This paper establishes a probabilistic scenario-based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED-UC) problem, by considering wind power integration. The scenario generation and reduction method are implemented to describe wind power uncertainty. Accordingly, each wind power scenario is analyzed separately to determine the on/off status of the units. As for a predetermined significance level, the UC scheduling solution can be obtained with a probabilistic point of view, considering all the original scenarios. Then the SDEED problem is converted into a number of deterministic scheduling problems. For each scenario in the reduced set, an enhanced multiobjective particle swarm optimization algorithm is proposed to produce the Pareto optimal solutions. The practicability and performance of the proposed approach are illustrated through a case study, and the results are compared with the existing multiobjective evolutionary algorithms.