Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Page - 192 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 192 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Image of the Page - 192 -

Image of the Page - 192 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text of the Page - 192 -

energies Article TheOptimizationofHybridPowerSystemswith RenewableEnergyandHydrogenGeneration Fu-ChengWang* ID ,Yi-ShaoHsiaoandYi-ZheYang DepartmentofMechanicalEngineering,NationalTaiwanUniversity,Taipei10617,Taiwan; r03522831@ntu.edu.tw(Y.-S.H.); rogeryoun123@gmail.com(Y.-Z.Y.) * Correspondence: fcw@ntu.edu.tw;Tel.:+886-2-3366-2680 Received: 26 June2018;Accepted: 23 July2018;Published: 26 July2018 Abstract: This paper discusses the optimization of hybrid power systems, which consist of solar cells, wind turbines, fuel cells, hydrogen electrolysis, chemical hydrogen generation, and batteries. Becausehybridpowersystemshavemultipleenergysourcesandutilizedifferent typesof storage,wefirstdevelopedageneralhybridpowermodelusingtheMatlab/SimPowerSystemTM, andthentunedmodelparametersbasedontheexperimental results. Thismodelwassubsequently applied to predict the responses of four different hybrid power systems for three typical loads, withoutconductingindividualexperiments. Furthermore,costandreliability indexesweredefinedto evaluatesystemperformanceandtoderiveoptimalsystemlayouts. Finally, the impactsofhydrogen costsonsystemoptimizationwasdiscussed. In the future, thedevelopedmethodcouldbeappliedto designcustomizedhybridpowersystems. Keywords:hybridpowersystem; fuelcell; solar;wind;hydrogen;optimization; cost; reliability 1. Introduction Thedevelopment of alternative energy, such as solar,wind, geothermal, hydropower, ocean power, and hydrogen, has attracted much research attention because of the energy crisis and environmentalpollutionproblems.Amongthese, solar,wind,andhydrogenarepromisingalternative energies. Solarcellsandwindturbines (WTs)convert solar irradiationandwindpower, respectively, intoelectricalpower.Hydrogenenergycanbeconvertedintoelectricityviaanelectrochemical reaction of fuel cells. Eachtypeofenergysourcehasvariousstrengthsandweaknesses. Forexample, solarand wind energy are pollution free and relatively cheap toproduce but lack stability because of their dependenceonweather conditions. In contrast, hydrogenenergywith fuel cellsguarantees stable power suppliesbut is expensiveatpresent. Therefore, hybrid systems thatutilizemultiple energy sources and storagemethods are thebest option for reducing systemcosts and increasing system reliability. Previously, inanIranianstudy,MalekiandAskarzadeh[1]designedahybridpowersystem containingphotovoltaic (PV)arrays,aWT,adieselgenerator,andasecondarybattery. Theyshowed thatsystemsconsistingofaWT,dieselgenerator,andasecondarybatterysatisfiedthe loaddemand at the lowest cost. Basedonananalysisofweatherdata inTurkey,DevrimandBilir [2] concluded thatwind energy could compensate for solar (PV) energy inwinter. Therefore, a hybrid system withaWTcanachievebetterperformancethanonewithoutaWT.Martinez-Lucasetal. [3] studied the performance of a system based onWTs and pump storage hydropower on ElHierro Island in theCanaryarchipelago. Thishybridwind–hydropowerplant showed improvements in system performance todifferentwindspeedsandpowerdemands. The most important issues when designing hybrid power systems are the selection of the system components and the component sizes, according to load demands. Wang andChen [4] consideredahybridsystemconsistingofPVarrays,aproton-exchangemembranefuel cell (PEMFC), Energies2018,11, 1948;doi:10.3390/en11081948 www.mdpi.com/journal/energies192
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