Carbon dioxide (CO2) is the most important component of Greenhouse Gas (GHG) that causes global warming and sea-level rising. Thermal power plants dominate electric power generation in the world, and has been reported to be the major contributor of CO2 emission. To prevent the related global warming caused by GHG emission, carbon quota trading is implemented and becomes a gradually arising market. This thesis proposed a research focused on the relationship between the carbon trading scheme and dynamic economic dispatch (DED) problem for the public utility. A model of the carbon trading market was investigated and introduced into DED problem incorporating wind and solar power plant. A refined particle swarm optimization (PSO) algorithm, PSO with time-varying acceleration coefficients (PSO-TVAC), is applied to determine the DED strategy with the incorporation of independent power providers (IPPs) and green power plant. The model of the carbon trading was considered in the DED problem. Carbon reduction is treated as the inner-cost of utility, and the fictitious carbon quotas can be resold to the market, while the energy shortage can be satisfied by purchasing quotas from the market. In order to avoid premature convergence of the original PSO, the PSO-TVAC method is introduced to improve the searching efficiency.