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Energies2018,11, 1948 Table3.Optimalsystemcosts. DailyEnergyConsumption(kWh) Lab Office House 30.318 21.885 19.933 SystemCostPerDay - - - SW 1399 865 1064 SWPH 1591 1148 1246 SWPC 1529 963 1194 SWPHC 1685 1241 1340 SystemCostPerkWh - - - SW 46.144 39.525 53.379 SWPH 52.477 52.456 62.509 SWPC 50.432 44.003 59.901 SWPHC 55.578 56.706 67.225 3.Discussion Theanalysesof the fourhybridpowersystemsshowedthat systemcostandreliabilitycanbe greatly improvedbyoptimizing systemsizes. For example, Figure 10 shows the referenceplot of applyingtheSWPHCmodel to the laboratory load. Ifweuse10unitsofbattery (300Ah),10unitsof solar (6.6kW),andnoWT, thesystemcost isestimatedasNT$3208/day(orNT$106.17/kWh)with apossiblepowercut (LPSP=0.33%). BasedonFigure10, theoptimalsystemsettingshouldbe61units ofbattery (1830Ah),18unitsof solar (11.88kW),andnoWT.Usingthesesettings, thesystemcost is reducedtoNT$1,685/day(orNT$55.6/kWh),andsystemreliability is improvedto100%(LPSP=0). 39 DUUD\V N: &RQWRXUV RI 3(0)& 6RODU :LQG %DWWHU\ +( &+* 6\VWHP /DE /RDG E V Z Figure10.ThereferenceplotofapplyingSWPHCto lab load. 206
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies