Performance of Electric Power Systems Under Physical Malicious Attacks

Bilis, Evangelos I.; Kröger, Wolfgang; Nan, Cen

doi:10.1109/jsyst.2012.2223512

Cited by 74 publications

(38 citation statements)

References 37 publications

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“…However, the opposing community asserts that the pure topological CN approach loses the details of the physics behind Kirchhoff's laws and fails at predicting important aspects of power grids. In this respect, as mentioned, hybrid approaches include concepts from EE [82][83][84]96,[98][99][100][101][102]. Nonetheless, the CN approach with purely topological analysis (or even with extended ones to take into account minimal electrical information [34]) has been found to be useful to detect critical elements and to assess topological robustness [35,97,103].…”

Section: Discussion: Is the Cn Approach Useful In Power Grids?mentioning

confidence: 99%

Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

et al. 2017

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Abstract:In this work, we describe an approach that allows for optimizing the structure of a smart grid (SG) with renewable energy (RE) generation against abnormal conditions (imbalances between generation and consumption, overloads or failures arising from the inherent SG complexity) by combining the complex network (CN) and evolutionary algorithm (EA) concepts. We propose a novel objective function (to be minimized) that combines cost elements, related to the number of electric cables, and several metrics that quantify properties that are beneficial for SGs (energy exchange at the local scale and high robustness and resilience). The optimized SG structure is obtained by applying an EA in which the chromosome that encodes each potential network (or individual) is the upper triangular matrix of its adjacency matrix. This allows for fully tailoring the crossover and mutation operators. We also propose a domain-specific initial population that includes both small-world and random networks, helping the EA converge quickly. The experimental work points out that the proposed method works well and generates the optimum, synthetic, small-world structure that leads to beneficial properties such as improving both the local energy exchange and the robustness. The optimum structure fulfills a balance between moderate cost and robustness against abnormal conditions. Our approach should be considered as an analysis, planning and decision-making tool to gain insight into smart grid structures so that the low level detailed design is carried out by using electrical engineering techniques.

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Section: Discussion: Is the Cn Approach Useful In Power Grids?mentioning

confidence: 99%

Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

et al. 2017

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show abstract

“…Likewise, metaheuristic formulations to identify promising attack targets are also common [37], [38], [40], [79], [84]- [88]. Finally, and as previously mentioned, network structural metrics are also used within the power systems literature on attack simulation [33], [46], [47], [54], [55], [89]- [91].…”

Section: E Attack Modellingmentioning

confidence: 99%

“…In essence, it uses mixed integer linear programming to select the K lines to remove from a system, where the novel objective function estimates post-removal line overloads (details of this formulation are given anon). Random This simple strategy is included as a baseline control, as in [91]. For each power system snapshot to be attacked, the K lines to be removed are selected based on a pseudorandom integer generator.…”

Section: A Branch Interdiction Strategiesmentioning

confidence: 99%

A Comparison of Malicious Interdiction Strategies Against Electrical Networks

Cuffe

2017

IEEE J. Emerg. Sel. Topics Circuits Syst.

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“…A heuristic methodology [1] uses five centrality measures: degree centrality, betweenness centrality, eccentricity centrality, centroid centrality and radiality. The methodology ranks nodes in five lists, one list for each centrality measure.…”

Section: Related Workmentioning

confidence: 99%