Simultaneous optimal network reconfiguration and power compensators allocation with electric vehicle charging station integration using hybrid optimization approach

This paper introduces a hybrid optimization approach, the Hybrid of African Vulture Optimizer with Genetic Operators (HAVOGO), designed to address the intricate challenges of optimal design in large distribution systems. The HAVOGO algorithm combines the robustness of the African vulture optimizer with the adaptability of genetic operators, resulting in superior optimization performance. The algorithm focuses on the simultaneous sizing and locating of distributed generation and distribution static compensator, alongside network reconfiguration, to efficiently incorporate electric vehicle charging stations into existing power distribution networks. A multi-objective optimization framework is utilized to allocate power compensating devices and optimize network reconfiguration, considering both technical and economic factors. The effectiveness of the HAVOGO algorithm is demonstrated through its application to 118-bus and 415-bus large distribution networks. Additionally, the results obtained from the HAVOGO algorithm are compared with those from other optimization algorithms and existing research in the field. Numerical results show significant improvements in performance metrics for both network sizes: for the 118-bus system, there is a reduction in active power loss by 84.72%, a decrease in voltage deviation by 76.22%, and an increase in voltage stability margin by 62.99%. Similarly, for the 415-bus system, the algorithm achieves a reduction in active power loss by 75.78%, a decrease in voltage deviation by 65.54%, and an increase in voltage stability margin by 26.06%.