Article:Aristidou, P orcid.org/0000-0003- 4429-0225, Fabozzi, D and Van Cutsem, T (2014) Dynamic simulation of large-scale power systems using a parallel schur-complement-based decomposition method. IEEE Transactions on Parallel and Distributed Systems, 25 (10). pp. 2561 -2570 . ISSN 1045 https://doi.org/10.1109/TPDS.2013.252 © 2013 IEEE. This is an author produced version of a paper published in IEEE Transactions on Parallel and Distributed Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. APPENDIX A DOMAIN DECOMPOSITION METHODSDDMs were originally used due to the lack of memory in computing systems: data needed for smaller portions of a problem could fit entirely to the memory while for the whole problem they could not. They lost their appeal as larger and cheaper memory became available, only to resurface in the era of parallel computing. These methods are inherently suited for execution on parallel architectures and many parallel implementations have been presented on multi-core computers, clusters and lately Graphics Processing Units (GPUs) [1], [2]. They are mainly distinguished by three features: subdomain partitioning, problem solution over sub-domains and sub-domain interface variable processing [3]. A.1 Sub-domain PartitioningSub-domain partitioning has to be chosen based on the desired sub-domain characteristics for the given problem. This includes choosing the number of sub-domains, the type of partitioning, and the level of overlap between the sub-domains. Each of these choices depend on a variety of factors such as size, type, and geometry of the problem domain, the number of parallel processors, communication cost, and the actual system being solved.When considering spatial domain problems, such as PDEs, the decomposition is usually g...
Abstract-Time-domain simulation of power system long-term dynamics involves the solution of large sparse systems of nonlinear stiff differential-algebraic equations. Simulation tools have traditionally focused on the accuracy of the solution and, in spite of many algorithmic improvements, time simulations still require a significant computational effort. In some applications, however, it is sufficient to have an approximate system response of the detailed model. The paper revisits the merits of the Backward Euler method and proposes a strategy to control its step size, with the objective of filtering out fast stable oscillations and focusing on the aperiodic behaviour of the system. The proposed method is compared to detailed simulation as well as to the quasi-steadystate approximation. Illustrative examples are given on a small but representative system, subject to long-term voltage instability.Index Terms-long-term dynamics, stiff decay property, backward Euler method, quasi-steady-state approximation, long-term voltage instability
Nonlinear model predictive control (NMPC) is investigated for load frequency control (LFC) of an interconnected power system which is exposed to increasing wind power penetration. The robustified NMPC (RNMPC) proposed here uses knowledge of the estimated worst-case deviation in wind-power production to make the NMPC more robust. The NMPC is based on a simplified system model that is updated using state-and parameter estimation by Kalman filters, and takes into account limitations on among others tie-line power flow. Tests on a proxy of the Nordic power system, shows that the RNMPC is able to fulfill system constraints under worst-case deviations in wind-power production in cases where the nominal NMPC is not. Keywords
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