Parallel Power System Restoration

TL;DR

This paper presents a comprehensive approach to optimize power system restoration by simultaneously solving grid partitioning and generator startup sequencing, employing integer linear programming, bounding methods, and randomized algorithms for large systems.

Contribution

It introduces a unified model for the parallel power system restoration problem, integrating sectionalization and generator startup sequencing for optimal solutions.

Findings

Efficient ILP formulations for GSS and PPSR problems.

Proven ability to solve systems with up to 2000 buses.

Development of bounding and randomized methods for large-scale systems.

Abstract

Power system restoration is an essential activity for grid resilience, where grid operators restart generators, re-establish transmission paths, and restore loads after a blackout event. With a goal of restoring electric service in the shortest time, the core decisions in restoration planning are to partition the grid into sub-networks, 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 problem or GSS) as soon as possible. Due to the complexity of each problem, the sectionalization and GSS problems are usually solved separately, often resulting in a sub-optimal 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 2000-bus systems.