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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 Thecomparisonof the fourdifferenthybridpowerconfigurationsshowsthatcurrently theSW systemcanachieve thecheapest systemcost. Forexample, thedailycost for theoffice loadisNT$865 using theSWsystem,butNT$963, 1,148, and1,241using theSWPC,SWPH,andSWPCHsystems, respectively.However, thereliability (LPSP=0)of the threesystems isgreater thantheSWsystem (seeFigure9) (i.e., the reliabilityof thesystems improvedbecause thePEMFCcanprovidereliable energywhennecessary).Undercurrentconditions, thecostrankingisSW>SWPC>SWPH>SWPCH forall loads for the followingreasons: (1)Thecostofhydrogenishighatpresent; (2)energystorage efficiencybyhydrogenelectrolyzation ismuchlower thanbyLi-Febatteries; (3) theextrahardware, suchas thePEMFCandhydrogenelectrolyzer, significantly increasesystemscosts. ThecostandenergydistributionofapplyingtheoptimalSWPCHsystemtothe laboratory load areshowninTable4. First,duetosystemoptimization, thePEMFCandSodiumborohydride tends not tobeused, because the fuel cost ishigh (NT$28perbatch toproduce150LofH2, seeTable2). Therefore, the corresponding equipment (hydrogen electrolyzer, PEMFC, and chemical hydrogen production)canbesavedtoreduce thesystemcostby13.39%. Second, thebatterycostaccounts for nearly73%of the total systemcosts,whereas thePVpanels tostore thesolarpowerconstituteonly 11.21%of thesystemcost. Thus, systemoptimizationtends tousesolarenergy,althoughthesystem isequippedwiththreeenergysources. Third, thesystemstores4.62%energyashydrogen; thiswas notusedtoproduceelectricityduringthe61-dayanalysesbecausebatteriesarebetter forshort-term storage.Wefurther compare thecost andenergydistributionof the twelvecases (four systems for three loadconditions). Forall foursystems, theoffice loadreaches thehighest solarcostbut the lowest batterycost,because theworkinghoursaresimilar to the irradiationcurve(seeFigure7).Contrarily, the lab loadreaches thehighestbatterycost for thesamereason(theworkinghoursaredifferent from the irradiationcurve), somorebatteriesneeds tobeusedforenergystorage. Table4.Thedistributionofcost, energysources,andloads. SWPCHSystemtotheLabLoadwith(b, s,w)= (61,18,0) 1.CostDistribution(%) Li-FeBattery 72.98%($1229) powerelectricdevices 2.39%($40) WT 0%($0) Solarpanels 9.87%($166) WTmaintenance 0%($0) Solarmaintenance 1.34%($22) Hydrogenelectrolyzer 5.68%($95) PEMFC 2.15%($36) Chemicalhydrogenproduction 5.56%($93) Sodiumborohydride (NaBH4) 0%($0) 2. EnergySupplyDistribution(%) Wind 0% PEMFC 0% Solar 99.32% battery 0.679% 3. LoadDistribution(%) Labload 95.38% Hydrogenelectrolyzer 4.62% Theoptimizationof thehybrid systemsdemonstrates apreference forusing the solar battery systembecauseof thehighcostofhydrogenproduction. Therefore,we investigatedthe impactsof hydrogenpricesonthe total systemcosts. Figure11showstheresultsofapplying theSWPCHsystem tothe laboratory load. First, thesystemcostsbegin todecreasewhenthehydrogencost falls toabout NT$10perbatch (60gofNaBH4 toproduceabout150Lofhydrogen).Whenthecostofhydrogen 207
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Kategorie
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies