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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 2.4. Performance IndexesHybridPowerModels ThehybridpowermodelofFigure1wasappliedtopredict thesystemresponsesunderdifferent operationconditionsbasedonthe followingmanagementstrategies (seeFigure6): 1. Toavoidwastingrenewableenergy, thewindandsolarpowersubsystemsareoperatedasfollows: whenthebatterySOCisgreater than98%andthe inputrenewablepower, includingsolarand windpower, isgreater thanthe load, redundantrenewableenergyisdumped. Solarenergy is reducedfirstbecause it ismuchmoreabundant thanwindenergy.WhenthebatterySOCis less than95%,all renewableenergy issuppliedto thesystem. 2. ThePEMFCsystemisswitchedonwhenthebatterySOCreachesalowboundof30%.ThePEMFC is thenswitchedoffwhenthebatterySOCrises toahigh limitof40%.ThePEMFCiscontrolled toprovideadefaultcurrent loadof20Awiththehighestenergyefficiency,andit isset toprovide a loadupto50AwhenthebatterySOCcontinuouslydrops to25%[20]. 3. Thechemicalhydrogengeneratorsystemisswitchedonif thestoragehydrogenlevel is lower than a safety limit [25,26]. Wedesigned abatchprocedurewith suitable production rates to satisfy thesystemrequirements. Eachbatchconsumes60gofNaBH4andproducesabout150L ofhydrogen[25]. Thus, thePEMFCcanbecontinuouslyoperated. (a) Management strategy of the renewable energy. (b) Management strategy of the PEMFC. Figure6.Flowchartsof thepowermanagement. 201
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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