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Energies2019,12, 57
the proposed iterative optimization canbe applied for a quick estimation of the systembehavior.
Simultaneousoptimizationcanbeconsideredforpotentiallybetteroptimization if timepermits.
3.2. LaboratoryLoad
Similarly, theresultsofapplying the laboratory load(seeFigure5) to thehybridpowermodel
are shown in Figure 7 and Table 6. First, the original system layout (b, s, w) = (8, 10, 2) with
managementsettingsof (SOClow,SOChigh)= (40%,50%)resulted inasystemcostof J=1.100$/kWh
and LPSP=26.42%. Note that the LSPSwasmuch higher thanwas obtained for the household,
because the laboratory loadwasmainlyatnightandthestoredenergybyhydrogenelectrolyzation
failed toprovide sufficient energy. The initial componentoptimizationcan reduce the systemcost
to J=0.929$/kWhby setting (b, s,w) = (27, 15, 2) butwith LPSP= 2.34% (see Step 2 of Table 6).
Thesub-optimal settingsof (b, s,w)= (30, 16, 2)gaveLPSP=0with J=0.944$/kWh(seeStep3of
Table6), i.e., thereliabilitywas improvedby26.42%,while thecostwasreducedby14.18%.
ȱ
(a)ȱwȱ=ȱ2ȱandȱ(SOClow,ȱSOChigh)ȱ=ȱ(40%,ȱ50%)ȱ (b)ȱwȱ=ȱ0ȱandȱ(SOClow,ȱSOChigh)ȱ=ȱ(30%,ȱ40%)ȱ
Figure7.Thereferenceplots for the lab load.
Table6.Theoptimaldesignprocedures for the lab load.
(b,s,w) (SOClow,SOChigh) LPSP (%) J ($/kWh)
Step1 (8,10,2) (40%,50%) 26.42% 1.100
Step2 (27,15,2) (40%,50%) 2.34% 0.929
Step3 (30,16,2) (40%,50%) 0% 0.944
Step4 (31,21,0) (40%,50%) 0% 0.684
Step5 (31,21,0) (30%,40%) 0% 0.668
Step6 (27,21,0) (30%,40%) 0% 0.660
Step7 (27,21,0) (30%,40%) 0% 0.660
Optimal (27,21,0) (30%,40%) 0% 0.660
Because theWTwasnoteconomicallyefficient for thisbuilding, settingw=0cangreatlyreduce
the systemcost to J=0.684 $/kWhwithLPSP=0by (b, s,w) = (31, 21, 0) (see Step 4 of Table 6).
The iterativeprocedurescould thenfurther improve thesystemcost to J=0.668$/kWhwithLPSP=0
bysettingthepowermanagementas (SOClow,SOChigh)= (30%,40%),andthecostfinallyconverged
to J=0.660$/kWhwithLPSP=0bysetting(b, s,w)= (27,21,0)and(SOClow,SOChigh)= (30%,40%).
Whencomparedwith theoriginal cost, thecostwasreducedby40%,while thesystemreliabilitywas
reducedby26.42%.
3.3.OfficeLoad
Theanalysesof theoffice load(seeFigure5)areshowninFigure8andTable7. First, theoriginal
systemlayout (b, s,w)= (8,10,2)withmanagementsettingsof (SOClow,SOChigh)= (40%,50%)gavea
systemcostof J=1.107$/kWhandLPSP=5.08%.Optimizingthesettingsslightlyreducedthesystem
cost to J=1.106$/kWhwithLPSP=0using(b, s,w)= (23,11,2) (seeStep2ofTable7).Note that the
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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