An Advanced Genetic Optimization Algorithm to Solve Combined Economic and Emission Dispatch Problem

The dispatch of electric load is one of the key functions in electrical power system operation, management and planning. The key intention of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulfilled. In the present time, energy resources to generate mechanical power supplied to the rotor shaft of generating units are of fossil fuels. This leads to the emission of huge amount of carbon dioxide (CO2), sulfur dioxide (SO2) and nitrogen oxides (NOx) that results in atmospheric pollution. Reducing those pollutions resulted by usage of fossil-fired generating units has received great consideration. This provides wide field for the researchers to develop a better system to handle those needs. This leads to the development of Combined Economic and Emission Dispatch (CEED) techniques. There are various technique proposed by several researchers to solve CEED problem based on optimization techniques. The efficient optimization technique among the proposed work is Genetic Algorithm (GA). But still some problems like slower convergence and higher computational complexity exists in using GA for solving CEED problem. To overcome those difficulties, this paper uses Non- Dominated Ranked Genetic Algorithm (NRGA) which uses rank based Roulette Wheel selection algorithm with Pareto-based population ranking Algorithm. The simulation result shows that the proposed technique for solving combined economic and emission dispatch problem results in better convergence rate when compared to the existing techniques.