Abstract:
The economic power dispatch is used to finding an optimal distribution of system load to minimize the total generation cost while meeting all the system constraints without considering the pollution. Due to environmental regulations and social awareness act, the emission is to be minimized. These two major functions are conflicting in nature and both have to be considered simultaneously to evaluate the overall optimal dispatch. The economic and emission dispatch (EED) is a multi-objective optimization problem (MOP) with contradictory objectives. The various conventional methods like Newton’s method,
Lambda-iteration method and gradient method are used to solve the environmental and economic dispatch problem but results are near to optimal solution and are not exact because more computational time. To overcome this problem, the mathematical solutions for the combined economic and emission dispatch using Pareto optimal front approach are developed and applied to the test system and also verify the results using MATLAB environment. . The developed algorithm is tested for a three-unit and a six-unit system. The results demonstrate the capabilities of the proposed approach to generate well distributed Pareto optimal solutions of the multi-objective CEED problem in a single run. Combined Economic Emission Dispatch (CEED) problem. CEED is a multi-objective optimization problem by considering the fuel cost and emission as the objectives. It is converted into single objective optimization problem using weighted sum method. power plants have different fuel costs and are not the same distance from the load centers. Hence the need for developing improved methods of economic dispatch of generated power from mostly remote locations to major load centers in the urban cities. Most methods adopted for optimal dispatch are either cumbersome in their computational approaches. This work proposes a fast and easy to use generic MATLAB syntax to aid in solving economic dispatch problems. The software component proposed in this work will try to estimate the optimal value of real power to be generated with the least possible fuel cost.
