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Title:Optimizacija proizvodnje električne energije z uporabo modificiranega algoritma diferenčne evolucije
Authors:ID Glotić, Arnel (Author)
ID Tičar, Igor (Mentor) More about this mentor... New window
Files:.pdf DOK_Glotic_Arnel_2015.pdf (8,93 MB)
MD5: 603049BAC2DF150D740A22A7B40B7919
 
Language:Slovenian
Work type:Dissertation
Typology:2.08 - Doctoral Dissertation
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Doktorska disertacija obravnava področje optimizacije proizvodnje električne energije iz hidroelektrarn in termoelektrarn. Nanaša se na kratkoročno obdobje in predstavlja kompleksen optimizacijski problem. Kompleksnost problema izhaja iz velikega števila odvisnih spremenljivk in številnih omejitev elektrarn. Glede na kompleksnost problema je za optimizacijo uporabljen algoritem diferenčne evolucije, ki je sicer znan kot uspešen in robusten optimizacijski algoritem. Uspešnost delovanja algoritma diferenčne evolucije je tesno povezana z izbiro krmilnih parametrov, zmogljivost pa je mogoče izboljšati med drugim tudi s paralelizacijo algoritma. V doktorski disertaciji je predstavljen modificiran algoritem diferenčne evolucije, ki z novim načinom paralelizacije izboljša zmogljivost doseganja globalno optimalnih rešitev pri optimizaciji proizvodnje električne energije. Uspešnost delovanja algoritma pa je izboljšana tudi z novim načinom dinamičnega spreminjanja velikosti populacije. S tem se poleg doseganja kvalitetnejših rešitev v primerjavi s klasičnim algoritmom diferenčne evolucije doseže hitrejša konvergenca postopka oz. se skrajša čas optimizacijskega procesa. Rešitve, ki so predstavljene v disertaciji, so poleg preverjanja na testnih modelih elektrarn, uporabljenih v številnih strokovnih in znanstvenih publikacijah, preverjene tudi na modelih realnih elektrarn. Pri optimizaciji proizvodnje električne energije iz hidroelektrarn in termoelektrarn je zasledovanih več kriterijev: zadovoljitev dane sistemske zahteve oz. voznega reda, minimiziranje porabe vode na enoto proizvedene električne energije, znižanje oz. eliminiranje prelivanja vode, zadovoljitev končnih stanj rezervoarjev hidroelektrarn ter minimiziranje stroškov energenta in emisij pri termoelektrarnah.
Keywords:Optimizacija, hidroelektrarne, termoelektrarne, sistemska zahteva, diferenčna evolucija, paralelni algoritem
Place of publishing:Maribor
Publisher:[A. Glotić]
Year of publishing:2015
PID:20.500.12556/DKUM-47728-51d2669a-ca7f-2f13-76b7-c61c3a97c303 New window
UDC:620.91:621.311.21(043.3)
COBISS.SI-ID:18645782 New window
NUK URN:URN:SI:UM:DK:OKN4CZVV
Publication date in DKUM:24.04.2015
Views:2902
Downloads:450
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
GLOTIĆ, Arnel, 2015, Optimizacija proizvodnje električne energije z uporabo modificiranega algoritma diferenčne evolucije [online]. Doctoral dissertation. Maribor : A. Glotić. [Accessed 19 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=47728
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Secondary language

Language:English
Title:Optimization of Electrical Energy Production by using Modified Differential Evolution Algorithm
Abstract:The dissertation addressed the optimization of electrical energy production from hydro power plants and thermal power plants. It refers to short-term optimization and presents a complex optimization problem. The complexity of the problem arises from an extensive number of co-dependent variables and power plant constraints. According to the complexity of the problem, the differential evolution algorithm known as the successful and robust optimization algorithm was selected as an appropriate algorithm for optimization. The performance of this differential evolution algorithm is closely connected with a control parameters’ set and its capabilities being inter alia improved by the algorithm’s parallelization. The capabilities of achieving a global optimal solution within the optimization of electrical energy production are improved by the proposed modified differential evolution algorithm with new parallelization mode. This algorithm’s performance is also improved by its proposed dynamic population size throughout the optimization process. In addition to achieving better optimization results in comparison with the classic differential evolution algorithm, the proposed dynamic population size reduces convergence time. The improvements of this algorithm presented in the dissertation, besides power plant models mostly used in scientific publications, were also tested on the power plant models represented by real parameters’. The optimization of electrical energy from hydro and thermal power plants is followed by certain criteria; satisfying system demand, minimizing usage of water quantity per produced electrical energy unit, minimizing or eliminating water spillage, satisfying the final reservoir states of hydro power plants and minimizing fuel costs and emissions of thermal power plants.
Keywords:Optimization, Hydro Power Plants, Thermal Power Plants, System Demand, Differential Evolution, Parallel Algorithm


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