Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Page - 203 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 203 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Image of the Page - 203 -

Image of the Page - 203 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text of the Page - 203 -

Energies2018,11, 1948 30Ah, 660W,and200W, respectively. Forexample, (b, s,w)= (1, 2, 3)means that the systemwas equippedwithone30Ahbatteryset, two660WPVarrays,andthree200WWTs. The initial cost Ji consistsof several systemcomponents,as follows: Ji(b,s,w) = ∑ k=component Jki(b,s,w) (6) wherek=PEMFC,DC, solar,WT,HE,CHG, andbatt for thePEMFC,powerelectricdevices,PVarrays, wind turbine, hydrogen electrolyzer, chemical hydrogen generator, and battery set, respectively. Similarly, theoperationcost Jo includes twoparts: Jo(b,s,w) = ∑ l=component Jlo(b,s,w) (7) where l=NaBH4,WT,andsolar forchemicalhydrogen,WTmaintenance,andPVmaintenance,respectively. Thecosts Jki(b,s,w) and J l o(b,s,w) canbecalculatedbythe followingequations: Jki(b,s,w) =Ck ·nk ·CRFk (8) Jlo(b,s,w) =Cl ·nl (9) inwhichC is thecomponentpriceperunit, andn is thecomponentunits.CRF represents the capital recovery factorandisdefinedas follows[10]: CRF= ir(1+ ir)ny (1+ ir)ny−1 (10) where ir is the inflationrate,andny is thecomponent life. Thecomponent lifeandcostare listed in Table2. The inflationratewasset as1.26%byreferring to theaverageannual changeof consumer price indexofTaiwan[4]. Table2.Simulationparameters. Component Lifetime Price ($NT) Hybridsystem 15(year) NA Fuelcell (3kW) 8000(h) 180,000 Powerelectronicdevices (3kW) 15(year) 50,000 PVarray(0.66kW) 15(year) 45,840 Windturbine (0.2kW) 15(year) 19,333 Hydrogenelectrolyzer (410W) 8000(h) 320,000 Chemicalhydrogengenerator 10 (year) 320,000 NaBH4 (60g/Batch,150LH2) NA 28 Thesystemreliability isdefinedas the lossofpowersupplyprobability (LPSP),as follows[4]: LPSP= ∑ T 1 LPS(t) Eload(t) (11) inwhichthenumerator is the total lossofpowersupplyduringtimeintervalT, andthedenominator represents the required load demand during time interval T. The system is more reliable with asmallerLPSP. 203
back to the  book Short-Term Load Forecasting by Artificial Intelligent Technologies"
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
Web-Books
Library
Privacy
Imprint
Austria-Forum
Austria-Forum
Web-Books
Short-Term Load Forecasting by Artificial Intelligent Technologies