Particle Swarm Optimization for Solving Combined Economic and Emission Dispatch Problems T. RATNIYOMCHAI, A. OONSIVILAI, P. PAO-LA-OR, and T. KULWORAWANICHPONG Power System Research Unit, School of Electrical Engineering Suranaree University of Technology 111 University Avenue, Suranaree District, Nakhon Ratchasima THAILAND thanatchai@gmail.com Abstract: - This paper presents a demonstration of solving combined economic and emission dispatch problems. The objective of the combined problem can be expressed by taking both the fuel cost and total emission into account with required constraints. Among potential intelligent search methods, particle swarm optimization is well-known and widely-used in solving economic load dispatch. In this paper, the particle swarm optimization is exploited to demonstrate its use. A three-unit thermal power plant is situated for test. Sets of suitable dispatch with respect to economic or emission objectives can be efficiently found. Key-Words: - Economic dispatch, emission dispatch, fuel cost function, total emission function, particle swarm optimization 1 Introduction Economic load dispatch is one of the main functions in electrical power system operation, management and planning [1,2]. Security, reliability, economy and stability are characterized and included to form the dispatching objective or constraints in various forms. Typically, the main objective of economic load dispatch is to minimize the total production cost of the generating system while the required equality and inequality constraints must be satisfied. Nowadays, energy sources to produce mechanical power applied to the rotor shaft of generating units are of fossil fuels. This can cause a vast amount of carbon dioxide (CO2), sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions in which atmospheric pollution is created [3]. Emission control over environmental pollution caused by fossil-fired generating units and the enforcement of environmental regulations [4] has received careful attention. May research work in generation allocation of thermal power plants have emphasized the essence of pollution control in electrical power systems [4-14]. However, taking only the operation of minimum environmental impact is impractical due to causing the higher production cost of the system. On the other hand, to operate the generating system with the minimum of total production cost is not met the emission requirement. Therefore, economic dispatch, emission dispatch or combined economic and emission dispatch is somehow chosen individually or merged all together. To find the appropriate solution to this question, a good power management strategy is set. Several optimization techniques such as lambda iteration, linear programming, non-linear programming, quadratic programming, interior point method or even intelligent search methods (e.g. genetic algorithm, evolutionary programming, particle swarm optimization, etc [1,3,6,13]) are employed for solving the various economic dispatch problems and also the unit commitment problems [15]. The solution of economic dispatch problems using genetic algorithm required a large number of generations when the power generating system has the large number of units. Combined economic and emission dispatch has been proposed in the field of power generation dispatch, which simultaneously minimizes both fuel cost and total emissions. When the emission is minimized the fuel cost may be unacceptably high or when the fuel cost is minimized the emission may be high. In literature as environmental economic dispatch or emission dispatch, many algorithms are used to solve such a problem. A cooling mutation technique in EP algorithm to solve CEED problem for nine units system was proposed [18]. However, [19] showed that particle swarm optimization is superior to those intelligent search techniques mentioned previously. RECENT ADVANCES in ENERGY & ENVIRONMENT ISSN: 1790-5095 211 ISBN: 978-960-474-159-5