基于灰狼算法的分布式电源优化配置

分布式电源(DG)的优化配置是主动配电网(ADN)规划中的重要环节,ADN中DG的合理配置对ADN的稳定运行具有重要意义。从电能供给侧出发,建立了考虑DG的投资和主动管理费用、系统网损及电压偏移的多目标联合优化配置模型,并根据1-9标度法对各目标权重系数进行设定。针对传统灰狼算法(GWO)在全局寻优能力和收敛速率上的不足,提出了引入混沌序列的GWO算法,并在IEEE33节点系统中对所提模型进行求解。结果表明,改进方法能对DG进行有效的优化配置,其收敛速度和寻优能力亦优于传统算法。

Optimal Configuration of Distributed Generation Based on Grey Wolf Algorithm

The optimal allocation of distributed generation (DG) is an important part of active distribution network (ADN) planning, and it has important significance for the stability operation of ADN. From the side of the power supply, a multi objective joint optimal allocation model with DG investment and active management cost, system network loss and voltage deviation was established, and the weight coefficients of each target were set based on the 1 9 scale method. Aiming at the shortcomings of the global optimization ability and the convergence speed in the traditional grey wolf optimization algorithm (GWO), chaotic sequences was introduced into the GWO. The proposed model was solved for the IEEE33 node system. The results show that the improved method can optimize the allocation of DG effectively, and its convergence speed and optimization ability are better than the traditional algorithms.