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.

翻译：在发电系统运行中,单位承诺是一个根本问题。在发电维护的同时,联合优化问题也带来了巨大的计算挑战,因为将维护与UC决定相结合的制约因素。很明显,这些挑战随着网络规模的扩大而增加。随着采用监测发电机健康和基于条件的维护(CBM)的传感器,这些挑战已经扩大。基于ADMM的分散化方法在解决大规模UC问题,特别是纵向集成电力系统的问题方面显示了希望。然而,在目前的形式下,这些方法在考虑联合的UC和CBM问题时,未能产生类似的计算性能和可缩放性。本文为解决大规模、联合的UCBM和CBM问题提供了一个新的分散化优化框架。我们的方法依靠的是采用新颖的次分级方法,在维护地平线上对基于ADMMMM的制定联合问题的各种子问题进行时间分级调和调和分级化。我们分散的分级方法有效地利用了多读方法,从而产生了更好的计算性能,并消除了移动数据的必要性,从而减轻了隐私和安全方面的顾虑。在大规模测试案例中,我们利用了对50x质量标准进行实验,我们的框架可以提供一种速度,而没有牺牲。我们的标准。