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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 35%,where thePEMFCisset toprovideadefault currentof20A.ThePEMFCisswitchedoff whenthebatterySOCis40%. 2. ThePVarraytransfers irradiance intoelectricityas follows[4,20]: PPV=0.69(Irr−1.52) wherePPV and Irr representsolarpowerandirradiance, respectively. 3. Thechemicalhydrogengeneration is switchedonwhenthepressureof thehydrogenstorage tankdecreases to 3 bar [25,26]. Currently, hydrogen is generated fromaNaBH4 solution by apreviouslydevelopedauto-batchprocess,withamaximumgenerationrateof93.8standard literspermin(SLPM)[25]. Thiscansustain theoperationofa3kWPEMFC[25]. 4. Thebatteryregulates thepowersupplyandloaddemandsas follows: it is charged(discharged) whenthesupply isgreater (lower) thanthedemand. Toavoidovercharging,batterycharging is stoppedwhenitsSOCreaches98%. Inthispaper,weextendthepreviouslydevelopedmodel[4]byaddingwindpowerandhydrogen electrolysismodules. (a) System configuration. 39 DUUD\V +\GURJHQ HOHFWURO]VHU '& '& &RQYHUWHU '& $& ,QYHUWHU /L32 %DWWHU\ /RDG +\GURJHQ (OHFWULFLW\ :LQG WXUELQH :LQG FRQWUROOHU +\GURJHQ VWRUDJH )XHO FHOO &KHPLFDO K\GURJHQ JHQHUDWRU Figure1.Cont. 195
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies