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Economic Load Dispatch (ELD) problem is one of the most important problems to be solved in the operation and planning of a power system. The main objective of the economic load dispatch problem is to determine the optimal schedule of output powers of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This study presents an application of Genetic Algorithm (GA) for solving the ELD problem to find the global or near global optimum dispatch solution. The proposed approach has been evaluated on 26-bus, 6-unit system with considering the generator constraints, ramp rate limits and transmission line losses. The obtained results of the proposed method are compared with those obtained from the Conventional Lambda Iteration Method and Particle Swarm Optimization (PSO) Technique. The results show that the proposed approach is feasible and efficient.
In this study, a Genetic Algorithm (GA) and particle swarm
optimization with constriction factor (CFPSO) are proposed for solving the short
term variable head hydrothermal scheduling problem with transmission line losses.
The performance efficiency of the proposed techniques is demonstrated on hydrothermal
test system comprising of two thermal units and two hydro power plants. The
simulation results obtained from the constriction factor based particle swarm
optimization technique are compared with the outcomes obtained from the genetic
algorithm to reveal the validity and verify the feasibility of the proposed
methods. The test results show that the particle swarm optimization give the
same solution as obtained by genetic algorithm but the computation time of the
constriction factor particle swarm optimization technique is less than genetic
algorithm.
