<p>A meta-heuristic based optimization method for solving combined economic emission dispatch (CEED) problem for the power system with thermal and wind energy generating units is proposed in this paper. Wind energy is environmentally friendly and abundantly available, but the intermittency and variability of wind power affects the system operation. Therefore, the system operator (SO) must aware of wind forecast uncertainty and dispatch the wind power accordingly. Here, the CEED problem is solved by including the nonlinear characteristics of thermal generators, and the stochastic behavior of wind generators. The stochastic nature of wind generators is handled by using probability distribution analysis. The purpose of this CEED problem is to optimize fuel cost and emission levels simultaneously. The proposed problem is changed into a single objective optimization problem by using weighted sum approach. The proposed problem is solved by using particle swarm optimization (PSO) algorithm. The feasibility of proposed methodology is demonstrated on six generator power system, and the obtained results using the PSO approach are compared with results obtained from genetic algorithm (GA) and enhanced genetic algorithms (EGA).</p>
Solving combined economic emission dispatch problem in wind integrated power systems