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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.