A novel hybrid clustering approach for robust ramp event characterization

Large power fluctuations in a brief amount of time, or ramp events, are an increasing concern for grid operators due to the rise in renewable energy generation and the unreliable hour-ahead predictions. To balance these ramp events, grid operators need to be aware of their anticipated occurrence intervals and range. Prior studies used binary ramp event categorization, whereas other studies employed non-causative classification techniques. Existing clustering methods, Z-score and k-means, have strengths but distinct limitations. To address these, this paper introduces the ZK-means hybrid approach, integrating Z-score normalization with k-means partitioning, forming a centroid-based clustering algorithm to enhance adaptability, noise resistance, and interpretability in ramp classification. Its need arises from the growing demand for accurate and efficient ramp analysis to support reliable grid operation and forecasting. Two comparison phases for the ZK-means approach were conducted: First, it was evaluated against its constituent methods to assess the benefits of their combination; second, it was compared to the density-based spatial clustering of applications with noise (DBSCAN) algorithm to verify its robustness and general applicability. Although DBSCAN can capture local variations in data, it produced inconsistent cluster numbers and required frequent parameter tuning across the ten years. In contrast, ZK-means achieved more stable clustering patterns and lower within-cluster variance, demonstrating superior reliability for long-term ramp event characterization. The new categorization method is applied to a real case study, and the results reveal that the new hybrid method offers significant improvements in the quality, robustness, and interpretability of the clustering process and its resulting cluster characteristics, as it combines the stability of normalization with the scalability of k-means, offering a robust and practical solution for large-scale, high-dimensional clustering. While this new method does entail a slight increase in time-speed, computational complexity, and energy consumption compared to its constituent methods, it remains faster than DBSCAN and the enhanced insights it provides offer critical advantages for effective grid management.