Particle Swarm Optimization (PSO) Technique for Unit Commitment and Economic Dispatch Problem Solution

Vol.2 No.2

Year: 2013

Issue: Mar-May

Title : Particle Swarm Optimization (PSO) Technique for Unit Commitment and Economic Dispatch Problem Solution

Author Name : Shekhappa Ankaliki, Dr. A. D. Kulkarni , Sujata I. Katti

Synopsis :

This paper presents a new improved particle swarm optimization (PSO) algorithm based technique for solving the unit commitment and economic dispatch problem. The economic dispatch problem is solved by various traditional and non traditional techniques. The proposed algorithm is much simpler and efficient than traditional methods and is used to solve sample systems consisting up to 10 units and the results obtained are compared with Genetic Algorithm  method in terms of quality of solution and computational efficiency and the convergence performance is presented. Particle swarm optimization (PSO) is a population based stochastic optimization technique which can be applied for almost all non-linear complex optimization in power system. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and non-smooth cost functions are considered using the proposed method in practical generator operation. It was inspired by the social behavior of organism such as bird flocking and fish schooling and it utilizes a population based search procedure. According to the research results, birds find food by flocking. This assumption is a basic concept of PSO. In PSO each solution is a particle in search space. Each particle adjusts its flying speed according to its own flying experience and its companion’s flying experience. All the particles have fitness values, which are evaluated by the fitness function to be optimized and have velocity, which direct the flying of particles. So initialized with a group of random particles search for optima by updating generations. Improved unit commitment schedules may save the electrical utilities of  crores of rupees per year in production cost. To illustrate the proposed approach, 10-unit system is considered and results are compared with Genetic Algorithm.