Blackouts, Restoration, and Islanding: A System Resilience Perspective

Braun, Martin; Hachmann, Christian; Haack, Jonas

doi:10.1109/mpe.2020.2986659

Cited by 30 publications

(21 citation statements)

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“…We numerically show the change in power flow on the lines for successive 2-line removal of (4, 6)(4, 12) and (21,22) (23,24) in the IEEE 30 bus system corresponding to the 2nd order LODF sensitivity of the lines. The DC power flow on the individual lines of (4, 6), (4,12), (21,22), (23,24) for the base case of IEEE 30 bus system at k = 0 are 73.12, 41.62, −2.2, and 0.95 MW respectively. Both the lines (4, 6) and (4, 12) have comparatively high pre-contingency power flow as compared to (21,22) and (23,24).…”

Section: Theorem Given Perturbations Applied On the Admittance Of K L...mentioning

confidence: 99%

“…In this subsection, the case of (1, 2) ∈  1 c is presented to illustrate the steps for identification of reduced number of k-line contingencies at 2 ≤ k ≤ 4 using successive k-line failure identification and topological k-line contingency selection algorithms in the IEEE 30 bus system. The top few lines obtained by sorting the solution of Equation ( 13) at k = 1 are given by  1 c = { (1, 2), (1,3), (2,5), (2,6), (3,4), (4,6), (4,12), (6,7), (6,8), (6,9),(6, (27,28), (24,25), (25,27), (10,20), (4,12), (23,24), (15,23), (6,28), (15,18), (10,17)} with t = 11 are shown in Figure 7. c at k = 2.…”

Section: Ieee 30 Bus Systemmentioning

confidence: 99%

“…For example, the k-th order LODF sensitivity is computed for the k-line contingency (1, 2)(2, 4) at k = 2 that render LS < d %, then the top lines obtained by sorting the k-th order LODF sensitivity are {(1, 3), (2,5), (3,4), (4,6), (5,7), (6, 7)} as shown in the third level in Figure 6. The k-line contingencies that are generated from (1, 2) (2,4) (2,4) is not a critical contingency at k = 2, however, with individual removal of {(1, 3), (2,5), (2,6), (3,4), (4,6)}, it becomes a critical contingency at k = 3. The number of k-line contingencies identified at k = 3 using the topological criteria are t 3 = 120.…”

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confidence: 99%

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An analytical approach for reducing k ‐line failure analysis and load shed computation

Sarkar

2022

IET Generation Trans & Dist

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In this paper, the problem of identification of critical k‐line contingencies that fail one after another in quick succession that render large load shed in the power system is addressed. The problem is formulated as a mixed‐integer non‐linear programming problem (MINLP) that determines total demand that cannot be satisfied under various k‐line removal scenarios. Due to the large search space of the problem, the solution through enumeration is intractable. Two algorithms are proposed using a proposed power flow sensitivity and a topological metric to identify a reduced number of k‐line contingencies that initiate cascading overload failure and islanding of power system respectively, that are used to solve the MINLP iteratively for identification of critical k‐line contingencies. The algorithms identify a reduced number of k‐line contingencies in linear time as compared to the exponential time complexity of brute‐force search for solutions of the MINLP. Case studies show that the proposed algorithms significantly reduce the search space and the computation time of the MINLP problem to find the most critical k‐line contingency in the IEEE 30 and 118 bus systems at 2≤k≤4$2 \le k \le 4$ that are also obtained in the list of k‐line contingencies identified using the proposed algorithms.

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Section: Theorem Given Perturbations Applied On the Admittance Of K L...mentioning

confidence: 99%

Section: Ieee 30 Bus Systemmentioning

confidence: 99%

mentioning

confidence: 99%

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An analytical approach for reducing k ‐line failure analysis and load shed computation

Sarkar

2022

IET Generation Trans & Dist

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“…Whether caused by a terrorist attack in an infrastructure network [3], a new pathogen in a social network [4], or climate change in an ecological network [5], at some point a change in parameters can be sufficiently large to destabilize the system's "good" state as it crosses a bifurcation point [6]. The net result is a loss of resilience, manifesting as power blackouts [7], epidemics [8], and multi-species extinctions [9,10]. In order to anticipate and prevent these catastrophic outcomes, we must be able to judge just how close a system is to one of these "tipping points" [11].…”

Section: Introductionmentioning

confidence: 99%

Sharpening the Universality of Network Resilience Patterns using Motifs

Zhang

Cornelius

2022

J. Phys.: Conf. Ser.

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The resilience of networked systems to perturbations is a fundamental problem with applications to ecosystem management, financial system stability, and cell reprogramming. This key challenge is that in high-dimensional systems, there is no “oracle” that can predict, a priori, which changes to a nonlinear system’s parameters will be harmless vs. which will cause a system-wide failure (bifurcation). Here, we present a proof of principle using the Florida Bay food web network, showing how one can use higher-order network structure to arrive at a reliable, universal scalar indicator of a system’s proximity to a bifurcation. Our framework builds on and sharpens a recently introduced mean-field theory for nonlinear dynamics on networks. We find that by incorporating information on high-order network structure in the form of network motifs, the prediction of resilience is greatly improved, especially near a bifurcation point. Our results stress the key role of higher-order structure in driving a system’s dynamics, offering new ways to anticipate and prevent the collapse of large networks raging from ecosystems to infrastructure networks.

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“…Several authors have studied the resilience of diverse types of networks, e.g., computer networks [22], communication networks [27], or collaboration networks [18], to mention just a few, and also somewhat more generally for flow networks [16]. Also, the resilience of power grids has already being studied extensively, typically regarding extreme weather events or other external influences such as cyber-attacks [4,15,5,24]. However, the concept of power grid resilience is also useful in the context of the necessary shift towards renewable energies, for example the German Energiewende.…”

Section: Introductionmentioning

confidence: 99%

Resilience basins of complex systems: an application to prosumer impacts on power grids

Bien¹,

Schultz²,

Heitzig³

et al. 2021

Preprint

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Comparable to the traditional notion of stability in system dynamics, resilience is currently predominantly measured in a way that quantifies the quality of a system's response, for example the speed of its recovery. We present a broadly applicable complementary measurement framework that quantifies resilience similarly to basin stability by estimating a resilience basin which represents the extent of adverse influences that the system can recover from in a sufficient manner.As a proof of concept, the resilience measurement framework is applied to a stochastic low-voltage DC power grid model which faces the unregulated rise of decentralized energy supply by solar-powered prosumers. Two response options are examined regarding their resilience effect and efficiency: Upgrading the load capacity of existing power lines and installing batteries in the prosumer households.The framework is able to show that line upgrades, while being more laborious to execute, can provide potentially unlimited resilience against energy decentralization. Household batteries, on the other hand, are simpler to realize but also inherently limited in terms of their resilience benefit (achieving only 70% of the resilience of line upgrades in our scenario). This is explained by considering that they can only reduce fluctuations in the power flows, but not the mean flow magnitude. Further, the framework can aid in optimizing budget efficiency by pointing towards threshold budget values as well as ideal strategies for the allocation of the line upgrades and for the battery charging algorithm, respectively, which both happen to depend on the budget size.

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Blackouts, Restoration, and Islanding: A System Resilience Perspective

Cited by 30 publications

References 4 publications

An analytical approach for reducing k ‐line failure analysis and load shed computation

An analytical approach for reducing k ‐line failure analysis and load shed computation

Sharpening the Universality of Network Resilience Patterns using Motifs

Resilience basins of complex systems: an application to prosumer impacts on power grids

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