Adaptive genetic-enhanced mountain gazelle optimization for integrated network reconfiguration, distributed generation, and capacitor bank planning in large-scale radial distribution systems

This paper introduces a novel adaptive genetic operator-based mountain gazelle optimizer (AGOMGO) algorithm for optimal planning of different types of distributed generation (DG) and capacitor bank (CB) along with network reconfiguration of large radial power distribution networks (RDPNs). In the proposed algorithm, the mountain gazelle optimizer is integrated with adaptive genetic operators to improve its ability to efficiently explore complex search spaces and identify optimal solutions. The main aim of this study is to optimize the performance of large RDPNs by mitigating power losses, enhancing voltage profiles, and increasing the voltage stability index. To achieve this goal, the adopted optimization problem is formulated as non-linear-mixed integer problem. The effectiveness of the proposed approach is validated through simulation studies on 118-bus and 136-bus RPDNs with various configurations for integrating compensating devices alongside optimal network reconfiguration. Furthermore, a comparative analysis is conducted to evaluate the performance of the proposed AMGOGO algorithm against the standard mountain gazelle optimizer and other well-known optimization algorithms, as well as the results of existing studies. The results indicate a significant reduction in real power losses, with a 90.77% decrease in the 118-bus system and an 87.54% decrease in the 136-bus system, achieved through the integration of DG operating at optimal power factor and CB along with NR.