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Intelligent Microgrid Energy Management System Based on Deep Learning Approach

Recently, microgrids (MG) have emerged as an essential solution for smart grids. The MG efficiently aggregates dispersed distributed energy resources (DERs) and balances renewable energy output variability. Uncertainties of power generation resources and consumption have disruptive influences on MG optimal decision making. This article proposes an intelligent energy management system (EMS) for grid-connected MGs. A forecasting approach is employed based on the convolutional LSTM (long-short time memory) to overcome uncertainties and improve the performance of the energy management system. MG comprises wind turbines (WTs), photovoltaic (PV) panels, battery energy storage systems (BSS), and electrical loads. Also, we utilized the mixed-integer linear programming (MILP) approach to offer an optimal solution by minimizing operating costs. Finally, the study compares its performance over similar state-of-the-art models by employing available real datasets from California, San Diego MGs. The experimental results show strong evidence of the effectiveness of the proposed model.