An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty

All over the world, the operators of the power distribution networks (DNs) are still looking for improving the efficiency of their networks. The performance of DNs and lifetime of its component have been significantly affected by its capability of varying their topologies with accurate load gathering via smart grid functions. This paper investigates making use of the smart DNs features and proposes a model of handling the capability of re-allocating the capacitors integrating with configuring the DNs topology. Using the developed formulation, the efficiency of DNs can be improved not only by minimizing the operational costs related to the network losses but also by optimizing the investment costs associated with capacitor re-allocations. Also, various load patterns are employed in the developed formulation to imitate the daily load variations over a year. The improved sunflower optimization algorithm (ISFOA) is proposed in this paper to get the optimal solution of the presented problem. The standard IEEE 33-node feeder and practical 84-node system of Taiwan Power Company (TPC) are the considered test systems. Besides, the uncertainties due to a distributed generation of wind power are investigated via Monte Carlo simulation involved with the proposed ISFOA. Furthermore, to verify the ability of ISFOA to obtain better solutions compared with different recent optimizers, a statistical comparison is carried out based on a large scale 118-node distribution systems. The simulation results reveal that significant technical and economic benefits are obtained by applying the proposed algorithm with higher superiority and effectiveness.