This paper presents a Reinforcement Learning (RL)-driven multi-objective function-based energy management system (RL-MOF-EMS) for optimizing the economic dispatch and lifespan of electrical, thermal, and hydrogen systems within a multi-energy microgrid (MEMG). By leveraging a discrete Deep Q-Network (DQN) agent, the RL-MOF-EMS dynamically balances energy distribution across multiple resources, ensuring optimal thermal and electrical power allocation. The system evaluates three distinct single-objective functions under different EMS strategies: 1) priority-based regulator (PBR), 2) proportional regulator (PR), and 3) particle swarm optimization (PSO). These objectives are then combined into a complex multi-objective optimization problem, solved using the RL-based DQN agent, which selects from 8 dynamic actions to enhance learning speed and accuracy. This RL approach not only accelerates decision-making but also ensures robust and real-time optimization, driving the efficient operation of MEMG systems. The performance is rigorously tested in Simulink under diverse weather conditions and fluctuating thermal and electrical demand profiles. The results are compared against an EMS based on a nonlinear MATLAB optimizer, demonstrating the effectiveness of the RL-MOF-EMS in coordinating power flows from energy sources and storages. The proposed RL-EMS outperforms the FM-EMS by achieving lower operational costs (0.704 €/h vs. 0.767 €/h) and heating costs (1.327 €/h vs. 1.484 €/h), while maintaining a higher hydrogen utilization rate (71.43 % vs. 63.15 %) and state of charge (60.83 % vs. 58.30 %). Additionally, RL-EMS demonstrates superior multi-objective balancing, with a lower overall MOF value and improved performance across key objectives, including operational efficiency, degradation minimization, and heating cost reduction.

In this study, a reinforcement learning (RL) algorithm is utilized within the energy management system (EMS) for battery energy storage systems (BESs) within a multilevel microgrid. This microgrid seamlessly integrates photovoltaic (PV) plants and wind turbines (WT), employing a multilevel configuration based on battery energy-stored quasi-Z-source cascaded H-bridge multilevel inverter (BES-qZS-CHBMLIs). Twin-delayed deep deterministic (TD3) policy gradient agent is implemented as an RL agent to dispatch power between the BES to meet the requested grid power while considering the BES efficiency and lifetime. Two 4.8 kW PV plants and a 5 kW WT, integrating BES with different rated capacities, are connected to the grid through a BES-qZS-CHBMLI configuration, and the resulting microgrid is simulated in MATLAB to evaluate the proposed RL-EMS performance. Moreover, a SOC-EMS, a fuzzy logic EMS (FL-EMS), and two nonlinear algorithm-based (PSO and fmincon) EMSs are implemented to compare the results with those obtained by the RL-EMS. The comparison demonstrates the superior performance of the RL-based EMS over other methods, with improvements of up to 18.09 % in the integral time absolute error (ITAE) for the active power, 17.77 % in the ITAE for the reactive power, and 21.38 % in the standard deviation (STD) for the active power compared to the other EMSs based on SOC, fuzzy, fmincon, and PSO. Additionally, the fmincon-EMS shows a notable improvement over other methods, achieving up to 15.12 % better performance in power demand tracking and BES dispatch. In a dynamic environment with fluctuating power production and demand, the trained RL system effectively optimizes the power injection or storage between BESs while maintaining grid demand and battery SOC balance.

A novel optimal energy management system (EMS) using a nonlinear constrained multivariable function to optimize the operation of battery energy storages (BESs) used in a hybrid power plant with wind turbine (WT) and photovoltaic (PV) power plants is proposed in this work. The hybrid power plant uses a configuration based on a battery-stored impedance-based cascaded multilevel inverter to integrate renewable energy sources (PV power plants and WT) and BESs into the grid. The new optimal EMS seeks for satisfying the demanded power while dispatching power between BESs to optimize their efficiency. A grid-connected configuration is implemented to assess the efficiency of the suggested supervisory control under changes in renewable energy (changes in wind speed and irradiation), and in a varying active and reactive powers’ request. The BES efficiency obtained from the suggested EMS is set side by side to the BES efficiency got from a conventional EMS and a model predictive control (MPC), both working based on the state-of-charge (SOC) of the BES and balancing power EMS. The results from MATLAB simulation and the experimental results with the real-time OPAL-RT simulator (OP4510, OPAL-RT) and dSPACE MicroLabBox show the effectiveness of the suggested approach and the improvement in long-term BES efficiency. © 2024 The Authors