An Entropy-Weighted TOPSIS and Directed Temporal-Dependency Framework for Electricity Market Risk Assessment and Propagation Identification

To address the challenge of quantifying the coupling relationship between user-side reporting and bidding behavior and electricity market operational risks, this study proposes a method for electricity market risk assessment and propagation identification based on entropy-weighted TOPSIS and directed temporal-dependency analysis. First, a risk indicator system is constructed across four dimensions, including electricity supply and demand, price volatility, system operation, and user-side bidding credibility. Robust quantile standardization and the entropy-weighted TOPSIS method are employed to construct category-specific and composite risk-state indices and warning levels. Subsequently, the PCMCI+ method is used to identify candidate temporal relationships among risk indicators, and risk propagation pathways are screened through stability and temporal direction tests; on this basis, a dynamic model with pathway constraints is established to quantify the propagation strength between different risk categories based on impulse responses. The PJM case study shows that the framework can distinguish elevated risk-state periods and identify a sparse full-summer directed temporal-dependency pattern, while sensitivity and chronological split-sample analyses reveal that the detailed propagation structure is specification- and period-dependent. These results support the use of the framework for multidimensional risk-state monitoring while also defining the empirical boundary of its propagation interpretation.