TY - JOUR

T1 - Parallel Power System Restoration

AU - Chopra, Sunil

AU - Qiu, Feng

AU - Shim, Sangho

N1 - Funding Information:
History:Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms – Discrete. Funding:This research was supported by the Visiting Faculty Program of Argonne National Laboratory and the U.S. Department of Energy Advanced Grid Modeling Program [Grant DE-OE0000875].
Publisher Copyright:
© 2022 INFORMS.

PY - 2023/1

Y1 - 2023/1

N2 - 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.

AB - 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.

KW - centered network partition problem

KW - generator startup sequencing

KW - integer programming

KW - power system restoration

KW - randomized rounding

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U2 - 10.1287/IJOC.2022.1258

DO - 10.1287/IJOC.2022.1258

M3 - Article

AN - SCOPUS:85153671978

SN - 1091-9856

VL - 35

SP - 233

EP - 247

JO - INFORMS Journal on Computing

JF - INFORMS Journal on Computing

IS - 1

ER -