Causality-aware multi-layer spatiotemporal graph state-space modeling and security posture assessment for weather-power-carbon systems

• Weather, carbon, and communication effects jointly shape power system risk patterns. • Causality-aware analysis exposes how disturbances propagate across layers. • Distinct risk signatures separate communication degradation from malicious attacks. The intricate coupling of meteorological volatility, decarbonization mandates, and communication irregularities significantly complicates power system risk assessment, yet a unified framework to model these interactions remains elusive. To bridge this gap, this paper develops a causality-aware spatiotemporal graph state-space model (GSSM) tailored for the coupled weather-power-carbon nexus. The proposed method explicitly aligns graph signals across three heterogeneous layers by employing semantic channel mapping. Directed, time-lagged interactions are captured via coupling matrices, where the underlying topology and intensity are quantified using transfer entropy. Distinctively, communication states are treated as exogenous security variables impacting observation and control channels, which allows for a robust distinction between benign natural degradation and malicious adversarial attacks. This architecture facilitates real-time diagnosis under diverse stress scenarios ranging from extreme weather and carbon-price shifts to cyber-physical disruptions. Empirical validation on IEEE 14-bus and 118-bus systems verified the event discrimination and detection performance through specialized situational indexes.