An intelligent approach for cost minimization of power generation

Cost reduction is one of the main targets in power industry due to economic load dispatch problem and allocating loads to plants for minimum cost. The principal objective in economic dispatch of thermal generators in a power system is to determine the economic loadings of the generators so that the load demand can be met and the loadings are within the feasible operating regions of the generators. This study presents an optimization approach for fuel cost and power loss minimization based on genetic algorithm and particle swarm optimization methods. To demonstrate the global optimization power of the presented techniques, these methods are applied to the IEEE 30 bus test system with highly non-linear generator input/output cost curves and the results compared to those obtained using OPF method based on mathematical programming approaches. The results demonstrate that PSO and GA method show great promise for minimum cost when it contains highly non-linear devices.