Optimal WDG planning in active distribution networks based on possibilistic–probabilistic PEVs load modelling, By Ali Ahmadian

Research

Title	Optimal WDG planning in active distribution networks based on possibilistic–probabilistic PEVs load modelling
Type	Article
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
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.