Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Page - 84 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 84 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Image of the Page - 84 -

Image of the Page - 84 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text of the Page - 84 -

Energies2019,12, 57 generation. Themodelparameterswere tunedbasedonexperimentaldata toallowtheprediction ofsystemresponsesunderdifferentoperationconditions.Historical irradiationandwinddatawere applied to estimate thepower suppliedby thePVandWT,while three typical loadprofileswere consideredtounderstandtheir impactsonsystemoptimization. Section3defines threeperformance indexes forevaluatinghybridpowersystemsequippedwithdifferentcomponentsandmanagement strategies.Weappliedthree typical loads tooptimizesystemdesignbytuningthecomponentsizes andpowermanagement. Theresults showedthat theoptimizationprocessescaneffectivelyreduce theenergycostsby38.9%,40.0%,and28.6%andgreatly improvesystemreliabilityby4.89%,26.42%, and5.08% for household, laboratory, and office loads, respectively. The guaranteed sustainable operationperiodsunderextremeweatherconditionswerealsoestimated. Theresults revealedthat systemsustainabilitycanbeimprovedbytheuseofasub-optimaldesignorchemicalhydrides.Wealso discuss thecriticalpricesof implementingachemicalhydrogengenerationsystem.Conclusionsare thendrawninSection4. 2. SystemDescriptionandModelling Thegreenbuilding, as showninFigure1 [34], is located inMiao-LiCounty inTaiwan. Itwas constructedbyChinaEngineeringConsultants Inc. (CECI)andwasequippedwithahybridpower systemthatconsistedof10kWPVarrays,6kWWTs,800Ahlead-acidbatteries, a3kWPEMFC,anda 2.5kWelectrolyzerwithahydrogenproductionrateof500L/h.Thebuildingwasautonomousand didnotconnect to themaingrid, i.e., itselectricitywassuppliedcompletelybygreenenergy, suchas solarandwind. Theenergycanbestoredforusewhenthegreenenergy is less thanthe loaddemands. Thesecomponentswereoriginallyselectedtoprovideadailyenergysupplyofabout20kWhbased on theNationalAeronautics andSpaceAdministration (NASA)data [34], as illustrated inTable 1. Solarenergywasabundant in thesummerbutpoor in thewinter, sowindenergywasexpected to compensate forsolarenergy inthewinter.However,ChenandWang[32]appliedtheVantagePro2 Plus Stations [35] tomeasure the realweather data on the building site and found that thewind energywasnotsufficient tocompensate for thereducedsolarenergy in thewinter. Furtheranalysesof theenergycostsalsorevealedthat thewindenergywasnoteconomicallyefficient for thisbuilding, as illustrated inTable2. Therefore, the followingcomponentselectionprinciplesweresuggestedto improvesystemperformance [32]: (1) Energy sources: the use of PV and PEMFC in the green buildingwas suggested, because solarenergywas themosteconomical energysourceandthePEMFCcouldguaranteeenergy sustainability. ThePEMFCcanbe regardedas anenergy source thatprovides steadyenergy andasanenergystoragesystemwhencoupledwithahydrogenelectrolyzer.Consideringthe transportation, storage,andefficiencyofenergyconversion, thePEMFCwithchemicalhydrogen generationbyNaBH4 [36]wassuggestedfor thesystem. (2) Energystorage: thelead-acidbatterywassuggestedbecauseof itsgreaterthan90%efficiency[37]. ThoughthePEMFCwithahydrogenelectrolyzercanalsostoreenergy, theconversionefficiency fromelectricity into hydrogenwas only about 60% [33]. Therefore, the total energy storage efficiencywas about 36%, because the PEMFC converted hydrogen into electricitywith an efficiencyofabout60%[38].Note that theLiFebatteryhasahigherefficiency(more than95%) but ismuchmore expensive than a lead-acid battery. Therefore, the lead-acid batterywas preferredfor thegreenbuilding. That is, theselectionofmultipleenergysourcesandstoragesdependedonthe localconditions andloadrequirements. 84
back to the  book Short-Term Load Forecasting by Artificial Intelligent Technologies"
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
Web-Books
Library
Privacy
Imprint
Austria-Forum
Austria-Forum
Web-Books
Short-Term Load Forecasting by Artificial Intelligent Technologies