Learning to branch in the generation maintenance scheduling problem

• Published in: Global Energy Interconnection Vol. 5; no. 4; pp. 409 - 417
• Main Authors: Mei, Jingcheng, Hu, Jingbo, Wan, Zhengdong, Qi, Donglian
• Format: Journal Article
• Language: English
• Published: KeAi Communications Co., Ltd 01.08.2022
• Subjects: Generation maintenance scheduling; Strong Branching (SB); Support vector machine (SVM); Variable selection
• ISSN: 2096-5117
• DOI: 10.1016/j.gloei.2022.08.007

To maximize the reliability index of a power system, this study modeled a generation maintenance scheduling problem that considers the network security constraints and rationality constraints of the generation maintenance practice in a power system. In view of the computational complexity of the generation maintenance scheduling model, a variable selection method based on a support vector machine (SVM) is proposed to solve the 0–1 mixed integer programming problem (MIP). The algorithm observes and collects data from the decisions made by strong branching (SB) and then learns a surrogate function that mimics the SB strategy using a support vector machine. The learned ranking function is then used for variable branching during the solution process of the model. The test case showed that the proposed variable selection algorithm — based on the features of the proposed generation maintenance scheduling problem during branch-and-bound — can increase the solution efficiency of the generation-scheduling model on the premise of guaranteed accuracy.