This article proposes a hybrid analytical and metaheuristic optimization technique for optimal allocation of multiple types of distributed generator (DG) in radial distribution networks. DG integration aims to minimize the total power losses and enhance the voltage stability of the distribution system. The proposed technique is formulated using the analytical technique which utilizes the exact loss formula to calculate the initial DG size at a certain bus. However, the analytical technique may not be proper to allocate multiple DGs types due to the mass of calculations, hence, a metaheuristic optimization technique named Tree Growth Algorithm (TGA) is combined with the analytical technique to find the final solution of optimal DG locations and sizes. The hybridization between the analytical and metaheuristic optimization techniques combines the advantages of both techniques and eliminates the disadvantages. The proposed hybrid Analytical TGA (ATGA) is validated using the stranded radial distribution feeders, IEEE 33‐bus and 69‐bus and practical 94‐bus Portuguese system. A comprehensive comparison between the proposed technique and other competitive optimization techniques is carried out to prove its effectiveness. The result shows that the proposed hybrid ATGA is efficient to allocate the multiple DGs types with minimum power loss and a high convergence rate.

中文翻译：

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基于混合优化技术的多种DG类型最优布置的电压稳定性分析

本文提出了一种混合分析和元启发式优化技术，用于径向分布网络中多种类型的分布式发电机（DG）的最优分配。DG集成旨在最大程度地减少总功率损耗并增强配电系统的电压稳定性。所提出的技术是使用分析技术制定的，该分析技术利用精确的损耗公式来计算特定总线上的初始DG大小。但是，由于计算量大，该分析技术可能不适用于分配多种DG类型，因此，将名为树生长算法（TGA）的元启发式优化技术与该分析技术结合在一起，以找到最佳DG位置和大小。分析和元启发式优化技术之间的混合结合了这两种技术的优点，并消除了这些缺点。拟议的混合分析TGA（ATGA）已使用多股径向分布馈线，IEEE 33总线和69总线以及实用的94总线葡萄牙语系统进行了验证。对提出的技术与其他竞争性优化技术进行了全面比较，以证明其有效性。结果表明，所提出的混合式ATGA能有效分配多种DG类型，且功耗最小且收敛速度较高。对提出的技术与其他竞争性优化技术进行了全面比较，以证明其有效性。结果表明，所提出的混合式ATGA能有效分配多种DG类型，且功耗最小且收敛速度较高。对提出的技术与其他竞争性优化技术进行了全面比较，以证明其有效性。结果表明，所提出的混合式ATGA能有效分配多种DG类型，且功耗最小且收敛速度较高。