Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Symmetry

DOI: 10.3390/sym17071032

Authors: Siting Dai, Jiawen Wang, Tianyao Ji

Institutions: Guangzhou City University of Technology, City University of Macau, South China University of Technology

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896