttheyear,abasepowergenerationof5.5%ofthetotalwindpowercapacityisavailablefromthewindfarms.Anotherfeatureofwindenergytobenotedfromthisstudyisthatduringoffpeakhoursindemand,thewindpowergenerationishighandduringpeakhoursthewindpowergenerationislow.Thewindpowergenerationishigherthanorequaltotheloaddemandforcertainperiodoftheday,butthetimeofthedaydoesnotmatchwiththedemandandsothedemandisnotmeteffectively.Thissignifiesthatthewindenergymustbeconsideredonlyformeetingpartofthebaseloadwhileintegratingwiththeconventionalsystems.Theanalysisalsoshowsthehoursduringwhichthepowergenerationishighandlow,andaccordinglytheenergyplannersmusthandlethereservesinpowersystem.从图4可以看出，风速变化影响发电量进而影响供电可靠性。6-8月机组表现量好。可以看出，全年以总装机容量的5.5%作为基数是可行的。另一个明显的特征是非峰期风电发电量大而峰期发电量小。风电的发电量在一天的某些时段高于或等于需求量，但是与需求曲线不匹配，所以不能有效的满足需求。因此风电并网时只能要求其满足一定的基数。本分析还显示出发电量的高低变化，决策者必须处理好电网中的这种波动。Incaseofstand-aloneorhybridsystems,theanalysissignifiestheroleofstoragesystems.Theenergysurpluscanbestoredinbatteriesorothermeans,inordertobeusedduringtimeofinsufficientwindenergyproductionorsoldtotheutilityorforotherpurposes.Reservedenergyinstoragesystemscanbeinstantaneouslyusedasback-up,iftheenergysupplyissignificantlyreducedbytherandomchangesinwindspeed.Inaddition,thestoragecanalsohelptoreducetheutilityofothersourcesforpeakhour'srequirementsbystoringgeyneinrtimesoflowdemandandusingitinpeakhours.除了独立的或混合型的网络，本分析还指出了存贮电能的作用。剩余的电量可以用电池或其他方法存起来，用于风电不足时或作为其他用途。存下来的电能同时可以作为备用，以防由于风速的随机变化引起风电减产。另外，存下来的电能还可以减少其他类发电量，即在谷载时存起来用于峰载。Theimprovementinreliabilitybyincreasingthehubheightofwindenergyconversionsystemby10misinvestigated.TherelationshipbetweenthehubheightandwindspeedisgiveninEq.(5).

to12December---Generationt Demand to12December---Generationt Demand .：5 9 13 I? 11 2-1Hours4Theresultsare