基于多群体自学习群搜索算法的分布式发电优化

为减少分布式发电(DG)对配电网电压和网络损耗的影响,在考虑DG的接入位置、接入容量及接入方法等因素对配电网影响的基础上,建立了以潮流方程及DG接入容量为约束、以配电网中电压偏差和网络损耗同时最小、以DG接入容量最大为优化目标的多目标优化模型。在此基础上,提出了一种多群体自学习群搜索算法(MSLGSO),并将其应用于求解DG优化问题。通过对IEEE33节点的标准配电网算例仿真分析,表明DG的合理配置可使配电网电压水平提升和减少有功网损,且所提算法具有良好的实用性和适应性。

Optimization of Distributed Generation Based on Multiple Self-learning Group Search Optimizer

In order to reduce the impacts of distributed generation (DG) on the distribution network voltage and power loss, this paper proposes a multi-objective model to optimize the voltage deviation, power loss and DG capacity simultaneously. In addition, the model also considers the power flow and DG capacity constraints by analyzing the influence of DG location, DG capacity and DG connection method. A multiple self-learning group search optimizer (MSLGSO) is proposed for solving the model. Through the analysis of standard IEEE33 node case of the distribution network, it shows that optimal configuration of DG can improve voltage level and reduce active power loss. In addition, simulation results demonstrate the practicality and applicability of the proposed algorithm.