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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
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