Symmetry-Guided Identification of Spatial Electricity Price Anomalies via Data Partitioning and Density Analysis

Accurate identification of electricity price anomalies is essential for enhancing transparency, stability, and efficiency in modern electricity markets. While prior methods primarily focus on temporal patterns, this study introduces a novel approach to detecting spatial anomalies by leveraging latent symmetry structures in nodal price data. The method consists of two key stages: (1) applying dimensionality reduction and density-based clustering (t-SNE + DBSCAN) to uncover symmetrical price zones, and (2) deploying the Isolation Forest algorithm to identify anomalous nodes and zones based on intra-zone and inter-zone data density deviations. Empirical tests on a full-year dataset from the PJM market (over 2000 nodes, 15 min intervals) show that the proposed method (M1) achieves a spatial anomaly detection accuracy above 95%, with false alarm rates consistently below 13%. Compared to benchmark models—including unzoned Isolation Forest (M2) and K-means-based methods (M3)—the proposed framework demonstrates superior stability and interpretability, especially in identifying clustered and zone-level anomalies linked to congestion or structural disturbances. By integrating spatial symmetry awareness into the detection framework, this approach enhances both sensitivity and traceability, enabling early-stage identification of systemic anomalies. The method is data-efficient and adaptable to diverse electricity market architectures. Overall, the proposed framework contributes a scalable and interpretable tool for anomaly surveillance in electricity markets, supporting more resilient and transparent market operations.