Large-scale branch contingency analysis through master/slave parallel computing

doi:10.1007/s40565-013-0024-0

Home > Archive>Volume 1, Issue 2, 2013 >159-166. DOI:10.1007/s40565-013-0024-0

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Large-scale branch contingency analysis through master/slave parallel computing

DOI:

10.1007/s40565-013-0024-0

Author:

Xi YANG
Xi YANG
Decision and Information Sciences Division of Argonne National Laboratory, Argonne, IL, 60439, USA
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Cong LIU
Cong LIU
Decision and Information Sciences Division of Argonne National Laboratory, Argonne, IL, 60439, USA
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Jianhui WANG
Jianhui WANG
Decision and Information Sciences Division of Argonne National Laboratory, Argonne, IL, 60439, USA
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Affiliation:

1. Decision and Information Sciences Division of Argonne National Laboratory, Argonne, IL, 60439, USA

Fund Project:

UChicago Argonne, LLC, Operator of Argonne National Laboratory("Argonne"). Argonne, a U.S. Department of Energy Office of Sciencelaboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paidupnon-exclusive, irrevocable world wide license in said article to reproduce,prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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Abstract:

Contingency analysis (CA) requires fast executiontime for real-time power system operations. Because CA problems can naturally be divided into separate subtasks,parallel computing helps to speed up the computationtime. This paper proposes a master/slave parallel computingarchitecture and studies the computation of CA in alarge-scale power system through high performance computing,adopting a message passing interface for implementation.In particular, although the execution time of CAvaries, there is a tradeoff between having an imbalanced workload and ‘‘paying’’ a synchronization penalty for parallel computing: either factor blocks the progress of scalability. The proposed layered dynamic scheduling method is effective to tackle the challenge of high synchronization cost and work load imbalance and have the potential to further scale for the N - 2 contingency analysis.

Key words:Massive parallel computing, Power system, Contingency analysis

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Online: May 22,2015
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