This paper develops intelligent energy management in Microgrid using forecasting-based multi-objective optimization using genetic algorithm framework. In this work, the energy storage system is included in Microgrid network, which is essential for effective energy management and smooth power transfer. The developed model incorporates the forecasted values of solar PV and wind generation obtained using time series long short-term memory network for the next 24 h and provides the optimal values of battery and grid powers to meet the deficit of power to meet the demand in the Microgrid. The proposed model incorporates grid power consumption cost and battery degradation cost as the objectives with battery status and renewables utilization as the constraints of the optimization model. The fuzzy decision-making strategy obtains the compromise solution in the Pareto optimal front from multiple solutions. The proposed model is investigated with fixed and variable grid tariff conditions achieving efficient performance in energy management with optimal utilization of renewables, grid and battery in the system for 24 hours-time horizons. The system parameters in the Microgrid network are also investigated in the time horizon with the optimal values of proposed model. The proposed intelligent energy management system model is tested in 2.5 MW PV/wind/energy storage Microgrid system in MATLAB 2020 simulation platform and experimental setup of 1 kW grid connected Microgrid with solar PV and battery.


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