王枭, 刘天琪, 李兴源. 含风电机组的配电网中分布式电源的最优配置[J]. 现代电力, 2014, 31(3): 12-16.
引用本文: 王枭, 刘天琪, 李兴源. 含风电机组的配电网中分布式电源的最优配置[J]. 现代电力, 2014, 31(3): 12-16.
WANG Xiao, LIU Tianqi, LI Xingyuan. Optimized Location of Distributed Generation in Distribution Network With Wind Turbine Generators[J]. Modern Electric Power, 2014, 31(3): 12-16.
Citation: WANG Xiao, LIU Tianqi, LI Xingyuan. Optimized Location of Distributed Generation in Distribution Network With Wind Turbine Generators[J]. Modern Electric Power, 2014, 31(3): 12-16.

含风电机组的配电网中分布式电源的最优配置

Optimized Location of Distributed Generation in Distribution Network With Wind Turbine Generators

  • 摘要: 为解决配电网中含风电机组分布式电源的最优配置问题,首先根据风速概率密度分布函数,推导出风电机组的输出功率函数,之后构建了含有分布式电源的固定投资费用、负荷增量与分布式电源出力的相关费用和以最小网损和最小常规发电机有功出力为目标的风电机组的惩罚成本的目标函数,并以配电系统中电压、电流等为约束条件,提出了一种以自适应惯性粒子群算法为全局搜索和以混沌算法为局部搜索的混合粒子群算法来获取目标函数的最优解。最后通过IEEE 69节点系统验证了所提出模型和算法的优异性。

     

    Abstract: In order to solve the problem of optimized locating distributed generation in the distribution network with the wind turbines, the output power function is deduced according to the probability density function of wind speed. Then an objective function of penalty cost of wind turbine generators is built with such indexes as fixed investment cost and cost associated with load increment and output power of distributed generation, and minimum system loss and minimum active power output of conventional generators. In addition, voltage and current in distribution network are taken as constraint condition. Furthermore, the hybrid particle swarm optimization approach with the adaptive inertia particle swarm algorithm is used for global searching, and chaotic algorithm is used for local searching, which can obtain the optimized solution of objective function. In the end, the advantage of proposed model and algorithm is verified through IEEE 69 system.

     

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