In order to comprehensively consider the impact of carbon emissions trading on wind-fire co-generation model, and based on the constraints of energy saving and emission reduction, power generation efficiency and unit operation, the carbon emission trading cost function is introduced. A multi-objective Dynamic Environmental Economic Dispatch (DEED) model of wind-fire combined power system considering generation cost, carbon trading cost and environmental cost is constructed. A multi-objective adaptive particle swarm optimization algorithm is proposed to solve the DEED problem. According to the current fitness function of particles in the optimization process, the inertia weight and learning factor are adaptively modified to further improve the premature defects and enhance the global search ability. The simulation results of 10-machine power systems with wind farms show that the proposed method can simultaneously optimize the two conflicting objectives of cost and emission, and obtain a wider and more uniform Pareto frontier than other algorithms, which can effectively reduce the carbon emissions and integrated operation costs of the combined generation system. This work is supported by National Natural Science Foundation of China (No. 51667018 and No. 51367015).