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A GPP-Based Sectionalization Toward a Fast Power Transmission System Restoration

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Advances in Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries (AHFE 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 599))

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Abstract

High voltage transmission lines, in outdoor area, are in danger of extreme events such as tornadoes and hurricanes. Accordingly, terrible damage of transmission lines will cause a power grid blackout. Sectionalization as a part of a restoration process can make a power grid resilient by splitting it into multiple smaller areas. Then a diminutive portion of the total load is supplied at each area by black-start (BS) generation units with their self-start capability. To find the optimal sectionalization and perform a fast consumer electrification, a mathematical model is designed upon the association between the power transmission system sectionalization (PTSS) and graph-partitioning problem (GPP). The proposed GPP-based PTSS model finds the optimal sectionalization and restoration plan through a bi-level programming structure with sectionalization and restoration levels. Furthermore, pre-emptive goal programming (PEGP) supports the multiple objective termsof both levels. The model’s efficiency is analyzed by IEEE 14- and 118-bus test systems.

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Author information

Authors and Affiliations

  1. Industrial Engineering Department, University of Houston, Houston, TX, 77204, USA

    Saeedeh Abbasi & Gino Lim

  2. Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA

    Masoud Barati

Authors
  1. Saeedeh Abbasi
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  2. Masoud Barati
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  3. Gino Lim
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Corresponding author

Correspondence to Gino Lim .

Editor information

Editors and Affiliations

  1. IBM Corporation, Medfield, Massachusetts, USA

    Paul Fechtelkotter

  2. Electric Reliability Council of Texas, Inc., Taylor, Texas, USA

    Michael Legatt

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Abbasi, S., Barati, M., Lim, G. (2018). A GPP-Based Sectionalization Toward a Fast Power Transmission System Restoration. In: Fechtelkotter, P., Legatt, M. (eds) Advances in Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries. AHFE 2017. Advances in Intelligent Systems and Computing, vol 599. Springer, Cham. https://doi.org/10.1007/978-3-319-60204-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-60204-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60203-5

  • Online ISBN: 978-3-319-60204-2

  • eBook Packages: EngineeringEngineering (R0)

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