Application of the load flow and random flow models for the analysis of power transmission networks

Zio, Enrico; Piccinelli, Roberta; Delfanti, Maurizio; Olivieri, Valeria; Pozzi, Mauro

doi:10.1016/j.ress.2012.02.005

Cited by 13 publications

(7 citation statements)

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“…In this section, we use the transmission network system IEEE 14 bus [33] as reference case study. The IEEE 14 Bus Test Case represents a portion of the American Electric Power Grid (in the Midwestern US), which is widely implemented for different case studies including short circuit analysis, load flow studies and interconnected grid problems.…”

Section: Numerical Results Of a Real Networkmentioning

confidence: 99%

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Network reliability analysis based on percolation theory

Zhang

Zio

et al. 2015

Reliability Engineering & System Safety

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117

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a b s t r a c tIn this paper, we propose a new way of looking at the reliability of a network using percolation theory. In this new view, a network failure can be regarded as a percolation process and the critical threshold of percolation can be used as network failure criterion linked to the operational settings under control. To demonstrate our approach, we consider both random network models and real networks with different nodes and/or edges lifetime distributions. We study numerically and theoretically the network reliability and find that the network reliability can be solved as a voting system with threshold given by percolation theory. Then we find that the average lifetime of random network increases linearly with the average lifetime of its nodes with uniform life distributions. Furthermore, the average lifetime of the network becomes saturated when system size is increased. Finally, we demonstrate our method on the transmission network system of IEEE 14 bus.

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Section: Numerical Results Of a Real Networkmentioning

confidence: 99%

“…For the analysis, we refer to nodes and edges to represent the network components. In this case study, failures of edges are considered, and with different rate and probability values taken from Tables 1 and 2 of reference [33]. For completeness, the failure rate data are reported in Table 1.…”

Section: Numerical Results Of a Real Networkmentioning

confidence: 99%

Network reliability analysis based on percolation theory

Zhang

Zio

et al. 2015

Reliability Engineering & System Safety

Self Cite

117

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“…The grid service efficiency, GSE, is obtained from the ratio of two powers -the grid received load, GRL, to the grid demanded load, GDL [9], [10]:…”

Section: Percolation and Network Resiliencymentioning

confidence: 99%

Topological resiliency analysis of the Australian electricity grid with increased penetration of renewable resources

Akter

Nasiruzzaman

Mahmud

et al. 2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

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In this paper, a complex network framework based network resiliency (percolation) analysis has been presented. A topological model of transmission level Australian National Electricity Market (NEM) with projected renewable integration has been simulated. The effects of random and targeted removal of transmission lines or substations on the network structure and functionality have been analyzed. A fast and simple algorithm to analyze percolation on large scale power grid has also been addressed.

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“…Researchers have proposed many alternative definitions of network robustness. Here we have used grid service efficiency, GSE, as a functional measure of network robustness defined in Zio and Piccinelli (2010); Zio et al (2012):…”

Section: Percolation and Network Resiliencymentioning

confidence: 99%