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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 declines fromNT$28 toNT$9 (NT$1)per batch, the systemcostdrops fromNT$1685 toNT$1662 (NT$1088)perday. Second, theenergysupplyratiobythePEMFCincreases to19.6%(59.5%)when thehydrogenprice isNT$9orNT$1perbatch. Under this scenario, the systemtends tousemore hydrogen energy, as the cost is competitivewith that of other renewable energies. Last, the stack pricehas little influenceon the systemcostbecause it is considered in the initial cost (from10k to 180k). Forexample,whenthehydrogenprice isgreater thanNT$11/batchandthePEMFCstackcost drops fromNT$180ktoNT$90k, thesystemcostdrops fromNT$1685 toNT$1667perday.Whenthe hydrogencost isNT$9(NT$1)perbatchandthePEMFCstackpricedrops fromNT$180ktoNT$90k, thesystemcostdrops fromNT$1662(NT$1088) toNT$1615(NT$999)perday. +\GURJHQ SULFH SHU EDWFK 17' EDWFK N N N N N N N N Figure11.The influenceofhydrogenpricesonsystemcosts. 4.Conclusions Thispaperdemonstrated theoptimizationofhybridpowersystems.Wedevelopedageneral hybrid power model that consisted of solar cells, a WT, a fuel cell, hydrogen electrolysis, chemicalhydrogengeneration,andbatteries. Themodelparametersare tunedbasedonexperimental data, so that system responses under different operation conditions can be predicted without conducting individual experiments. Then, the performance of four hybrid systems under three typical loadswasevaluatedbycalculatingsystemcostsandreliability. Theresults showedthat the costs and reliability of all the systemswere effectively improved by optimizing the system sizes. The hybrid systemwith the solar panels andbattery sets achieved the lowest costs, aswind and hydrogenenergyarerelativelyexpensiveatpresent. Last, the impactsof stackandhydrogenpriceson systemcostswasanalyzed. Theresults indicatedthathydrogenpriceshadamoresubstantial influence thanthestackpriceonsystemcosts,andthathydrogenenergywouldbecompetitivewhenitsprice fell toaboutone-thirdof thecurrentprice. In futureresearch, the impactofcostofothercomponents, suchas thePVandWT,canbeanalyzedinasimilarway. Author Contributions: Conceptualization, F.-C.W.; Methodology, F.-C.W. and Y.-S.H.; Software, Y.-S.H.; Validation,F.-C.W.,Y.-S.H.andY.-Z.Y.;FormalAnalysis,F.-C.W.andY.-S.H.; Investigation,F.-C.W.andY.-S.H.; Resources,F.-C.W.andY.-S.H.;DataCuration,F.-C.W.,Y.-S.H.,andY.-Z.Y.;Writing-OriginalDraftPreparation, Y.-S.H.; Writing-Review and Editing, F.-C.W.; Visualization, F.-C.W., Y.-S.H., Y.-Z.Y.; Supervision, F.-C.W.; ProjectAdministration,F.-C.W.;FundingAcquisition,F.-C.W. 208
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies