Unit Commitment and Economic Load Dispatch Using Hybrid Genetic - Particle Swarm Optimization Algorithm

Abstract

Unit commitment and Economic load dispatch is considered one of the most important problems in power systems that optimize the operation cost with respect to the load demands of customers. Several strategies have been proposed to provide quality solutions to these problems and increase the potential saving in the power system operation. These include deterministic and stochastic search algorithms. One of the limitations of deterministic approaches is that they suffer from the curse of dimensionality when dealing with the modern power system with large number of generators. Recently evolutionary based search techniques are popularly applied to these problems which may handle complex non-linear constraints and provide high quality solution. This paper proposes a solution for unit commitment and economic load dispatch problem using hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental research shows lower operating cost and execution time when compared to several state-of-the-art techniques. The proposed system was tested on the seven unit Neyveli thermal power station system data. The algorithm was developed and executed using the C++ and MATLAB 7.1 software. The simulations were carried out on a PC with INTEL DUO CORE CPU 1.8 GHz and 512MB RAM.