Deep-Reinforcement-Learning-Based Sliding Mode Control for Optimized Energy Management in DC Microgrids

Monia Charfeddine, Mongi Ben Moussa and Khalil Jouili ()
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Monia Charfeddine: Laboratory of Advanced Systems, Polytechnic School of Tunisia (EPT), B.P. 743, Marsa 2078, Tunisia
Mongi Ben Moussa: Department of Physics, College of Sciences, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Khalil Jouili: Laboratory of Advanced Systems, Polytechnic School of Tunisia (EPT), B.P. 743, Marsa 2078, Tunisia

Mathematics, 2025, vol. 13, issue 19, 1-25

Abstract: A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for fast and robust DC bus voltage regulation with a Deep Q-Learning (DQL) agent that learns optimal high-level policies for charging, discharging, and load management. This dual-layer design leverages the real-time precision of SMC and the adaptive decision-making capability of DQL to achieve dynamic power sharing and balanced state-of-charge levels across storage units, thereby reducing asymmetric wear. Simulation results under variable operating scenarios showed that the proposed method significantly improvedvoltage stability, loweredthe occurrence of deep battery discharges, and decreased load shedding compared to conventional fuzzy-logic-based energymanagement, highlighting its effectiveness and resilience in the presence of renewable generation variability and fluctuating load demands.

Keywords: DC microgrid; energy management system; sliding mode control; deep reinforcement learning; Deep Q-Learning; intelligent control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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