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Reinforcement learning can autonomously dispatch demand response actions under changing prices, loads, and grid constraints. However, operator acceptance requires explanations that clarify why specific customers or aggregations are selected. This article presents an explainable reinforcement learning framework for demand response dispatch using customer flexibility profiles, decomposed rewards, and operator review. The objective is to preserve dispatch performance while improving transparency and controllability. The framework combines a profile-aware RL dispatcher, a decomposed reward model, an explanation generator, and a structured operator review protocol. Evaluation is performed through simulation on realistic demand response scenarios with household and flexible-load representations. The proposed explainable RL framework is designed to achieve dispatch performance comparable to black-box RL while providing interpretable action rationales. Operator review is incorporated to approve, reject, or modify dispatch recommendations before execution. The framework bridges autonomous RL control and trustworthy grid-edge operation. It provides an algorithmic blueprint for human-centered demand response dispatch.

Generative AI, including generative adversarial networks, diffusion models, variational approaches, and large language models, is increasingly being explored for smart-grid operation. Its appeal lies in its ability to create synthetic data, expand scenario sets, and support human operators facing complex and uncertain grid conditions. This review critically examines the state of generative AI in four key smart-grid applications: synthetic load profiles, contingency simulation, human-in-the-loop dispatch, and associated governance risks. It evaluates not only technical capability but also reliability, accountability, privacy, and operational readiness. A critical review of 28 peer-reviewed articles published between 2017 and 2026 is conducted. The selected literature is synthesized across methodological contributions, application domains, evaluation practices, and governance concerns. Generative AI shows promise in creating realistic load data and expanding contingency sets, but reliability, interpretability, and governance frameworks remain underdeveloped. Human-in-the-loop dispatch integration is still nascent, and most studies remain closer to proof-of-concept than operational deployment. While generative AI can enhance grid flexibility, simulation richness, and planning capability, its current maturity is uneven. Substantial research is needed to address technical, ethical, and regulatory challenges before these systems can be responsibly embedded in smart-grid operation.