A Demand Response Based Two Stage Microgrid Energy Management Considering Uncertainity

Microgrid faces challenges due to uncertainties such as penetration of intermittent sources, dynamic energy pricing, and load demand. This article provides a two-stage hybrid energy management system that handles parameter uncertainty utilizing Hong's 2m point estimate method (PEM) is proposed to maintain generation and demand balance in distribution microgrids. The methodology consists of an incentive-based direct load control (DLC) demand response program using a population-based metaheuristic genetic algorithm (GA) at prior stage and scheduling of energy aggregators using a deterministic mixed integer linear programming (MILP) in later. The goals of proposed methodology include lowering overall operating expenses, reducing pollutants and energy losses for day ahead energy management while accounting for unpredictability. Furthermore, a fuzzy interface schedule is developed for charging and discharging of electric vehicle (EV) charging stations for the energy exchange operation during the grid offline condition. Finally, this proposed approach is assessed by considering two cases, grid connected and islanded mode for a 24-hour period on a 14-bus distribution system, The simulation study findings support the effectiveness and acceptability of the method