36 0 6 0 Vol.36 No. June00 DOI:0.3969/j.issn.000-06.0..006 3 (. 3007;. / 0003; 3. 330006) : ( ) ; AMPL/IPOP AMPL/CPLEX 5 IEEE30 : ; ; ; 0 [] [-4] (VG) [5] VG ; [6] [7] [8] [] ( ) ( ) [9] ; : [0] ; :0--7; :0-0- (507457745); 5 (0-007-03); ( ) IEEE30 (-007-0) 30
. SOC [3]..3 ( ) ( 3G ) : : 3: ( Fig. Hierarchicalandzonalarchitecturefor ) dispatchingelectricvehicles ( )... 4: / : (SOC) 5: ; 4 5... BrackenJ McGilJ 973 [4] / 3
036() ;X= [X X X N0 ] ;X k=[x k x k x k ] [5] [6] k ;Y k [7] : k J = max F(xyy ym) ;ykmt k m x () 烅烆 s.t.g(x) 0 t ykmt= ykmt= - ykmt=0 J i = max fi(xyy ym) y i () 烅 ;n k k 烆 s.t.gi(xyy ym) 0 :F( ) ;x ) ;G( ) N ;fi( ) i ;yi P Git = P Dit +P Eit +U it U j t(g ij cosθijt + j= i ;gi( ) i 烅 B ij sinθijt) N. Q Git =Q Dit+U it U j t(g ij sinθijt-b ij cosθijt ) 烆 j=.. (6) :t [];P Git Q Git i ( t ;P Dit Q Dit ) i t ;P Eit i t ;U it i t ;N ;G ij B ij ;θijt t ) P min Gi P Git P max Gi : t [] (7) min F = X - Y k= t= ( N 0 P dt + kt -P k=x ) d + N 0 α fk(x k Y k ) (3) k= P d = 熿 t= ( N 0 P dt + k=x k) yk yk yk ykm ykm ykm t (4) 燄 (5) { Q min Gi :P max Gi P min Gi Q Git Q max Gi i ; Q max Gi Q min Gi i 3) n ykn k ykn k ykn k n k 燀 k 燅 - : ;P dt P kmdcha x kt P kmcha t [] m= m= t / (0) ;P d ;x kt :P kmch P kmdch k k t x kt>0 x kt<0 m ; k A=I kmtk avail I kmt k m ;N 0 ;α I kmt= I kmt=0 Ui min :U i max U min U it Ui max t [] (8) i i 4) P lt P l max t [] (9) :P lt l t ;P l max l 5) 3
( CPLEX k avail MINOSIPOPSNOPKNIRO ) AMPL /.. AMPL IPOP3.8.0 [9] AMPL / CPLEX. k : min fk(x k Y k )= Y k P kmch n k t= ( kmt -x m=p k) t ykmt = () P kmt = 烅 0 ykmt =0 () 烆 -P kmdch ykmt =- :P kmt k m ) S kmt+ = 烅 S kmt + ηchpkmchδt S kmt β km S kmt - PkmdchΔt 烆 β km η dch ykmt = ykmt =0 ykmt =- (3) :S kmt k m t SOC; η ch η dch ; β km k m ;Δt (3) ) S min S kmt S max (4) :S max S min SOC 3) ykmt =0 t<t kms t kme (5) :t kms t kme k m 4) / S kmtkme S km (6) :S kmtkme S km k m Fig. Flowchartforsolvingbi-leveloptimal SOC dispatchingmodelwithelectricvehicles SOC 4 3 4. AMPL [8] 33
036() f fe(x)= f : exp - ( x+4-μ ) e f = N 0 槡 ( σ ) e πσe P - ( dt + ykt -P ) d t= k= 0<x μ (7) 烅 N 0 n k f = k= t= ( kmt -x m=p k) e - 烅 exp - ( x-μ ) t 槡 ( e σ ) e πσe 烆 :ykt k 烆 μ e -<x 4 t (9) (7) f : μ e=7.47;σe=3.4 ;f fm(x)= 槡 πσmx exp - ( lnx-μ m ) (0) ( σ m ) : 4. 5 IEEE30 μ m=.98;σm=.4 A A5 5 IEEE30 [0] A A A A A3 SOC :00 :00 SOC 90% h 5 695 8 ( [] A A) A α () A ( SOC ) A A4 α=exp n-0 () ( 3 ) 009 :n 0 [] [3] d A A6 A A7 (8) (0) 4.3 fs(x)= ( ) ( ) exp - ( x-μ ) s 0<x μ 槡 πσs σ s+ s 烅 exp - ( x-4-μ ) s 槡 πσs σ s 烆 μ s+<x 4 (8) : μ s=8.9;σs=3.4 able Comparisonsofsystemloadlevelindexes /MW / MW 84.4 79.9 04.5 9.0 80.8 0. 69.3 8.9 50.4 : 34
3 / : ; ; 5 AMPL/ 3 Fig.3 Comparisonamonguncoordinatedcharging IPOP AMPL/CPLEX paternoptimalchargingpaternanddischargingpatern 5 : IEEE30 ; VG 4.4 (htp://aeps.sgepri.sgcc. com.cn/aeps/ch/index.aspx) 3 ( α=α=000 α ) PowerandEnergySocietyGeneralMeetingonConversionand 3 DeliveryofElectricalEnergyinthestCenturyJuly0-4 able Comparisonsofeachevaluationindexunder threepenaltycoeficients 008PitsburghPAUSA:6p. f α= 65.43 8.09 α=000 80.60 0.4 α=exp ( n-0 3 ) 65.76 0.7 : (α=) / 87-83. [4] ; (α=000) f []SCHNEIDER KGERKENSMEYER CKINNER-MEYER Metal.Impactassessmentofplug-inhybridvehiclesonPacific Northwestdistribution systems[c]// Proceedings ofieee []FERN NDEZ L PROM N G SCOSSEN Retal Assessment of the impact of plug-in electric vehicles on distributionnetworks[j].ieee rans on Power Systems 06():06-3. [3]PURUS G ASUWANAPINGKARL PJOHNSON D etal.impactofelectricvehiclesonpowerdistributionnetworks [C]// Proceedings of IEEE Vehicle Power and Propulsion Conference September 7-0 009 Dearborn MI USA:. [J]. 035(4):8-3. 35
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AChargingandDischargingDispatchingStrategyforElectricVehiclesBasedonBi-levelOptimization YAO Weifeng ZHAO Junhua WEN Fushuan XUE Yusheng XIN Jianbo 3 (.ColegeofElectricalEngineeringZhejiangUniversityHangzhou3007China;.StateGridElectricPowerResearchInstituteNanjing0003China; 3.JiangxiElectricPowerResearchInstituteNanchang330006China) Abstract:heextensiveintegrationofnumerousplug-inelectricvehicles(PEVs)intoapowersystemcouldproducesignificant negativeimpactsonthesecureandeconomicoperationofthepowersystemconcernedifthechargingproceduresofpevsare uncoordinated.giventhisthehierarchicalandzonaldispatchingarchitectureisadoptedandanewbi-leveloptimizationmodel ispresentedforcoordinatingthecharging/dischargingschedulesofthepevs.heupper-levelmodelisdevotedtominimizing thesystemloadvariancesoastoimplementpeakloadshiftingbydispatchingeachelectricvehicleaggregator (EVA)andthe loweroneisaimed attracingthe dispatching scheme determined bythe upperdecision-makerthroughfiguring outan appropriatecharginganddischargingschedulesthroughoutaspecifiedday.wohighlyeficientcommercialsolversampl/ IPOPandAMPL/CPLEXrespectivelyareemployedtosolvethedevelopedoptimizationproblem.FinalyamodifiedIEEE 30-bussystem with5evasisemployedtodemonstratethebasiccharacteristicsofdevelopedmodelandmethod. hisworkissupportedbynationalnaturalsciencefoundationofchina (No.5074No.57745 )aprojectfrom JiangxiPowerCompany(No.0-007-03)andaprojectfrom ycoelectronics(shanghai)co.ltd.(no.-007-0). Keywords:electricvehicle;vehicletogrid (VG);charginganddischargingoptimization;hierarchicalandzonaldispatching; bi-levelop timization 櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧櫧 ( continuedfrompage) (963) : E-mail:liuruiye@hit.edu.cn (986) : E-mail:huanglei@ms.giec.ac.cn WindPowerForecastingBasedonDynamicNeuralNetworks LIU Ruiye HUANG Lei (.SchoolofElectricalEngineeringandAutomationHarbinInstituteofechnologyHarbin5000China;.GuangzhouInstituteofEnergyConversionChineseAcademyofSciencesGuangzhou50630China) Abstract:heprecisionofwindpowerforecastisveryimportantintheselectionofwindfarmsiteandintheintegrationand operationofpowersystem withincreasingpenetrationofwindpower.compared withstaticneuralnetworkstwodynamic neuralnetwork modelslocalyrecurrenttime-delayneuralnetwork modelandglobalyrecurrenttime-delayneuralnetwork modelareproposedfortheforecastingofa windfarm outputinordertosimulatethetime-seriescharacteristicofthe generationseries.odemonstratetheefectivenessthemodelsareappliedandtestedonawindfarmlocatedinthenorthof China.Baseonnumericalmeteorologicalpredictionshourlyforecastsupto4hoursaheadareproducedforthewindfarm. Simulationresultsdemonstratethatthedynamicneuralnetworkmodelsoutperformthestaticonesintheforecastofwindpower withtime-seriescharacteristic. hisworkissupportedbythenationalhighechnologyresearchanddevelopmentprogram ofchina (863Program) (No.0AA05A05). Keywords:windpower;windpowerforecast;neuralnetwork;recurrenttime-delayneuralnetwork;timeseries 37