Flexible implementation of power system corrective topology control

Dehghanian, Payman; Wang, Yaping; Gurrala, Gurunath; Moreno-Centeno, Erick; Kezunovic, Mladen

doi:10.1016/j.epsr.2015.07.001

Cited by 39 publications

(14 citation statements)

References 28 publications

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“…Constraint (9) limits the power flow across line k within the minimum and maximum line capacities. Power balance at each node is enforced by (10) and Kirchhoff's laws are incorporated in (11) and (12). The status of any line k of the system is identified via an integer variable in (13).…”

Section: Probabilistic Topology Control Optimizationmentioning

confidence: 99%

“…optimal switching solution. Such implementation design would not only increase the chance that at least one set of the solutions would survive all the subsequent AC feasibility/stability tests and other operational concerns, but also would provide the operator with more flexibility in final decision making [12]. The two point estimation method (2-PEM) decomposes (5) into several sub problems by taking only two deterministic values of each uncertain variable located on the two sides of its mean value.…”

Section: Probabilistic Topology Control Optimizationmentioning

confidence: 99%

“…Another extension of the work may be focused on modifications of the DCOPFbased topology control optimizations with inclusions of realistic factors such as losses and nomogram constraints. Future works should also include the practical mechanisms for selection of an optimal switching sequence which is very critical as far as the system stability is concerned [12].…”

Section: B Future Workmentioning

confidence: 99%

“…Topology control as a corrective tool in dealing with system emergency operating conditions is reported for the following: mitigation of voltage violations [3], [4], alleviating the line overloads [5], [6], ensuring system security [7], [8], congestion management [9], [10], and recovering the load shed in the cases of critical contingencies [11], [12]. Application of topology control for network loss reductions is also studied in [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Decision Making for the Bulk Power System Optimal Topology Control

Dehghanian

Kezunovic

2016

IEEE Trans. Smart Grid

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Power system topology control through transmission line switching for economic gains and reliability benefits has been recently considered in day to day operations. This paper introduces a probabilistic formulation for a more efficient application of topology control strategies in real world scenarios with anticipated increase in the presence of highly variable renewable generation and uncertain loads. Such uncertainties are modeled via the Point Estimation Method (PEM) embedded into the DC optimal power flow (DCOPF)-based formulations for optimal switching solutions. Hourly and daily advantages of the proposed probabilistic framework, compared to the conventional operations and deterministic formulations, are discussed. As the anticipated economic gains would increase through sequential implementation of several switching actions, a new probabilistic decision making approach to identify the optimal number of switching actions at each hour is also proposed. This decision support tool uses the probabilistic reliability cost/value analytics in which not only the financial benefits, but also the costs of reliability risks are taken into account. The approach is tested through various scenarios on the modified IEEE 118-bus test system, with and without renewables integration, and the results revealed its applicability and efficiency.

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Section: Probabilistic Topology Control Optimizationmentioning

confidence: 99%

Section: Probabilistic Topology Control Optimizationmentioning

confidence: 99%

Section: B Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Decision Making for the Bulk Power System Optimal Topology Control

Dehghanian

Kezunovic

2016

IEEE Trans. Smart Grid

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“…All the more concerning is the fact that roundoff errors can also cause LP and MIP solvers to misclassify feasible problems as infeasible and vice versa (Koch 2004;Neumaier and Shcherbina 2004). Other puzzling outcomes can unexpectedly occur and remain largely unreported based on their less clear classification; for instance, from the authors' experiences solving large-scale power systems problems in Dehghanian et al (2015) and Escobedo et al (2014), roundoff errors could lead to a paradoxical outcome in which the working basis is eventually flagged as being singular. Though infrequent and unpredictable, the incidence of these incongruous outcomes brings the reliability of mathematical programming software into question and could expose users to serious risks.…”

Section: Introductionmentioning

confidence: 99%

Solution of Dense Linear Systems via Roundoff-Error-Free Factorization Algorithms

Escobedo

Moreno-Centeno

Lourenco

2018

ACM Trans. Math. Softw.

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Exact solving of systems of linear equations (SLEs) is a fundamental subroutine within number theory, formal verification of mathematical proofs, and exact-precision mathematical programming. Moreover, efficient exact SLE solution methods could be valuable for a growing body of science and engineering applications where current fixed-precision standards have been deemed inadequate. This article contains key derivations relating, and computational tests comparing, two exact direct solution frameworks: roundoff-error-free (REF) LU factorization and rational arithmetic LU factorization. Specifically, both approaches solve the linear system Ax = b by factoring the matrix A into the product of a lower triangular (L) and upper triangular (U) matrix, A = LU . Most significantly, the featured findings reveal that the integer-preserving REF factorization framework solves dense SLEs one order of magnitude faster than the exact rational arithmetic approach while requiring half the memory. Since rational LU is utilized for basic solution validation in exact linear and mixed-integer programming, these results offer preliminary evidence of the potential of the REF factorization framework to be utilized within this specific context. Additionally, this article develops and analyzes an efficient streamlined version of Edmonds’s Q-matrix approach that can be implemented as another basic solution validation approach. Further experiments demonstrate that the REF factorization framework also outperforms this alternative integer-preserving approach in terms of memory requirements and computational effort. General purpose codes to solve dense SLEs exactly via any of the aforementioned methods have been made available to the research and academic communities.

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