Studies in Informatics and Control, Vol. 22, No. 2, June 2013 http://www.sic.ici.ro 113 1. Introduction The problem of Economic environmental dispatch (EED) is an important optimization task in fossil fuel fired power plant operation for allocating generation among the committed units. It aims at optimizing two conflicted objectives of fuel cost and emission level, simultaneously while satisfying all operational system constraints [1-3]. The EED problem is a large-scale highly non- linear constrained optimization problem characterized by complex and nonlinear with heavy equality and inequality constraints characteristics [4]. Traditionally, electric power systems aim at operating in such a way that the total fuel cost is minimized regardless of the emission produced in the system. An increased public awareness regarding the harmful effects of atmospheric pollutants on the environment has been noticed with concentrating on the importance of environmental protection and the passage of the Clean Air Act Amendments of 1990 has forced the utilities to adapt their design and operational strategies in order to reduce pollution and atmospheric emissions of the thermal power plants [5]. Many algorithms are developed to alleviate the effects of emission as installation of pollutant cleaning equipment, switching to low emission fuels, replacement of the aged fuel-burners with cleaner ones, and emission dispatching. The fourth option is the recent interested costless option compared to the first three options. That option is not any installing or modifying the exited pollution equipment. Then, the problem that has attracted much attention is pollution minimization due to the pressing public demand for clean air [4-6]. As the concern of environmental pollution has been increased in recent decades as well as the dramatic growing of fuel costs assure the continuous necessity of improvement of optimization methodologies for efficiently solving EED problems. Classical methods such as the lambda iteration method and gradient method have been applied to solve the EED problems. But unfortunately, these methods are not feasible in practical power systems owing to the non-linear characteristics of the generators and non- smooth cost functions. Consequently, many powerful mathematical optimization techniques that are fast and reliable, such as non-linear programming and dynamic programming have been employed to solve the EED problems. But due to the non-differential and non-convex characteristics of the cost functions, these methods are also unable to locate the global optima [1, 3]. Multiobjective Real-Coded Genetic Algorithm for Economic/Environmental Dispatch Problem Ragab A. El-SEHIEMY 1 , Mostafa Abdelkhalik El-HOSSEINI 2 , Aboul Ella HASSANIEN 3 1 Electrical Engineering Department, Faculty of Engineering-Kafrelsheikh University elsehiemy@eng.kfs.edu.eg 2 Computers and Systems Engineering Department, Faculty of Engineering- Mansoura University melhosseini@eng.mans.edu.eg 3 Faculty of Computers & Information, Cairo University aboitcairo@fci-cu.edu.eg Abstract: This paper outlines the optimization problem of nonlinear constrained multi-objective economic/environmental dispatch (EED) problems of thermal generators in power systems and presents novel improved real-coded genetic optimization (MO-RCGA) algorithm for solving EED problems. The considered problem minimizes environmental emission and non-smooth fuel cost simultaneously while fulfilling the system operating constraints. The proposed MO- RCGA technique evolves a multi-objective version of GA by proposing redefinition of global best and local best individuals in multi-objective optimization domain. The performance of the proposed MO-RCGA enhanced with biased Initialization, dynamic parameter setting, and elitism is carried out. The validity and effectiveness of the proposed MO- RCGA is verified by means of several optimization runs accomplished at different population sizes on standard IEEE 30- bus test system. Simulation results demonstrated the capabilities of the proposed MO-RCGA algorithm to obtain feasible set of effective well-distributed solutions. Keywords: Multiobjective real-coded genetic algorithm, economic environmental dispatch (EED), security.