Large-Scale Maintenance and Unit Commitment: A Decentralized Subgradient Approach
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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 along with the decentralized subgradient formulation, our approach delivers superior computational performance. Our approach does not require transfer of sensor data from its source thereby alleviating cybersecurity concerns. Using experiments on large scale test cases, we show that our framework delivers significantly faster solutions compared to centralized techniques without compromising solution quality.
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