This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA （Genetic Optimization algorithm）. It is proved that GA method is fast a...This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA （Genetic Optimization algorithm）. It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss. GA fitness function is introduced including the active power losses, reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable. GA fitness function is subjected to voltage constraints, active and reactive power losses constraints and DG size constraint.展开更多
文摘This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA （Genetic Optimization algorithm）. It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss. GA fitness function is introduced including the active power losses, reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable. GA fitness function is subjected to voltage constraints, active and reactive power losses constraints and DG size constraint.
