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Proceedings ArticleDOI

On Bus Type Assignments in Random Topology Power Grid Models

TLDR
The correlation between the three bus types of G/L/C and some network topology metrics such as node degree distribution and clustering coefficient is examined and the impacts of different bus type assignments on the grid vulnerability to cascading failures are investigated.
Abstract
In order to demonstrate and test new concepts and methods for the future grids, power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments. If the random networks are truly representative and if the concepts or methods test well in this environment they would test well on any instance of such a network as the IEEE model systems or other existing grid models. Our previous work [1] proposed a random topology power grid model, called RT-nested-small world, based on the findings from a comprehensive study of the topology and electrical properties of a number of realistic grids. The proposed model can be utilized to generate a large number of power grid test cases with scalable network size featuring the same small-world topology and electrical characteristics found from realistic power grids. On the other hand, we know that dynamics of a grid not only depend on its electrical topology but also on the generation and load settings, and the latter closely relates with an accurate bus type assignment of the grid. Generally speaking, the buses in a power grid test case can be divided into three categories: the generation buses (G), the load buses (L), and the connection buses (C). In [1] our proposed model simply adopts random assignment of bus types in a resulting grid topology, according to the three bus types' ratios. In this paper we examined the correlation between the three bus types of G/L/C and some network topology metrics such as node degree distribution and clustering coefficient. We also investigated the impacts of different bus type assignments on the grid vulnerability to cascading failures using IEEE 300 bus system as an example. We found that (a) the node degree distribution and clustering characteristic are different for different type of buses (G/L/C) in a realistic grid, (b) the changes in bus type assignment in a grid may cause big differences in system dynamics, and (c) the random assignment of bus types in a random topology power grid model should be improved by using a more accurate assignment which is consistent with that of realistic grids.

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Statistical Considerations in the Creation of Realistic Synthetic Power Grids for Geomagnetic Disturbance Studies

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Using interdependency matrices to mitigate targeted attacks on interdependent networks

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References
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Journal ArticleDOI

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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Journal ArticleDOI

The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Proceedings ArticleDOI

Random graphs

TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
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