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Energy Systems

, Volume 3, Issue 1, pp 77–93 | Cite as

A mixed integer programming approach for optimal power grid intentional islanding

  • Neng Fan
  • David Izraelevitz
  • Feng Pan
  • Panos M. Pardalos
  • Jianhui Wang
Original Paper

Abstract

A power grid island is a self-sufficient subnetwork in a large-scale power system. In weakly connected islands, limited inter-island power flows are allowed. Intentional islanding of a power grid is helpful for the analysis of distributed generation systems connected to a power grid, and valuable for power system reliability of extreme emergency states. In this paper, we use graph partitioning methods to form islands in a power grid and formulate these problems as mixed integer programs. Our models are based the optimal power flow model to minimize the load shedding cost. With these mathematical programming models, optimal formation of islands can be obtained and the different approaches can be compared. Through experiment on IEEE-30-Bus system, computational results are analyzed and compared to provide insight for power grid intentional islanding.

Keywords

Intentional islanding Weakly connected islands Optimal power flow Graph partitioning Mixed integer programming 

Notes

Acknowledgements

This work was supported in part by the Defense Threat Reduction Agency through the grant BRCALL08-A-2-0030. N. Fan is currently with Sandia National Laboratories, which is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Neng Fan
    • 1
    • 2
  • David Izraelevitz
    • 3
  • Feng Pan
    • 3
  • Panos M. Pardalos
    • 4
    • 5
  • Jianhui Wang
    • 6
  1. 1.D-6 Risk Analysis and Decision Support SystemsLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Discrete Math & Complex SystemsSandia National LaboratoriesAlbuquerqueUSA
  3. 3.D-6 Risk Analysis and Decision Support SystemsLos Alamos National LaboratoryLos AlamosUSA
  4. 4.Center for Applied Optimization, Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  5. 5.Laboratory of Algorithms and Technologies for Networks Analysis (LATNA), Higher School of economicsNational Research UniversityMoscowRussia
  6. 6.Center for Energy, Environmental, and Economic Systems AnalysisArgonne National LaboratoryArgonneUSA

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