Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration - Ayikpa, Malinwo Estone [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2025)
The distribution network is likely to experience higher active losses and lower voltage profiles due to its radial topology, the nature of loads and transformers, and recently with increased penetration of distributed generations. The power quality study in the distribution system aims to decrease power losses and mitigate voltage drops. This has been carried out using empirical techniques or exact optimization methods. However, distribution feeder reconfiguration (DFR) has become the best way to reduce losses and improve voltage profiles in the distribution system. In this study, the DFR was addressed simultaneously with the integration of distributed generation (DG) and capacitor bank (CB) into a large-scale distribution network. The stochastic fractal search algorithm (SFS) has been implemented to solve this problem. The loss-sensitive factor (LSF) approach was applied to reduce the search space of candidate buses for DG and CB placement when initializing the optimization variables and when correcting the boundary violation during the optimization process. Furthermore, we proposed two efficient mathematical strategies to control and correct the violation of tie-switches bounds at the diffusion and updating stages of the SFS, and to define CB sizes for better integration of them into the grid. The proposed algorithm was tested on the 136-bus distribution network. Different simulation cases have been carried out, and the results showed a significant impact on reducing the losses and enhancing the minimum voltage value of the feeder, especially when distributed generations and capacitor banks are simultaneously addressed in the DFR problem.
Keywords: capacitor bank; combinatorial optimization; distributed generation; Distribution feeder reconfiguration; mathematical strategy; power losses reduction; stochastic fractal search