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Distribution-grid congestion is becoming more difficult to anticipate as photovoltaic generation, batteries, electric vehicles, and flexible demand reshape feeder power flows. Graph neural networks provide a natural way to represent feeders, but a conceptual framework is needed to connect topology, physics, and flexibility signals coherently. This conceptual research article proposes a physics-informed graph neural network framework for forecasting distribution-line congestion and voltage violations. The framework is designed to produce hourly congestion probabilities over a 24-hour horizon. The proposed model represents buses as graph nodes and lines or transformers as graph edges, while DER flexibility signals are encoded as forward-looking node attributes. Physical consistency is introduced through power-flow residuals, voltage-bound penalties, and thermal-limit penalties in the learning objective. Rather than reporting experimental results, the article specifies the architecture, input requirements, loss formulation, benchmarking logic, and explainability design required for future empirical validation. The framework is intended to guide implementation on realistic feeder datasets. The proposed PINN-GNN is expected to improve congestion awareness by combining electrical topology, flexibility availability, and physics-informed regularization. Its explainability layer is designed to help operators identify which lines, buses, or DER resources most influence a forecasted congestion event.

Autonomous control is increasingly relevant to smart-grid operation, where control systems may support voltage regulation, frequency control, dispatch, and anomaly response. Yet their trustworthiness remains uncertain when decisions are opaque, intervention options are unclear, and cyber-physical conditions are unstable. Existing research often treats explainability, operator intervention, and cyber-physical uncertainty as separate design or evaluation issues. This separation limits theory development because trust in autonomous grid control depends on how these mechanisms operate together. This article develops a conceptual theory of trustworthy autonomous smart-grid control. The theory links explainability, operator intervention, and cyber-physical uncertainty to both perceived and operational trustworthiness. The article uses theory-building logic rather than empirical testing. It synthesizes concepts from trustworthy AI, human-automation interaction, explainable AI, and cyber-physical security to construct mechanisms and derive propositions. The proposed model specifies causal pathways through which explanation quality, intervention capability, and uncertainty handling shape trustworthiness. It also identifies interaction effects, boundary conditions, and testable propositions for future research. The theory provides a foundation for designing, evaluating, and empirically testing trustworthy autonomous control systems in smart-grid environments. It emphasizes that trustworthiness is not produced by transparency, human oversight, or robustness alone, but by their coordinated operation.

Sophisticated machine learning (ML) techniques have demonstrated superior performance over conventional methods across numerous forecasting tasks, particularly in electricity price forecasting (EPF). Nevertheless, the predictive capability of these ML models deteriorates markedly when encountering input data that differs significantly from the examples observed during training. This phenomenon frequently occurs in EPF due to shifts in market conditions, such as surges in fuel costs, higher integration of renewable energy sources, or modifications in regulatory frameworks and operational rules. While the decline in accuracy on unfamiliar data is problematic, an even greater difficulty lies in the inability to anticipate when the model will produce unreliable outputs. This lack of foresight exposes electricity market participants—including bidding agents and retailers—to considerable financial risks stemming from erroneous price predictions. Consequently, it is critical to determine whether a given model prediction can be relied upon in real time. To address this need, the present study introduces a trust evaluation algorithm for EPF applications grounded in explainable artificial intelligence (XAI) methods. The proposed algorithm computes trust scores that indicate the reliability of each new prediction generated by the model. These scores are constructed in two phases: first, a preliminary score is derived from the correlation between local and global explanations; second, the score is refined using SHapley Additive exPlanations (SHAP) values for individual features. Such score-based interpretations are more accessible and actionable for EPF practitioners, such as asset managers and traders, than conventional feature-based visualizations. The effectiveness of the algorithm is assessed using datasets from the Italian electricity market and the Electric Reliability Council of Texas (ERCOT). Empirical results reveal that the method achieves over 85% accuracy in recognizing reliable predictions when the incoming data distribution closely matches the training distribution. Under conditions of distributional shift, the algorithm maintains a comparable level of accuracy in correctly flagging unreliable predictions.