Bioinfo Publications 9 UNIT COMMITMENT USING PARTICLE SWARM OPTIMIZATION BIOINFO Computational Optimization ISSN: 2249-5533 & E-ISSN: 2249-5541, Volume 2, Issue 1, 2012, pp.-09-16 Available online at http://www.bioinfo.in/contents.php?id=265 VINOD PURI 1 , NITIN NARANG 2 , JAIN S.K. 3 AND CHAUHAN Y.K. 4 1 Department of Electrical and Electronics Engineering, SRM University, NCR Campus, Ghaziabad, India. 2 Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India. 3 Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India. 4 School of Engineering, Gauttam Buddha University, Greater Noida, Uttar Pradesh, India.. *Corresponding Author: Email- vinod_tu24@yahoo.co Received: February 13, 2012; Accepted: March 09, 2012 Abstract- An important criterion in power system operation is to meet the power demand at minimum fuel cost using an optimal mix of differ- ent power plants. Moreover, in order to supply electric power to customers in a secured and economic manner, thermal unit commitment is considered to be one of the best available options. It is thus recognized that the optimal unit commitment of thermal systems results in a great saving for electric utilities. Unit Commitment is the problem of determining the schedule of generating units subject to device and oper- ating constraints. The formulation of unit commitment has been discussed and the solution is obtained by classical dynamic programming method. An algorithm based on Particle Swarm Optimization technique, which is a population based global search and optimization tech- nique, has been developed to solve the unit commitment problem. The effectiveness of these algorithms has been tested on systems com- prising three units and four units and compared for total operating cost. Key words- Unit commitment, dynamic Programming, PSO. BIOINFO Computational Optimization ISSN: 2249-5533 & E-ISSN: 2249-5541, Volume 2, Issue 1, 2012 Introduction Unit commitment (UC) is a nonlinear mixed integer optimization problem to schedule the operation of the generating units at mini- mum operating cost while satisfying the demand and other equali- ty and inequality constrains. Several solution strategies have been proposed to provide quality solutions to the UC problem and in- crease the potential savings of the power system operation. These include deterministic and stochastic search approaches. Deterministic approaches include the priority list method, dynamic programming, Lagrangian Relaxation and the branch and- bound methods. Although these methods are simple and fast, they suffer from numerical convergence and solution quality problems. The stochastic search algorithms such as particle swarm optimization, genetic algorithms, evolutionary programming, simulated anneal- ing, ant colony optimization and tabu search are able to overcome the shortcomings of traditional optimization techniques. These methods can handle complex nonlinear constraints and provide high quality solutions. This formulation drastically reduces the number of decision variables and hence can overcome the short- comings of stochastic search algorithms for UC problems. Due to simplicity and less parameter tuning, particle swarm optimization is used for solving the unit commitment problem. In this thesis we have to study the algorithm of particle Swarm optimization and formulate the algorithm for solving unit commitment using PSO. In the results we have to find the variation in the results of total oper- ating cost of the system in the given time horizon and compare it with the results of the already existing method like dynamic pro- gramming. Formulation of unit commitment problem The objective of the UC problem is to minimize the total operating costs subjected to a set of system and unit constraints over the scheduling horizon. It is assumed that the production cost, PCi for unit ‘i’ at any given time interval is a quadratic function of the gen- Citation: Vinod Puri, et al (2012) Unit Commitment Using Particle Swarm Optimization. BIOINFO Computational Optimization, ISSN: 2249- 5533 & E-ISSN: 2249-5541, Volume 2, Issue 1, pp.-09-16. Copyright: Copyright©2012 Vinod Puri, et al. This is an open-access article distributed under the terms of the Creative Commons Attribu- tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cred- ited.