Large-Scale Maintenance and Unit Commitment: A Decentralized Subgradient Approach

TL;DR

This paper introduces a decentralized subgradient method for large-scale joint unit commitment and maintenance optimization, significantly improving computational efficiency and privacy preservation over existing ADMM-based approaches.

Contribution

A novel decentralized subgradient framework that enhances scalability and privacy in solving large-scale joint UC and CBM problems using multithreading.

Findings

Achieves up to 50x speedup over benchmarks

Maintains solution quality comparable to existing methods

Effectively handles large-scale power system optimization

Abstract

Unit Commitment (UC) is a fundamental problem in power system operations. When coupled with generation maintenance, the joint optimization problem poses significant computational challenges due to coupling constraints linking maintenance and UC decisions. Obviously, these challenges grow with the size of the network. With the introduction of sensors for monitoring generator health and condition-based maintenance(CBM), these challenges have been magnified. ADMM-based decentralized methods have shown promise in solving large-scale UC problems, especially in vertically integrated power systems. However, in their current form, these methods fail to deliver similar computational performance and scalability when considering the joint UC and CBM problem. This paper provides a novel decentralized optimization framework for solving large-scale, joint UC and CBM problems. Our approach relies on the novel use of the subgradient method to temporally decouple various subproblems of the ADMM-based formulation of the joint problem along the maintenance horizon. By effectively utilizing multithreading, our decentralized subgradient approach delivers superior computational performance and eliminates the need to move sensor data thereby alleviating privacy and security concerns. Using experiments on large scale test cases, we show that our framework can provide a speedup of upto 50x as compared to various state of the art benchmarks without compromising on solution quality.