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Parallel Power System Restoration

Author

Listed:
  • Sunil Chopra

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Feng Qiu

    (Energy Systems Division, Argonne National Laboratory, Lemont, Illinois 60439)

  • Sangho Shim

    (School of Engineering, Mathematics and Science, Robert Morris University, Moon Township, Pennsylvania 15108)

Abstract

After a blackout event, power system restoration is an essential activity for grid resilience; operators restart generators, re-establish transmission paths, and restore loads. With a goal of restoring electric service in the shortest time, the core decisions in restoration planning are to partition the grid into subnetworks, each of which has an initial power source for black-start (called sectionalization problem), and then restart all generators in each network (called generator startup sequencing (GSS) problem) as soon as possible. Due to their complexity, the sectionalization and GSS problems are usually solved separately, often resulting in a suboptimal solution. Our paper develops models and computational methods to solve the two problems simultaneously. We first study the computational complexity of the GSS problem and develop an efficient integer linear programming formulation. We then integrate the GSS problem with the sectionalization problem and develop an integer linear programming formulation for the parallel power system restoration (PPSR) problem to find exact optimal solutions. To solve larger systems, we then develop bounding approaches that find good upper and lower bounds efficiently. Finally, to address computational challenges for very large power grids, we develop a randomized approach to find a high-quality feasible solution quickly. Our computational experiments demonstrate that the proposed approaches are able to find good solutions for PPSR in up to 2,000-bus systems.

Suggested Citation

  • Sunil Chopra & Feng Qiu & Sangho Shim, 2023. "Parallel Power System Restoration," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 233-247, January.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:1:p:233-247
    DOI: 10.1287/ijoc.2022.1258
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    References listed on IDEAS

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    1. Carleton Coffrin & Pascal Van Hentenryck, 2014. "A Linear-Programming Approximation of AC Power Flows," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 718-734, November.
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