Coordinated Distribution Network Reconfiguration and Optimal Distributed Generation Placement for Power Loss Reduction and Reliability Enhancement Using HGA–PSO

Abstract

Distribution network reconfiguration (DNR) and distributed generation (DG) integration are effective strategies for improving the operational efficiency and voltage performance of modern power distribution systems. However, their coordinated optimization constitutes a complex multi-objective problem involving both discrete and continuous decision variables. This paper proposes a Hybrid Genetic Algorithm–Particle Swarm Optimization (HGA–PSO) framework for simultaneous distribution network reconfiguration and optimal DG placement and sizing. The proposed HGA–PSO combines the global exploration capability of the Genetic Algorithm (GA) with the fast convergence characteristics of Particle Swarm Optimization (PSO) to effectively balance exploration and exploitation. The optimization framework simultaneously minimizes active power losses and improves voltage profiles while satisfying power balance, voltage magnitude, branch current, DG operating, and network radiality constraints. The proposed algorithm was implemented in MATLAB R2024a and evaluated using the Institute of Electrical and Electronics Engineers (IEEE) 33-bus distribution network and the IEEE 118-bus benchmark system. The IEEE 33-bus system is used to validate the proposed DNR and DG methodology, while the IEEE 118-bus system is used as a computational benchmark to evaluate the performance of the optimization framework on a larger-scale optimization problem. For the IEEE 33-bus system, active power losses were reduced by 52.22%, while the minimum bus voltage improved from 0.913 p.u. to 0.982 p.u. For the IEEE 118-bus benchmark system, active power losses were reduced by 25.75%, and the minimum bus voltage increased from 0.869 p.u. to 0.966 p.u. The results demonstrate the effectiveness of the proposed HGA–PSO framework under the investigated benchmark systems, modelling assumptions, and simulation conditions.