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The simple genetic algorithm is introduced at first. On the basis of many strategies to selector operator, crossover operator and mutation operator, the advanced genetic algorithm sufficiently considers the global optimum by generating new individuals in each generation, which guarantees the population's multiplicity and accelerates the evolved speed. The optimal design of permanent magnet generator...
Ant Colony Optimization (ACO) is more suitable for combinatorial optimization problems. This paper proposes Genetic Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints,...
Microgrids are low voltage intelligent distribution networks comprising various distributed generators, storage devices and controllable loads which can be operated as interconnected or as islanded system. The optimal generation scheduling is one of the important functions for the Microgrid operation. This paper describes a three-step efficient method for the optimal generation scheduling of a Microgrid...
With increasingly proportion of distributed generation (DG) in the power system, traditional power system planning has new challenges and demands. The necessity for flexible electric system, changing regulatory, energy savings and environmental impact are providing impetus to the development of DG. Itpsilas critical that the power system impacts be assessed accurately so that DG can be applied in...
The objective of this paper is to compare the permanent magnet synchronous generator (PMSG) systems for wind turbines with the different drive trains and power ratings by design optimization. The comparison is mainly based on the cost and annual energy production (AEP) for a given wind climate. Firstly, the analytical models of the wind turbine, the 1-stage and 3-stage gearbox, the 3-phase radial-flux...
This paper presents a two-phase genetic algorithm for economic load dispatching of generators in power systems. The problem of ELD is expressed as a Lagrange function. The conventional GA has a drawback that the algorithm is not so effective as the number of variables increases. To improve the GA characteristic, a two-phase GA is proposed to obtain better solutions. The proposed genetic algorithm...
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