Fig. 2

Including HVDC transmission data improves the modeling of frequency stability indicators. For three European synchronous areas, we present Machine Learning (ML) models that predict frequency stability indicators from techno-economic features such as generation per type, load, and day-ahead electricity prices. We measure their performance by the \(R^2\) score, which quantifies the share of variance explained by the model (colored bars). As a benchmark, we also provide the \(R^2\) score for the daily profile predictor, which predicts the targets purely based on their daily average evolution (grey bars). The ML models outperform the benchmark by a large margin, showing the overall importance of techno-economic features for frequency stability. Compared to previous models without HVDC features (cf. (Kruse et al. 2021a)), the inclusion of cross-area flows improves the performance by a factor of up to 1.8 (indicated by the numbers above the red bars). The benefits of including HVDC flows are particularly large in GB and the Nordic area