Abstract—This paper presents the applications of computational intelligence techniques to economic load dispatch problems. The fuel cost equation of a thermal plant is generally expressed as continuous quadratic equation. In real situations the fuel cost equations can be discontinuous. In view of the above, both continuous and discontinuous fuel cost equations are considered in the present paper. First, genetic algorithm optimization technique is applied to a 6- generator 26-bus test system having continuous fuel cost equations. Results are compared to conventional quadratic programming method to show the superiority of the proposed computational intelligence technique. Further, a 10-generator system each with three fuel options distributed in three areas is considered and particle swarm optimization algorithm is employed to minimize the cost of generation. To show the superiority of the proposed approach, the results are compared with other published methods. Keywords—Economic Load Dispatch, Continuous Fuel Cost, Quadratic Programming, Real-Coded Genetic Algorithm, Discontinuous Fuel Cost, Particle Swarm Optimization. I. INTRODUCTION COMONIC load dispatch is defined as the process of allocating generation levels to the generating units in the mix, so that the system load is supplied entirely and most economically [1]. The objective of the economic dispatch problem is to calculate the output power of every generating unit so that all demands are satisfied at minimum cost, while satisfying different technical constraints of the network and the generators. In this problem, the generation costs are represented as curves and the overall calculation minimizes the operating cost by finding the point where the total output of the generators equals the total power that must be delivered. It is an important daily optimization task in the operation of a power system [2]. _______________________________________ S. Swain is working as an Assistant Professor in the Electrical Engineering Department, School of Technology, KIIT University, Bhubaneswar, Orissa, India (e-mail:scs_132@rediffmail.com). S. Panda is working as a Professor in the Department of Electrical and Electronics Engineering, NIST, Berhampur, Orissa, India, Pin: 761008. (e- mail: panda_sidhartha@rediffmail.com ) A.K. Mohanty is working as a Professor Emeritus in the Electrical Engineering Department, School of Technology, KIIT University, Bhubaneswar, Orissa, India, (e-mail: dhisi1@rediffmail.com).. C. Ardil is with National Academy of Aviation, AZ1045, Baku, Azerbaijan, Bina, 25th km, NAA (e-mail: cemalardil@gmail.com). Several optimization techniques have been applied to solve the ED problem. To solve economic dispatch problem effectively, most algorithms require the incremental cost curves to be of monotonically smooth increasing nature and continuous [3-6]. For the generating units, which actually having non-monotonically incremental cost curves, the conventional method ignores or flattens out the portions of the incremental cost curve that are not continuous or monotonically increasing. Hence, inaccurate dispatch result may be obtained. To obtain accurate dispatch results, the approaches without restriction on the shape of fuel cost functions are necessary [7-8]. Most of conventional methods suffer from the convergence problem, and always get trap in the local minimum. Moreover, some techniques face the dimensionality problem especially when solving the large- scale system. In recent years, one of the most promising research fields has been “Evolutionary Techniques”, an area utilizing analogies with nature or social systems. Evolutionary techniques are finding popularity within research community as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces applied to non-differentiable objective functions. Several modern heuristic tools have evolved in the last two decades that facilitate solving optimization problems that were previously difficult or impossible to solve. These tools include evolutionary computation, simulated annealing, tabu search, particle swarm, etc. Recently, genetic algorithm (GA) and particle swarm optimization (PSO) techniques appeared as promising algorithms for handling the optimization problems [9]. These techniques are finding popularity within research community as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces applied to non-differentiable cost functions. Genetic Algorithm (GA) can be viewed as a general- purpose search method, an optimization method, or a learning mechanism, based loosely on Darwinian principles of biological evolution, reproduction and ‘‘the survival of the fittest’’ [10]. GA maintains a set of candidate solutions called population and repeatedly modifies them. At each step, the GA selects individuals from the current population to be parents and uses them to produce the children for the next generation. In general, the fittest individuals of any population tend to reproduce and survive to the next generation, thus improving successive generations. However, inferior individuals can, by chance, survive and also reproduce. GA is well suited to and has been extensively applied to solve complex design optimization problems because it can handle Application of Computational Intelligence Techniques for Economic Load Dispatch S.C. Swain, S. Panda, A.K. Mohanty, C. Ardil E World Academy of Science, Engineering and Technology International Journal of Electrical and Computer Engineering Vol:4, No:3, 2010 582 International Scholarly and Scientific Research & Innovation 4(3) 2010 ISNI:0000000091950263 Open Science Index, Electrical and Computer Engineering Vol:4, No:3, 2010 publications.waset.org/2222/pdf