Kalman-Particle Filter Used for Particle Swarm Optimization of Economic Dispatch Problem

Abstract

This paper presents an effective evolutionary method to solve the Economic Dispatch (ED) problem with units having prohibited operating zones. The Kalman filter is an efficient recursive filter that estimates the state of a dynamic system from a series of noisy measurements in the Total Power Generation (TPG). ED is an example of a dynamic system algorithm that has been widely used for determination most economical generation profile to optimize the overall electricity prices. ED is a non-smooth problem when valve-point effects of generation units are considered. This paper applies Kalman - Particle filter (KF-PF) to the ED state estimation problem that has been optimized with Particle Swarm Optimization (PSO), with the emphasis to avoid the solution being trapped in local optimas [1], [2]. Kalman and particle filter are used to estimate TPG as state of ED problem. The performance of the KF-PF has been tested on a typical system and compared with others proposed in the literatures. The comparison results show that the efficiency of proposed approach can reach higher quality solutions.