A Multi-Objective Fuzzy Optimization Model for Electricity Generation and Consumption Management in a Micro Smart Grid

This manuscript proposes an intelligent supply and demand management system in a complete network of electricity production and consumption. A micro smart grid (MSG), which includes a solar cell, a wind turbine, a diesel generator, and battery storage system capable of trading energy with the smart gride (SG), connected to smart buildings with different types of loads is modelled. Different types of intelligent fuzzy controllers for distributed management were proposed and optimized via the non-dominated sorting genetic algorithm-II (NSGAII), which is a multi-objective optimization method. Maximum user comfort, the amount of renewable energy employment, minimum total power consumption cost, total energy consumption at peak time, and MSG loss of power supply probability are the five objective functions of the optimization process. Various uncertainties of the real world have also been considered. The most crucial distinguishing feature of this proposed method is the design of controllers to manage the demand and supply of electricity without the need for daily optimization. Comparison experiments with other methods presented in the field of electricity supply and demand management are conducted to show the superiority of this method in terms of optimality of the results, low processing volume required to implement real controllers, and its resilience to changing conditions.