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فارسی
Ali Ahmadian

Ali Ahmadian

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId:
HIndex: 0/00
Faculty: Faculty of Engineering
Address:
Phone: 04137745000
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Research

Title
Optimal WDG planning in active distribution networks based on possibilistic–probabilistic PEVs load modelling
Type
JournalPaper
Keywords
distributed power generation , electric vehicles , genetic algorithms , particle swarm optimisation , power distribution planning , power distribution reliability , power generation planning , power generation reliability , wind power ,reliable operation , hybrid modified particle swarm optimisation-genetic algorithm , economic constraint , technical constraint , optimisation problem , renewable based distributed resource , PEV uncertain spatial effect , PEV temporal uncertainty , optimal wind distributed generation planning , plug-in electric vehicles load demand model , possibilistic-probabilistic PEV load modelling , active distribution network , optimal WDG planning
Year
2017
Journal IET Generation Transmission & Distribution
DOI
Researchers Ali Ahmadian ، Mahdi Sedghi ، Ali Elkamel ، Masoud Aliakbar Golkar ، Michael Fowler

Abstract

Distribution network operators and planners usually model the load demand of plug-in electric vehicles (PEVs) to evaluate their effects on operation and planning procedures. Increasing the PEVs' load modelling accuracy leads to more precise and reliable operation and planning approaches. This study presents a methodology for possibilistic-probabilistic-based PEVs' load modelling in order to be employed in optimal wind distributed generation (WDG) planning. The proposed methodology considers not only the PEVs temporal uncertainty, but also the uncertain spatial effect of PEVs on WDGs as renewable-based distributed resources. The WDG planning is considered as an optimisation problem which is solved under technical and economic constraints. A hybrid modified particle swarm optimisation/genetic algorithm is proposed for optimisation that is more robust than the conventional algorithms. The effectiveness of the proposed load modelling of PEVs and the proposed algorithm is evaluated in several scenarios.
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