Optimal DG Placement and Feeder Reconfiguration for Enhanced Voltage Stability and Loss Minimization in Radial Distribution Networks

Abstract

Optimal allocation of distributed generation (DG) and feeder reconfiguration are critical strategies for improving the operational efficiency and voltage stability of modern radial distribution networks under increasing penetration of renewable resources. However, the simultaneous optimization of DG placement, sizing, and network topology constitutes a highly nonlinear multi-objective problem subject to electrical, operational, and radiality constraints. Unlike existing studies that treat DG allocation and feeder reconfiguration as separate or weakly coupled problems, this work introduces a unified mixed-integer nonlinear optimization framework that captures their strong interdependency. In addition, a hybrid Big Bang-Big Crunch (HBB-BC) algorithm is proposed, combining stochastic contraction with adaptive learning mechanisms to improve convergence robustness in highly nonlinear search spaces. This contribution addresses the limitations of conventional metaheuristics in handling coupled topology-generation optimization problems and provides a scalable solution for modern active distribution networks. We propose a coordinated optimization framework for optimal DG placement and feeder reconfiguration aimed at minimizing real power losses while enhancing voltage stability and reducing both operational cost and environmental impact. The problem is formulated as a constrained multi-objective optimization model and solved using an improved hybrid Big Bang-Big Crunch metaheuristic algorithm which integrates exploration and exploitation mechanisms to achieve fast convergence and robust global search performance. The proposed method is validated on both IEEE 33-bus and IEEE 69-bus radial distribution systems under multiple operational scenarios. The results demonstrate that the coordinated optimization consistently achieves significant performance improvements across different network scales, confirming the robustness and scalability of the proposed framework.