Optimal intentional islanding to enhance the robustness of power grid networks

Pahwa, Sakshi; Youssef, Mina; Schumm, Phillip; Scoglio, Caterina; Schulz, Noel N.

doi:10.1016/j.physa.2013.03.029

Cited by 55 publications

(42 citation statements)

References 27 publications

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“…In this line of reasoning, as pointed out in [196], intentional islanding of a power grid can be an emergency reaction aiming at isolating failures. The aim of this study is to design a novel strategy that forms islands capable of stopping cascading failures.…”

Section: Intentional Islandingmentioning

confidence: 99%

“…The intentional islanding method proposed by [196] have some novel ingredients when compared to other prior islanding techniques, which suffer from two drawbacks: they do not take into account the power flow model, and they are often designed to minimize load shedding only within the islands. The novelty of [196] resides in the incorporation of DC power flow models into the graph, the partition method to split the power grid into islands and the criteria to perform the partition, which aim at minimizing the load shedding not only in the region where the failure starts, but also in the topological complement of the region. The basic idea is to apply the "community" concept (a set of well connected nodes that are less connected to the rest of the network) from CN theory to power grids.…”

Section: Intentional Islandingmentioning

confidence: 99%

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A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

et al. 2015

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This paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and Energies 2015, 8 9212 smart grids, for which novel studies suggest that small-world networks seem to be the best topology.

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Section: Intentional Islandingmentioning

confidence: 99%

Section: Intentional Islandingmentioning

confidence: 99%

A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

et al. 2015

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“…This is a strategy that aims to stop the initial failure occurring in a small part of a power grid [127][128][129] and to prevent it from propagating through the rest of the system, causing thus a larger blackout. In [110,130], the authors suggest that a feasible method to prevent the propagation of disturbances would be to design the network so as to allow for intentionally separable, stable, small islands or "micro-grids".…”

Section: The Benefits Of Adding Linksmentioning

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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“…When the island is large enough compared to the initial network, it is more likely that it has enough capacity. This problem has been studied in the power systems community but almost all the algorithms provided in the literature are heuristic methods that have been shown to be effective only by simulations [8,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Doubly Balanced Connected Graph Partitioning

Soltan

Yannakakis

Zussman

2017

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms

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We introduce and study the Doubly Balanced Connected graph Partitioning (DBCP) problem: Let G=(V, E) be a connected graph with a weight (supply/demand) function p:V →{−1, +1} satisfying|V1| }≤c s , for some constants c p and c s . When G is 2-connected, we show that a solution with c p =1 and c s =3 always exists and can be found in polynomial time. Moreover, when G is 3-connected, we show that there is always a 'perfect' solution (a partition with p(V 1 )=p(V 2 )=0 and |V 1 |=|V 2 |, if |V |≡0(mod 4)), and it can be found in polynomial time. Our techniques can be extended, with similar results, to the case in which the weights are arbitrary (not necessarily ±1), and to the case that p(V ) =0 and the excess supply/demand should be split evenly. They also apply to the problem of partitioning a graph with two types of nodes into two large connected subgraphs that preserve approximately the proportion of the two types.

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Optimal intentional islanding to enhance the robustness of power grid networks

Cited by 55 publications

References 27 publications

A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

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

Doubly Balanced Connected Graph Partitioning

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