Design of a Two-Layer Intelligent Framework for Energy Management in Microgrids Utilizing Deep Learning-Based Prediction and MISOCP Optimization
Keywords:
Microgrid Energy Management, Two-Layer Optimization, Deep Learning Prediction, Mixed-Integer Second-Order Cone Programming (MISOCP), Battery Energy Storage System (BESS)Abstract
With the rapid increase in energy consumption and the expansion of renewable energy usage, microgrids have emerged as an effective solution for enhancing the sustainability, flexibility, and efficiency of power systems. In this study, an adaptive two-layer Energy Management System (EMS) is designed and presented for microgrids. The first layer utilizes Mixed-Integer Linear Programming (MILP) with a rolling horizon strategy to focus on economic optimization over a medium-term horizon. The second layer employs nonlinear optimization using the Particle Swarm Optimization (PSO) algorithm to accurately manage short-term and real-time network conditions. Additionally, intelligent forecasting models based on deep learning, such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM), are used to improve the accuracy of load and meteorological parameter predictions. A precise model of the Battery Energy Storage System (BESS), considering converter efficiency, aging processes, and optimal charging strategies, is also proposed as part of the innovation. Simulation results demonstrate that the proposed two-layer structure exhibits high adaptability to uncertainties and can significantly improve performance, reduce costs, and enhance the sustainability of microgrids.
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Copyright (c) 2025 Ali Faal Meskin (Author); Ali Bozorgi Amiri (Corresponding author); Ali Akbar Safavi (Author)
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