Enhancing Distribution System Performace with Concurrent Planning of DG and Soft Open Point

In modern power systems, distribution networks face numerous challenges such as increasing active and reactive power losses, poor voltage profiles, and limited system reliability due to rising load demands and network constraints. These issues necessitate the adoption of advanced optimization methods for effective planning and operation. This study addresses these challenges by exploring two independent optimization strategies Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO)-for the optimal placement and sizing of Distributed Generation (DG) units and Soft Open Points (SOPs) within the IEEE 33-bus Radial Distribution System (RDS). The proposed method aims to determine the optimum locations and sizes of multiple DGs and SOPs to improve overall system efficiency. The optimization process is guided by a well-defined multi-objective function, which incorporates key technical performance indices including active and reactive power loss minimization, voltage profile enhancement, and system reliability improvement. The IEEE 33-bus RDS is used as the test system to validate the proposed approach. The simulation results reveal that both TLBO and PSO contribute significantly to improving system performance compared to the base case. However, TLBO consistently outperforms PSO across all evaluated parameters, achieving greater reduction in power losses, better voltage profile enhancement, and higher reliability improvements. These findings demonstrate that TLBO is a more effective optimization approach for DG and SOP planning in RDS, offering a superior solution for improving technical performance and supporting future grid modernization.