2018
DOI: 10.1587/transinf.2017edl8177
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An Evolving Network Model for Power Grids Based on Geometrical Location Clusters

Abstract: SUMMARYConsidering that the traditional local-world evolving network model cannot fully reflect the characteristics of real-world power grids, this Letter proposes a new evolving model based on geographical location clusters. The proposed model takes into account the geographical locations and degree values of nodes, and the growth process is in line with the characteristics of the power grid. Compared with the characteristics of real-world power grids, the results show that the proposed model can simulate the… Show more

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“…Motivated by this pioneering work, subsequent studies have been focused on local-world effect in modeling real-world complex networks, characterizing underlying structures and analyzing important dynamical processes occurring in complex systems. Some variants of local-world models are continually proposed to better understand the structural and functional properties of real systems, such as wireless communication networks [8], power grids [9], and energy supply-demand system [10]. Also, localization mechanism has been incorporated into the construction of other models in describing the structures of hypernetworks [11], bipartite networks [12], and weighted networks [13].…”
Section: Introductionmentioning
confidence: 99%
“…Motivated by this pioneering work, subsequent studies have been focused on local-world effect in modeling real-world complex networks, characterizing underlying structures and analyzing important dynamical processes occurring in complex systems. Some variants of local-world models are continually proposed to better understand the structural and functional properties of real systems, such as wireless communication networks [8], power grids [9], and energy supply-demand system [10]. Also, localization mechanism has been incorporated into the construction of other models in describing the structures of hypernetworks [11], bipartite networks [12], and weighted networks [13].…”
Section: Introductionmentioning
confidence: 99%