Partial-Dimensional Correlation-Aided Convex-Hull Uncertainty Set for Robust Unit Commitment
Bo Zhou, Jiakun Fang, Xiaomeng Ai, Yipu Zhang, Wei Yao, Zhe Chen, Jinyu Wen
Abstract
Correlations help narrow the uncertainty region in robust unit commitment (RUC) of power systems for economic improvement, yet in high-dimensional cases, state-of-the-art full-dimensional correlation (FDC) based uncertainty set methods suffer from either conservativeness or computational burden. This article proposes the novel partial-dimensional correlation (PDC) aided convex-hull uncertainty set (CHUS) for RUC. The PDC-aided framework is established for the first time to utilize the accurate and accessible PDC instead of the assumed but inaccessible FDC, which provides a general formula that covers both the traditional correlation-ignored and the emerging FDC-based methods. The diamond-cut CHUS of correlation data is developed to approach the compact CHUS to reduce conservativeness under an acceptable complexity. The customized scenario-parallel algorithm is proposed for efficient calculation, which combines the extreme scenario-based constraint rebuild and the parallel computing-enabled column-and-constraint generation. Case studies demonstrate the effectiveness of the proposed method in enhancing both economic and computational efficiency.