In recent years, the awareness of environmental protection has made the power dispatch model no longer purely economical-oriented. This thesis proposed the application of particle swarm optimization (PSO) algorithm and interactive compromise programming method to solve the 24-hour two-objective power dispatch problem. Considering simultaneously the lowest generating cost and the lowest pollution emission, the two mutually-conflicting objectives will choose a compromised dispatch model. This thesis joined the mixed-integer programming problem of optimal power flow (MIOPF) with the dynamic economic dispatch (DED), making this dispatch solution more realistic without electrical violations; The MIOPF considers both continuous and discrete types of variables. The continuous variables are the generating unit real power output and the generator-bus voltage magnitudes; the discrete variables are the shunt capacitor banks and transformer tap setting. Simulation were run on the standard IEEE 30 Bus system. In order to avoid the PSO local optimality problem, this thesis proposed the utilization of the PSO algorithm with time-varying acceleration coefficients (PSO_TVAC) plus the local random search method (LRS), so it can quickly and effectively reach the optimal solution, without advantages of performance and accuracy of PSO. This thesis also proposed the consideration of the available transfer capability (ATC) on transmission lines of the existing dispatch model. Applying sensitivity factors to calculate each generator¡¦s available transfer capability that can be offered in the analyzed time interval, enables the creation of a new constraint. Joined with the dynamic economic dispatch problem, it will make possible that a load client wishes to raise its demand. Simultaneously taking care of the minimum cost and the limits of system security, better dispatch results could be expected.