Optimum Network Reconfiguration using Grey Wolf Optimizer

An Optimal Power Flow (OPF) for transmission power system whith presence of photovoltaic based DG (PV-DG) is proposed by this study. We have minimized power losses using Biogeography-Based Optimization method (BBO) by the objective function. This method has determined the optimal PV-DG size and location in a transmission power system. The BBO method has been examined and tested by the modified IEEE 30_bus, and validated by reduced Algerian power system (114_bus) using MATLAB software.

In this article, we consider a fractional $SEIR$ model, denoted by the $SEIQHR$ model, which aims to predict the outbreak of infectious diseases in general. In particular, we study the spread of COVID-19. The fractional order offers a flexible, appropriate, and reliable framework for pandemic growth characterization. Firstly, we analyze some elementary results of the model (boundedness and uniqueness of solutions). In addition, we establish certain conditions to ensure the local stability of the disease-free and endemic equilibrium points. Based on analytical and numerical results, we conclude that coronavirus infection (COVID-19) remains endemic, which requires long-term prevention and intervention strategies.

This work presents a multiobjective optimization approach to search the optimal position and size of many Wind based on Distributed Generation (WDG) in radial distribution network (DN). The goal are to minimize the active power losses (APL) and to maximize the voltage stability index (VSI). A multiobjective differential evolution with ranking-based mutation operator (MODE-RMO) algorithm is used to obtain the set of optimal Pareto solutions. The technique of fuzzy logic was used in order to chosen the best arrangement solution from the optimal Pareto. The approach is tested on IEEE network (69 bus) and validated on Algerian DN (59 bus).