Optimal Energy Management of the Smart Microgrid Considering Uncertainty of Renewable Energy Sources and Demand Response Programs

The increasing integration of renewable energy sources in components of power systems such as microgrids (MGs) is driving more research focused on evaluating reliability and economic goals. Consequently, the uncertainty in energy generation from renewable sources necessitates using advanced tools like smart grids and demand response programs to enhance reliability metrics within MGs to supply load demand. This study focused on energy management of the MG in the presence of renewable energy sources considering multi-objective function modeling in connecting mode in main grid. The multi-objective functions include minimizing operation costs and maximizing reliability for supply energy to load demand. The Monte Carlo simulation with analytical modeling is used for scenario generation and uncertainty modeling of renewable energy resources and load demand. Also, demand response programs (DRPs) like incentive and price-based demand response improve reliability and minimize costs. The DRPs lead to shaving load demand in peak periods and high prices in the main grid, whereby costs and shortage of energy generation are reduced. The solving of the proposed energy management in MG is done by particle swarm optimization (PSO) algorithm and non-dominated sorting genetic algorithm II (NSGA II). The results of proposed approach are obtained in two case studies considering implementation of DRPs. With implementing DRPs, reliability is improved by 42.2% and 46.15% via PSO algorithm and NSGA II, respectively. Also, by PSO algorithm and NSGA II, operation costs are minimized by 1.8% and 2.67%, respectively.