Parallel Power System Restoration

Sunil Chopra, Feng Qiu, Sangho Shim*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)233-247
Number of pages15
JournalINFORMS Journal on Computing
Volume35
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • centered network partition problem
  • generator startup sequencing
  • integer programming
  • power system restoration
  • randomized rounding

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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