Power Grids, Smart Grids and Complex Networks

Scala, Antonio; Caldarelli, Guido; Chessa, Alessandro; Damiano, Alfonso; Mureddu, Mario; Pahwa, Sakshi; Scoglio, Caterina

doi:10.1007/978-94-017-8704-8_8

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…The uniform-flow allocation can be found for any α ≥ 0 from problem (7) and Eq. (1), with an additional set of constraints ensuring that path flows are uniform: (2) N-2 f (2) N-1 f (2) N f (2) 1 f (2) 2 f (N) N+1 f (1) N+1 f (2) N+1 f (3) N+1 f (1) N-1 Figure 5. Ring lattice with N nodes and edges, such that each edge has capacity c, with paths and nodes indexed clockwise from the top.…”

Section: Uniform-flowmentioning

confidence: 99%

“…12/18 f (2) N-2 f (2) N-1 f (2) N f (2) 1 f (2) 2 f (N) N+1 f (1) N+1 f (2) N+1 f (3) N+1 f (1) N-1 f (1) N f (1) 1 f (1) 2 f (1) 3 N+1 at node 1 and uses all edges of the network. If we distribute edge capacity proportionally to flows entering the edge, the network throughput goes to zero as the flow injected at each node increases, because queues of increasing length build up at each node.…”

Section: Uniform-flowmentioning

confidence: 99%

“…on a short path j; the flow f (1) j passes from node j to node ( j + 1) (mod N); the flow f (2) j passes from node ( j + 1) (mod N) to node ( j + 2) (mod N) and exits the network. Consider also that node 1 is a source and sink of a long path, user N + 1, that passes through all nodes, with flow f ( j) N+1 on the edge linking node j to node ( j + 1) (mod N).…”

Section: Network Modelsmentioning

confidence: 99%

“…Because of high construction costs, these networks often end up operating close to the unstable region: a small increase in flow leads to network congestion or even shut-down. [1][2][3][4] Without properly designed congestion control, the consequences can be catastrophic, as in the congestion collapse on the Internet. 5 In 1986, the Internet (then ARPANet) was a slow (56 Kbps) and small network with a large number of hosts (5,089).…”

Section: Introductionmentioning

confidence: 99%

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Controlling congestion on complex networks: fairness, efficiency and network structure

Buzna

Carvalho

2017

Sci Rep

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We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

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Section: Uniform-flowmentioning

confidence: 99%

Section: Uniform-flowmentioning

confidence: 99%

Section: Network Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Controlling congestion on complex networks: fairness, efficiency and network structure

Buzna

Carvalho

2017

Sci Rep

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Multilayer hybrid modeling framework for the performance assessment of interdependent critical infrastructures

Nan

Sansavini

2015

International Journal of Critical Infrastructure Protection

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The use of PLANS and NetworkX in modeling power grid system failures

Hadaj

Strzałka

Nowak

et al. 2022

Sci Rep

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The theoretical and practical aspects and results of simulations based on a specialized tool that is used in the energy industry were adressed. The previously discussed cases in the literature by taking into account the worst case and critical states of networks in terms of complex networks were extended. Using the Monte-Carlo method, the vulnerability of the power grid to node failures was investigated, both in terms of the use of specialized software, which is used in the power industry, and a tool for the analysis of complex networks graphs. We present the results obtained and the observed analogy between the results of the analysis performed in specialized software and the complex network graph analysis tool. It has been shown that the results obtained coincide for both software packages, even though their application focuses on slightly different aspects of system operation. Moreover, further possibilities of extending the research in this direction are proposed, taking into account not only the improvement of the method used, but also a significant increase in the size of the tested structure model.

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Power Grids, Smart Grids and Complex Networks

Cited by 3 publications

References 27 publications

Controlling congestion on complex networks: fairness, efficiency and network structure

Controlling congestion on complex networks: fairness, efficiency and network structure

Multilayer hybrid modeling framework for the performance assessment of interdependent critical infrastructures

The use of PLANS and NetworkX in modeling power grid system failures

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