Robust Unit Commitment Optimization under Volatile Wind Power Outputs Assisted by Clustering-based Data-Driven Techniques

Abstract

This study proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power generation, which integrates a two-stage adaptive robust UC model with machine learning techniques to flexibly capture the uncertainty space of the wind power forecast errors with disjunctive structures. K-means clustering and density-based spatial clustering of applications with noise (DBSCAN) are applied for clustering, and data-driven disjunctive uncertainty sets are constructed as the union of multiple basic uncertainty sets following the clustering results. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. A tailored decomposition-based optimization algorithm is developed to facilitate the solution process. A numerical experiment and a UC case study on the IEEE 39-bus system are presented to demonstrate the effectiveness of the proposed approach. In both applications, using the proposed disjunctive uncertainty sets can effectively reduce the price of robustness compared to the conventional "one-set-fits-all" approach. Furthermore, the disjunctive uncertainty sets using DBSCAN tend to provide less conservative solutions than those using K-means, implying that DBSCAN can handle the outliers and noise of the uncertainty data more efficiently.