Abstract In this study, we analyzed the optimal location and sizing of distributed generators (DGs) in radial distributed networks using a hybrid master-slave metaheuristic technique. The master stage corresponds to the selection of suitable points for the locations of the DGs, whereas the slave stage is the optimal dimensioning problem. The Chu-Beasley genetic algorithm (CBGA) is employed to solve the master stage, and the optimal power flow (OPF) method via the vortex search algorithm (VSA) is employed to solve the slave stage. The OPF solution from the VSA technique uses a successive approximation power flow to determine the voltage profiles and power losses by guaranteeing the energy balance in all the nodes of the network. The conventional and widely used 33- and 69-node test feeders are used to validate the hybrid CBGA-VSA for analyzing the optimal location and sizing of the DGs in the distribution networks using MATLAB software. The numerical results demonstrate the efficiency of the proposed optimization method in terms of power loss reduction as compared with the results available in the literature. An additional 24-h dimensioning analysis is included for demonstrating the efficiency and applicability of the proposed methodology for daily operations with renewable generation.

中文翻译：

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涡旋搜索和 Chu-Beasley 遗传算法在配电网络中优化分布式发电机的位置和大小：一种新的混合方法

摘要 在这项研究中，我们使用混合主从元启发式技术分析了径向分布式网络中分布式发电机 (DG) 的最佳位置和大小。主级对应于为 DG 的位置选择合适的点，而从级是最佳尺寸标注问题。主级采用Chu-Beasley遗传算法(CBGA)求解，从级采用涡旋搜索算法(VSA)的最优潮流(OPF)法求解。VSA 技术的 OPF 解决方案使用逐次逼近的功率流来通过保证网络所有节点的能量平衡来确定电压分布和功率损耗。传统且广泛使用的 33 和 69 节点测试馈线用于验证混合 CBGA-VSA，以使用 MATLAB 软件分析配电网络中分布式电源的最佳位置和尺寸。与文献中可用的结果相比，数值结果证明了所提出的优化方法在降低功率损耗方面的效率。包括额外的 24 小时尺寸分析，以证明所提议的方法对可再生能源发电的日常运营的效率和适用性。