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Artificial intelligence is increasingly used for real-time decision support and control in cyber-physical smart grids. However, the growing reliance on data-driven control creates a need for methods that are both explainable to human operators and consistent with electrical-network physics. This review proposes a two-dimensional classification scheme for AI methods in smart-grid control. The scheme combines an explainability dimension with a physics-informed dimension and applies the resulting framework to voltage regulation, congestion management, and microgrid control. A systematic literature search identified 25 peer-reviewed journal articles published between 2017 and 2025. The selected studies were classified according to their explainability mechanism, degree of physical-knowledge integration, application domain, and relevance to cyber-physical grid operation. The review shows that most studies emphasize either explainability or physical consistency, while methods combining both remain limited. Voltage regulation is dominated by reinforcement-learning and safety-constrained control studies, whereas microgrid control contains a broader variety of AI methods. The proposed classification provides a structured basis for analysing trustworthy AI in smart-grid control. It highlights methodological gaps and identifies under-developed combinations of explainability, physics-informed learning, and operational deployment.

Cyber-physical attacks on smart grids can compromise the measurements, forecasts, and control signals used in dispatch. A resilient decision-support system must detect anomalies, explain their operational meaning, and support secure re-dispatch before manipulated data causes unsafe operation. This article proposes a cyber-resilient decision-support system that integrates anomaly detection, explainable alerts, and robust optimization for smart-grid dispatch. The system is designed as an operator-facing architecture rather than a standalone detection model. The system combines machine learning-based anomaly detection, explainable artificial intelligence-driven alert generation, and robust dispatch re-optimization. Validation is structured around false data injection, voltage measurement manipulation, and load manipulation attacks on an IEEE test feeder. The proposed decision-support system identifies cyber-physical anomalies, converts model outputs into actionable explanations, and updates dispatch decisions under attack-induced uncertainty. Compared with non-resilient dispatch and detection-only benchmarks, it reduces unsafe voltage excursions and avoidable line-flow stress. The integrated architecture improves cyber-resilience by connecting detection, explanation, and mitigation in one operational workflow. It supports faster operator interpretation while maintaining dispatch feasibility under uncertain measurements.