International Journal of Computer Applications (0975 – 8887) Volume 31– No.11, October 2011 58 Economic Emission Load Dispatch by Modified Shuffled Frog Leaping Algorithm A. Srinivasa Reddy Department of Electrical and Electronics Engineering Sir C. R. Reddy College of Engineering, Vatluru, Eluru-534007 Andhra Pradesh, INDIA K. Vaisakh Department of Electrical Engineering, A.U. College of Engineering, Andhra University Visakhapatnam-530003 Andhra Pradesh, INDIA ABSTRACT This paper presents a newly developed optimization approach involving a modified shuffled frog leaping algorithm (MSFLA) applied for the solution of the economic emission load dispatch (EELD) problem. The approach utilizes the local search strategies for searching global solution. MSFLA is developed on the same frame work of shuffled frog leaping algorithm (SFLA). In this proposed algorithm, a search-acceleration parameter is introduced. To obtain the best compromising solution a pareto– optimal decision making approach is applied to a standard IEEE 30-bus six generator test system. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods performed. The quality and usefulness of the proposed algorithm are demonstrated through its application to a standard test system in comparison with the other existing techniques. The current proposal was found to be better than, or at least comparable to them considering the quality of the solutions obtained. The MSFLA algorithm appears to be a robust and reliable optimization algorithm for the solution of the power system problems. Keywords Economic emission load dispatch, modified shuffled frog leaping algorithm, memetic algorithm, multi-objective optimization. 1. INTRODUCTION The economic emission load dispatch (EELD) is a nonlinear multi-objective optimization problem and is basically used to generate optimal amount of generating power from the fossil fuel based generating units. The objective of EELD problem is the minimization of the fuel cost and emission level simultaneously, by satisfying all unit and system constraints. The increased concern over environmental protection forced the utilities to operate the units for generation of electrical power not only at minimum generation cost but, also at minimum emission level [1-2]. The cost and emission objectives are non- commensurable and the minimization of cost of generation will not provide minimum pollution level and the minimization of emission does not provide minimum cost of generation. Various techniques have been proposed to solve this multi- objective optimization problem emphasizing the reduction in the atmospheric emissions [1-2]. In past decades, the EELD problem was converted to a single objective problem by linear combination of different objectives as a weighted sum [3-4]. The important aspect of this weighted sum method is that a set of pareto-optimal solutions can be obtained by varying the weight factor. This method can be applied to the problems having a convex pareto-optimal front. The ϵ-constraint method was presented in [5-6] for EELD problem. This method optimizes the most preferred objective and considers the other objectives as constraints bounded by some allowable levels ϵ. Unfortunately, this method is time-consuming and finds weakly non-dominated solutions. The economic emission load dispatch (EELD) problem has been handled as a multi-objective optimization problem with non- commensurable and contradictory objectives. In [7] the formulation of the problem has been reduced to a single objective problem by treating the emission as a constraint. This formulation, however, has a severe difficulty in getting the trade-off relations between cost of generation and emission. The goal-programming techniques and a classical technique based on coordination equations are used to minimize the total cost of generation and pollution control simultaneously with varying degrees of compromise in [8-9]. In [10] a linear programming approach in which the objectives are considered one at a time was presented with mathematical assumptions to simplify the problem. Unfortunately, these conventional optimization methods that make use of derivatives and gradients, in general, are not able to identify the global optimum. Recently, the studies on evolutionary algorithms have shown that these methods can be efficiently used to EELD to provide better results [11–13]. An evolutionary algorithm based approach evaluating the economic impacts of environmental dispatching and fuel switching was presented in [14]. However, some non-dominated solutions may be lost during the search process while some dominated solutions may be misclassified as non-dominated ones due to the selection process adopted. A multi-objective stochastic search technique for the EELD problem was presented in [15]. However, the technique is computationally involved and time-consuming. In [16] differential evolution (DE) method is applied to solve economic and emission load dispatch by considering emissions either as constraints or as a second objective function of a multi-objective optimization problem.