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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.
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Short-Term Load Forecasting by Artificial Intelligent Technologies
- Title
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Authors
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Editor
- MDPI
- Location
- Basel
- Date
- 2019
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Size
- 17.0 x 24.4 cm
- Pages
- 448
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Category
- Informatik