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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2019,12, 57 Therefore, thepowermanagementcanbeadjustedbytuningSOClow andSOChigh. Asa last stage, thehydrogenelectrolyzer transferredredundantenergytohydrogenstoragebasedonthe following strategies (seeFigure3b) [33]: (1) When the battery SOC was higher than 95%, the extra renewable energy was regarded asredundant. (2) Theelectrolyzermodulewouldwait for tenminutes toavoidchattering. If the total redundant energy increasedduringthisperiod, theelectrolyzerwasswitchedon. (3) When the hydrogen tankwas full or the battery SOCdropped to 85%, the electrolyzerwas switchedoff. Thus, the electrolyzerproducedhydrogenwhen thebatterySOCwasbetween85%and95%. Theelectrolyzermodulewasset toproducehydrogenatarateof1.14L/minbyconsumingaconstant powerof410W,basedontheexperimental results [33]. 2.2. InputsEnergyandOutputLoads Weappliedthehistorical irradiationandwindspeeddata [32],asshowninFigure4, to thePV andWTmodules, respectively.AsshowninFigure4, solar radiationwasabundant in thesummer but poor in thewinter; therefore, solar energy in the summer canbe stored for use in thewinter. Conversely, thewind speedwas high in thewinter but low in the summer, sowind energywas expectedtocompensate for the lackofsolarenergy in thewinter.However, thecompensationeffects werenotas significantasoriginallydesignedbecause thewindwasnot sufficientlystrongandthe energycostwasmuchhigher (seeTable2)whencomparedtootherenergysources.Note thatboth solarandwindenergywereconcentrated in thedaytime, indicating that thisenergyshouldbestored foruseatnight. ȱ (a)ȱ61Ȭdayȱradiationȱdata.ȱ (b)ȱAverageȱdailyȱradiation.ȱ ȱ (c)ȱ61Ȭdayȱwindȱspeedȱdata.ȱ (d)ȱAverageȱdailyȱwindȱspeed.ȱ /RQJ 7LPH ,UUDGLDQFH ( WLPH GD\ 6XPPHU :LQWHU 'DLO\ $YHUDJH ,UUDGLDQFH ( WLPH KU :LQWHU 6XPPHU :LQG 6SHHG /RQJ 7LPH WLPH GD\ 6XPPHU :LQWHU :LQG 6SHHG 'DLO\ $YHUDJH WLPH KU 6XPPHU :LQWHU Figure4.Radiationandwinddata. 87
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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