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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 2038 Inorder tocompare the real electricitybill in2016of thecustomer (thecampusuniversity)with theone that itwouldhavehadactingasaDMC, theparts that aredifferent inbothof thebillshave beenemphasized. The real electricitybill emittedby thesupplier in2016 (a retailer) consistsof three components: theaccess fees (regulatedprice), the taxes, andthe“referencedenergy”(which includes someregulatedpricessuchasthecapacitypaymentsor losscoefficientsandallof theunregulatedprices). FinalCostRetailer =AccessFees+ReferencedEnergy+Taxes (13) Takingintoaccount that theaccess feesandtaxesare thesamefor thetwotypesofsupply(retailer andDMC), thecostof the“referencedenergy”for2016 isanalyzed. ForaDMC, thehourlycostof the (referenced)energy isgivenbythe followingsumofcosts: E(h) = ECBC(h)+DMP(h)·EDM(h)+ IMP(h)·EIM(h)+ SAC(h)·EMCB(h) +MDP(h)·EMD(h)+CPP(h)·EMCB(h) (14) where: • E(h)=Energycost in thehour“h”, in€. • ECBC(h)=Energycost in thehour“h” frombilateral contracts, in€. • DMP(h)=DailyMarketprice in thehour“h”, in€/kWh. • EDM(h)=Energybought in theDailyMarket in thehour“h”, inkWh. • IMP(h)= IntradayMarketprice in thehour“h”, in€/kWh. • EIM(h)=Energybought in the IntradayMarket in thehour“h”, inkWh. • SAC(h)=Systemadjustmentcostpassedonto theDMCinthehour“h”, in€/kWh. • EMCB(h)=EnergymeasuredinCentralBars in thehour“h”, inkWh. • MDP(h)=MeasuredDeviationsprice in thehour“h”, in€/kWh. • EMD(h)=MeasuredDeviationofEnergy in thehour“h”=Differencebetweenconsumedenergy andprogrammedenergy in thehour“h”, inkWh. • CPP(h)=Capacitypaymentprice in thehour“h”, in€/kWh. Inthispaper, it isassumedthat theDMCinthestudy(thecampusuniversity)doesnotparticipate inbilateral contractsnor in the IntradayMarket, thus thehourlycostof theenergyreduces to: E(h)=DMP(h)·EDM(h)+ SAC(h)·EMCB(h)+MDP(h)·EMD(h)+CPP(h)·EMCB(h) (15) It ismandatory for theSpanishRegulator (ComisiónNacionaldelMercadoy laCompetencia, CNMC)topublishonitswebsiteadocumentwiththecriterionusedtocalculate theaveragefinalprice (AFP)ofenergy in themarket. TheAFP(seeFigure4) representsanapproximatevalueof thecost ofelectricenergyperkWh,beingonlyareference thatcanvarytoagreateror lesserextent fromthe actualfinalprice,dependingontheconsumer. Specifically, thecapacitypaymentsandthedeviations betweenenergyconsumedandprogrammed,are those thatcanmarkgreaterdifferencesbetweenthe realcostof the invoicingandthecost resultingfromusingtheaveragefinalprice.Asanadditional objective,wecompare thereal costactingasaDMCwiththeresultingcostusingtheAFP. 4.3. CaseStudy:ACampusUniversityasaDMC Todate, all thedependenciesof theTechnicalUniversityofCartagenahavecontractedsupply with a retailer,which is themodality of supplyingof almost all consumers inhighvoltage of the Spanish electrical system. Onlyaround200 consumershavedared toparticipate in theMarket as DMC,see the list in [43]. In2016, thecontractedtariff for theAlfonsoXIII campuswastheATR6.1, 6-periodhighvoltage tariff,withasupplyvoltageof20kV. As ithasbeenstatedbefore, thefinalpriceof the campusuniversity’s invoice is composedof theaccess fees (whichrefers to theuseof thenetwork), the taxes, andthepriceof theenergyfreely 174
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Title
Short-Term Load Forecasting by Artificial Intelligent Technologies
Authors
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Editor
MDPI
Location
Basel
Date
2019
Language
English
License
CC BY 4.0
ISBN
978-3-03897-583-0
Size
17.0 x 24.4 cm
Pages
448
Keywords
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Category
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies