电力市场的竞价原理与数学模型分析

华中科技大学硕士学位论文电力市场的竞价原理与数学模型分析姓名：胡涛申请学位级别：硕士专业：概率论与数理统计指导教师：周晓阳20060429I:;;;;;;IIAbstractInrecentyears,powermarketsareundergoingreformstepbysteptoenhancetheircompetitivenessovertheworld.Sincethereformtrendisinfrangible,thederegulationandcompetitionwasbroughtintopowerindustrysystem.Thefirststepofthepowermarketreformistoopensupply-sidemarket,thentotransmission-sideanddistribution-sidemarketstepbystep.Asthecoreofthepowermarkets’techniquesupportsystem,biddingarithmeticvariesinthedifferentstageofpowermarket,butitsmathematicalmodelsinnaturearethesameanditspurposeistoimproveeconomiceffectivenesswhilesatisfyingsystemsecurityconstraintsandelectricityquality.Tomaximizepowerproducers’profitandtominimizeoperator’spurchasecost,theBiddingstrategiesareveryimportantforalltheparticipantsofelectricitymarkets.Inthispaper,wefirstintroducedthemarkets’structure,operationmode,tradingmodeanddescribesournation’spowermarketreforminganditsfuture.Inthenextchapter,wefirstgeneralizepowermarkets’biddingruleandsettlementmethod,thendescribeandanalysisbiddingmathematicalmodelscompletely.Asthereissomanyconstraintsanditishardtosolvetheproblemwholly,abiddingmodelofmultiplestagesoftimebasedonthreeconstraintsisdescribedinthechapter4,andwecandrawsomeconclusionsthroughcomputationbyusingthismodel.Theseconclusionswillhelpustoimprovetheexistentbiddingmodels.Attheendofthispaper,weuselearningautomationtostudypowermarketandabiddingmodelofconsideringtransmissionnetworkconstrainsispresented,thismodelshowstheinteractionofthepowerproducersanditworthtoresearchfurther.Keywords:Powermarket;Biddingstrategy;Biddingmodel;Climbingconstrant;Networkconstrant;Marketpower;Learningautomation_____111.1[1][2][3]10%21.2[4]-[9]31.31.44[10]522.12.2[3]2.3[10]62.42.572.5.12.5.289102.61133.13.1.1[11]3.1.2[12]123.1.3[13][14]3.2[15][16][17][18]133.2.1[19][20][21]143.2.2[22][21][23]15[24][25][26][22][27][28][29][30]16[31]3.2.3[32][33][34][35]173.33.3.1[36]18[37]3.3.219[38]3.3.3203.3.4[39][40]213.4[41][42][43]3.4.11minIomiiCP=∑1()O{}1122max(),(),...,()omooolIiCCPCPCP=iPii1,2,...,iI=IomCoiCi()oiiCPiiP11min()min()IIiioiiiiiCPCPP===∑∑2()OiCi()oiiCPioiCiP3.4.22210IiDiPP=-=∑1.1()CDP,min,max(1,2,...,)iiiPPPiI≤≤=1.2()C,maxiPminiP,max,,,min1,min,,,min1IiDDROUiIDiDRODiPPPPPP==⎧-≥⎪⎪⎨⎪-≥⎪⎩∑∑1.3()C,,,minDROUP,,,minDRODP,max,,max,,,min1,min,,max,,,min1min{,}min{,}IiiiUDRSUiIDiiUDRSDiPPPPPPPP==⎧-Δ≥⎪⎪⎨⎪-Δ≥⎪⎩∑∑1.4()C,,maxiUPΔ,,maxiUPΔi,,,minDRSUP,,,minDRSDP23,1,,,,max,1,,,,maxitititUitititDPPPPPP+--≤Δ⎧⎨-≤Δ⎩2.1()C,,,max,,,max,itUitDPPΔΔit,,max1,,min1TititTititPWPW==⎧≤⎪⎪⎨⎪≥⎪⎩∑∑2.2()C,maxiW,miniWiT,max(1,2,...,)llPPlL≤=3.1()ClPl,maxlPlL,max,minnnnnVVVV≤⎧⎨≥⎩3.2()CnVn,maxnV,minnVn1,2,...,nN=N24)()(1211CC-1()O2()O,3.5254[44][45]4.14.1.1(),1,,,,maxititituqqq+-≤Δ,,1,,,maxitititdqqq--≤Δ,itq,1itq+it1t+,,,max,,,max,ituitdqqΔΔit4.1.2264.1.3[46][47]LMCLMCl-=lMCMCl274.2:11221minmax{(),(),....()}TttttrttqqqQlll=⎧⎫⎨⎬⎭⎩∑1..st1rtitiqQ==∑,min,maxtiiiqqq≤≤21,,maxtttiiiuqqq+-≤Δ1,,maxtttiiidqqq+-≤Δ1,2,.....,ir=1,2,....,tT=3,()ttitiqqlit1,2,.....,ir=1,2,....,tT=12{(,,....,),1,2,...,}ttttrqqqqtT==uurttQt2,min,max,iiqqi3,,max,,max,ttiuidqqΔΔit11(,...,,)minttrtTtttqqQll=⎧⎫⎨⎬⎭⎩∑4..st1rtitiqQ==∑()ttitqll≤,min,maxtiiiqqq≤≤51,,maxtttiiiuqqq+-≤Δ1,,maxtttiiidqqq+-≤Δ1,2,.....,ir=1,2,....,tT=6tlt11max{(),....,()}ttttrqqlll=()ttiql628LINDO[21]ttttQrl-trt(1,2,...,)tT=1min()ttttttQrll=⎧⎫-⎨⎬⎭⎩∑1rtitttiqQrl==-∑()ttiqlLINGO4.312()iiiiiiCqaqbqc=++()..(2)itttiiiidCqmmaqbdql==+1,2,..,6i=1,2,3t=62916abc(mw)(mw)10.03701800020020.04002400020030.07501650020040.09502300020050.10502300020060.1250100002001361.2mmm===451.4mm==21.5m=695.80MW897.48MW,1008.40MW.LINDO21l360.002l365.1963l368.79611q200.0021q200.0031q200.0012q0.00022q43.3032q73.3013q191.6223q200.0033q200.0014q95.9324q145.9334q175.9315q88.2525q138.2535q159.1716q120.0026q170.0036q200.0031l360.002l361.6373l368.79611q200.0021q200.0031q200.0012q0.00022q13.6532q73.3013q200.0023q200.0033q200.0014q0.000024q149.0134q175.9315q129.2525q134.8235q159.1716q166.5526q200.0036q200.00302362()(2),1,2,...,.1,2,...tttiiiiiiqkaqbiItTl=+==1695.80MW897.48MW,1008.40MWtiktiq11.1(1,...,6)iki==2222221364521.2,1.3,1.4kkkkkk======2222221364521.3,1.4,1.5kkkkkk======441l273.052l340.853l368.8011q200.0021q200.0031q200.0012q41.6222q43.3032q73.3013q150.0023q200.0033q200.0014q95.9324q145.9334q175.9315q86.7925q136.7935q159.1716q121.4626q171.4636q200.0024314.43255.1[48]()335.25.2.1,maxllPP≤(1,2,...,)lL=lPl,maxlP5.2.2[49](,)ij2(cos)sinijiijijijijijijPUUUgUUbqq=--(1)ijgijb1ijUU==sinijijqq=cos1ijq=0ijr=(1)()/ijijijPxqq=-(2)34ijx,ijqqi0SPPBq=(3)SPP1n×NN1Nn=+0Bnn×0,(,)1/ijjijiBiix∈≠=∑0(,)1/ijBijx=-(4)q3(4)0155510B-⎡⎤=⎢⎥-⎣⎦20.5SPP-⎡⎤=⎢⎥⎣⎦100.140.02SPBPq--⎡⎤==⎢⎥-⎣⎦:121212()/0.6Pxqq=-=-,131313()/1.4Pxqq=-=-,232323()/0.1Pxqq=-=-,31.5mw1.4mw31-10.1mw32-220.5mw21-111.50.52120.20.10.21352mw5.2.3LearningAutomation(LA)(ReinforcementLearning)[50][51][52],LA,LA,LA,LA,,,,LA,LA,LA,,(ReinforcementScheme)Narendra-Shapiro,Luce12{,,...,}lSsss=t12(){(),(),...,()}lptptptpt=()stS∈12()()...()1lptptpt+++=()Rt,()0Rt=()1Rt=36(1)()(1())(1())()()kkkkptptRtp