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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.
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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