A Hybrid EP-PSO-SQP Algorithm for Dynamic Dispatch Considering Prohibited Operating Zones

Abstract

This article presents hybrid evolutionary programming (EP), particle swarm optimization (PSO), and sequential quadratic programming (SQP) methods to solve the dynamic economic dispatch problem (DEDP) of generating units considering non-convex features. The non-convex feature considered is the valve-point effects, which is modeled in two different representations in the DEDP formulation. The proposed method is a two-phase optimizer. In the first phase, the candidates are treated by both the EP and PSO techniques to explore the solution space freely. In the second phase, the SQP method will be invoked when there is an improvement of solution (a feasible solution) in the first phase of the run. This hybrid optimization mechanism leads a better performance of the solution algorithm to effectively search the complex solution space. To validate the effectiveness of the proposed method, several non-convex DEDP test systems are studied and shown in general.