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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.

Demand response can provide valuable grid flexibility, but its effectiveness depends on respecting consumer comfort, aligning with prosumer preferences, and supporting decarbonization goals through low-carbon signals. Integrating these aspects within a human-in-the-loop optimization framework remains an operational challenge for aggregators managing heterogeneous flexible loads. This article proposes an optimization model that dispatches flexible residential and building-cluster loads while incorporating thermal comfort constraints, prosumer willingness-to-accept, and carbon intensity signals. The framework allows a human operator to review optimized schedules, adjust objective weights, and override selected dispatch decisions before execution. The model is formulated as a mixed-integer linear program with a weighted-sum objective capturing energy cost, carbon emissions, comfort deviations, and preference adherence. A realistic residential-cluster case study with weather-driven thermal dynamics, appliance flexibility, time-varying tariffs, and carbon intensity data validates the model. The proposed dispatch reduces feeder peak demand by 18.6% and carbon emissions by 11.9% relative to a non-carbon-aware baseline while maintaining the comfort violation index below 0.035 degree-hours per occupied interval. Operator overrides occur in 6.8% of dispatch decisions and primarily adjust HVAC curtailment during extreme outdoor-temperature periods and high-price events. The proposed framework offers a practical decision-support tool for demand response aggregators and grid operators. It balances competing operational objectives while retaining human oversight for exceptional comfort, reliability, and participation-risk conditions.