Based on the analysis on the uncertain nature of wind power output, the Weibull distribution parameters of regional wind speed at different periods are calculated to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of the wind power. The spinning reserve (SR) requirements for wind power incorporation are determined according to the obtained PDF and CDF, and by converting the wind power into a chance constrained form, a model for dynamic economic emission dispatch with wind power is constructed. A hybrid multi‐objective algorithm that integrates differential evolution (DE) and particle swarm optimisation (PSO) algorithm is put forward to solve the proposed model. The algorithm is implemented based on the Pareto dominance theory and a dynamic external archive set, and fully exploits the advantages of DE and PSO. An improved calculation method of crowding distance and Pareto solution set reduction rule are also employed to enhance the performance of the proposed algorithm. Also, three performance indicators are introduced to evaluate the performance of the algorithm. Two distinct test systems are performed to verify the proposed model and algorithm, and the results show that they are effective and reasonable.