Power quality disturbances (PQDs) and energy management parameters (EMPs) pose significant operational challenges in radial distribution systems. Fast and accurate computational solutions are essential for efficient system performance. This study introduces a novel parallel computing approach for clustering-based PQD and EMP analysis, leveraging the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed method reduces processing times by more than 40% compared to sequential implementations, enabling real-time analysis and facilitating operator decision-making. A comparative evaluation with a parallel k-means approach reveals that k-means achieves slightly higher clustering efficiency, while DBSCAN provides automatic cluster selection. The methodology is validated on the IEEE 33-bus radial distribution system, where new correlations between PQD variations and their impact on EMPs are identified, offering insights for energy management optimization. The findings demonstrate that parallel computing enhances clustering performance, significantly improving computational efficiency and integration of PQD and EMP calculations for large-scale distribution networks.
