Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments

To represent the cross-border exchange capacities defined by the flow-based approach in the European resource adequacy assessments, transmission system operators currently employ a data-driven methodology that consists of sequential clustering and correlation steps. This methodology entails assumptions and simplifications within both clustering and correlation analyses that may lead to an erroneous representation of import–export capacities in the subsequent adequacy assessments. While the first stage of this methodology can be improved by leveraging a clustering technique tailored to adequacy assessments, the correlation step presents a poor performance in terms of accuracy and scalability. To address the latter challenges, this paper proposes a supervised learning-based model that can enhance the mapping between several relevant explanatory variables and the pre-clustered flow-based domains, leading to a more accurate representation of the flow-based domains in adequacy assessments. Furthermore, the current paper leverages supervised learning to develop a single-step approach that directly maps the selected explanatory variables to the flow-based domains using the K-Nearest Neighbors algorithm, eliminating the clustering step. This circumvents inaccuracies introduced by the significant intra-cluster discrepancies due to numerous shapes and forms of the flow-based domains and enables an enhanced modeling of the flow-based domains in adequacy assessments. In an extensive case study, we demonstrate that the proposed single-step model can significantly improve the accuracy of adequacy assessments, compared to the best-in-class result obtained by the two-step set-up. Moreover, the proposed single-step model involves no hyper-parameters, eliminates the computational complexity of the two-step set-up, and efficiently upscales to integrate the new zones joining to the flow-based market coupling.