MOGA-Based Optimal Placement of SOPs in DG-Integrated Distribution Network (OODBODHAN, 2026)
This research paper proposes a multi-objective genetic algorithm (MOGA) optimization framework based on Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) for the optimal placement and sizing of Soft Open Points (SOPs) in distribution networks with high penetration of distributed generation (DG). The methodology addresses key operational challenges introduced by DG, including voltage deviation and increased real power losses, issues that cannot be effectively mitigated using conventional tie-switches. By replacing these switches with SOP devices, implemented through back-to-back voltage source converters (B2B-VSCs), continuous bidirectional control of active and reactive power between feeders is achieved. The IEEE 33-bus test system is used as the study platform, and eight operating cases are examined to evaluate the performance of standalone DG placement, standalone SOP deployment, and coordinated planning of both technologies. The optimization framework efficiently manages discrete location variables and continuous sizing variables, demonstrating stable convergence and avoiding entrapment in local optima. Multiple objectives, including minimization of real power losses, improvement of voltage profiles and minimization of cost are optimized simultaneously. The outcomes confirm that the proposed MOGA-based strategy is highly effective for enhancing power flow control, voltage stability, and overall operational flexibility in active distribution networks, making it a practical solution for future DG-dominated grid environments.