Dynamic economic emission dispatch incorporating wind farms using modified co-evolutionary particle swarm optimization meta-heuristic algorithm

The dynamic economic load dispatch is one of the main problems of power systems generation and operation. The objective is to schedule power generation for units over a certain period of time، while satisfying operating constraints and load demand in each interval. Wind farms، as renewable energy resources are playing an increasing role in electricity generation. In this paper، a computational framework is presented to solve the dynamic economic emission dispatch problem with inclusion of wind farms considering their associated constraints. An optimization algorithm called modified co-evolutionary particle swarm optimization (MCPSO) is proposed to solve the problem. In the proposed algorithm، two kinds of swarms evolve interactively where one of them is used to calculate the penalty factors (constraints handling) and the other is used for searching good solutions (optimization process). In addition، some modifications such as using an inertia weight that decreases linearly during the simulation are made to improve the performance of the algorithm. Finally، the validity and superiority of the proposed method are demonstrated by simulation results on a modified IEEE benchmark system including six thermal units and two wind farms.