Minimizing Operational Cost of a Microgrid with an Optimum Storage System Size and PHEV Charging Demand

The increased need for power and the energy recession have drawn a lot of interest to microgrids (MGs). Conceptually and technologically, MG has made significant strides, especially in automating the distribution process and improving the integration of alternate resources. This article's primary goal is to reduce operating cost by implementing appropriate energy management (EM) in an MG system that integrates distributed generation (DG), battery storage systems (BSS), and plug-in hybrid electric vehicles (PHEVs). Furthermore, the proper EM also estimates the optimum size of BSS. A smart charging approach is proposed to manage the demand for PHEV charging at home and public charging points. Over the course of a whole day, the simulation study is carried out, accounting for the intermittent nature of PHEV charging, load profiles, the outputs of renewable energy sources, and changes in energy costs. A stochastic optimizer based on metaheuristics, the slime mould algorithm (SMA), is used to solve this complicated nonlinear optimization issue of EM in an MG. SMA's effectiveness is evaluated by comparing its results with those of other optimization techniques.