A self-adaptive differential evolution algorithm for dynamic economic dispatch with valve-point effects

In this paper, a self-adaptive differential evolution algorithm (SaDEA) is proposed for solving dynamic economic dispatch (DED) problem with valve-point effects consideration. The purpose of DED problem is to minimize the total generation costs of thermal power plants associated with the technical and economical constraints. The software development has been performed within the mathematical programming environment of MATLAB in this work. The efficiency and effectiveness of the proposed technique is initially demonstrated via the analysis of 3-unit and 10-unit test systems considering valve-point loading and ramp rate constraints. A detailed comparative study among an evolutionary programming (EP), a particle swarm optimization (PSO), an enhanced particle swarm optimization (EPSO), an enhanced particle swarm optimization with Gaussian mutation (EPSO-GM), a hybrid method between evolutionary programming and sequential quadratic programming (EP-SQP), a modified hybrid EP-SQP (MHEP-SQP) and the proposed method is presented. From the experimental results, the proposed method has the achieved solutions with good accuracy, stable convergence characteristics, simple implementation and satisfactory computational time.