Learning operational compatibility in distribution networks under multi-step renewable dynamics

High penetration of renewable generation introduces rapid variability and multi-timescale uncertainty into distribution network operations, making real-time feasibility assessment increasingly difficult for traditional optimization-based dispatch frameworks. This paper proposes a learning-based operational compatibility framework that identifies whether candidate dispatch states remain feasible under Multi-Step renewable dynamics. The proposed method constructs a compatibility learning model that maps system states, renewable forecasts across multiple time horizons, and network operating conditions to a probabilistic compatibility score representing the likelihood that voltage, line flow, and power balance constraints are satisfied. A Multi-Step renewable dynamics representation is introduced to capture correlated variability across short-term and intra-day forecasting intervals, enabling the model to anticipate feasibility degradation caused by forecast uncertainty propagation. The learning architecture integrates network structural information and operational features to approximate the feasible operating manifold without repeatedly solving computationally intensive AC power flow problems. Extensive simulations on modified IEEE distribution feeders with high renewable penetration demonstrate that the proposed framework can correctly identify feasible operating conditions in approximately 94.6% of scenarios while reducing feasibility evaluation time by over 82% compared with conventional AC-OPF screening procedures. Under severe renewable fluctuation conditions, the method maintains a compatibility prediction accuracy exceeding 90%, allowing operators to rapidly filter infeasible dispatch candidates and maintain secure network operation across multiple forecast horizons.