Managing Massive RES Integration in Hybrid Microgrids: A Data-Driven Quad-Level Approach With Adjustable Conservativeness

Hybrid ac/dc microgrids (HMGs) have emerged as a promising paradigm for integrating large numbers of inherently uncertain and correlated renewable energy sources (RESs). To address the uncertainty introduced by extensive RES integration, a quad-level energy management model is proposed for HMGs, incorporating a novel data-driven uncertainty set. Specifically, a pair convex hull uncertainty set (PCHUS) is developed with adjustable size, which utilizes a graph neural network to identify and exclude outliers in RES data. This approach provides trustworthy RES data at any given confidence level. Then, a quad-level energy management model is designed to determine the minimal-size PCHUS among all possible options, ensuring the least conservative solution, while maintaining robustness against RES fluctuations. Furthermore, a modified version of Taguchi’s orthogonal array testing (TOAT) method, termed quasi-TOAT, enhances the proposed solution algorithm. This modification enables parallel processing capabilities, significantly improving both the global optimum-seeking process and computational efficiency. To validate the proposed approach, the proposed uncertainty set and enhanced algorithm are compared against existing methods using a practical HMG case study. The results demonstrate the effectiveness and superiority of the proposed methodology in managing uncertainty within HMGs.