Multi-energy microgrids (MEMGs) represent a specific typology of microgrids that combine multiple energy carriers—including electricity, heat, cooling, and hydrogen—within a coordinated framework. Existing studies emphasize energy dispatch optimization and often neglect real-time dynamic control. This paper presents a novel fuzzy-logic control method for the coordinated operation of electricity, hydrogen, and thermal systems in a residential MEMG. A photovoltaic (PV) power plant serves as the primary renewable energy source, while thermal sources include an electric boiler and a chiller. Additionally, a gas boiler is integrated to manage the hot water circuit. A hybrid energy storage system (HESS), comprising a battery and a hydrogen system, enhances operational flexibility. The fuzzy logic-based energy management system (FL-EMS) dynamically coordinates the interaction among energy systems based on renewable energy input and the state of energy (SOE) of the HESS. The proposed method is evaluated through simulations and hardware-in-the-loop (HIL) testing using OPAL-RT4512 and dSPACE MicroLabBox. The results show that the MEMG operates autonomously, with effective storage coordination and accurate thermal regulation. A sensitivity analysis confirms the robustness and adaptability of the FL-EMS, validating its suitability for real-time MEMG control. Compared to a machine-state-based EMS, the FL-EMS reduces the integral time-weighted squared error (ITSE) for temperature control by 49.38%, operating costs by 12.78%, and energy consumption by 15.05%.

Energy storage systems have been considered in the last few years to improve the performance of energy grids. In a typical power system, an instantaneous balance between generated and consumed power must be maintained, without storing energy. As a result, the power generation must follow the load curve, and due to the variability of electrical demand, the operation of the energy grid may not be economically efficient. Balancing total generated power with total demand, while accounting for losses, requires optimal performance of the electric power system. Consequently, one of the critical components in any energy network is its ability to regulate load frequency effectively. This study investigates the effect of an energy storage system on enhancing load frequency regulation performance in an interconnected energy network comprising two-area steam and hydropower plants. Initially, the energy grid model, incorporating a superconducting magnetic energy storage (SMES) unit, is expressed in state space using first-order differential equations. Subsequently, the effect of the energy storage system on the power network is explored through system mode analysis. Results from time-domain simulations conducted in MATLAB demonstrate the effectiveness of the system mode investigation and its responsiveness to load fluctuations, confirming the reliability of the approach.

The design of energy management systems (EMS) and dynamic control systems for multi-energy microgrids (MEMGs) combining diverse energy vectors (electricity, heating/cooling, and hydrogen) has not been extensively explored. Instead, prior research on MEMGs has predominantly focused on daily or weekly time horizons, adopting a static perspective primarily aimed at cost optimization or emission reduction. To address this gap, this article introduces a novel intelligent EMS based on fuzzy logic and model predictive control designed to minimize energy consumption within a MEMG while avoiding reliance on the main electrical grid. The MEMG comprises a photovoltaic power plant, a battery, electrical residential demand, a fuel cell, an electrolyzer, a hydrogen tank, a gas boiler, an electric boiler, an absorption chiller and thermal residential demand, with a connection to the main grid. By efficiently adjusting the operating points of thermal components based on renewable production and the available energy in the battery and hydrogen system, the MEMG achieves a synergistic integration of various energy vectors. The efficient energy dispatch leads to a 9.56 % reduction in operational costs, and a 2.82 % decrease in the CO2 emissions. Moreover, the findings demonstrate a notable reduction in the usage of both the gas boiler (by 2.82 %) and electric boiler (by 18.75 %), as well as the absorption chiller (8.91 %). Additionally, there is an increase in the state of charge of the electrical battery (by 21.43 %), hydrogen level (by 4.8 %), and state of energy (by 18.85 %). The results of this work thus highlight the adaptability and resilience of the presented EMS in establishing an effective intelligent energy dispatch among multiple energy vectors.

The penetration of renewable power production units in electrical networks through power electronic converters due to their low rotating inertia, leads to increased frequency fluctuations and reduced power system stability. The synchronization of the power converters with the main network is of great importance, so that it must be maintained even during disturbances. Virtual synchronous machines are among the efficient methods to comply with the scarcity of inertia in the power network. In this paper, the aim is to investigate the stability and simulate the dynamic behavior of connecting a virtual synchronous generator (VSG) to an infinite bus employing a small-signal representative. The characteristics of the VSG are compared with the droop method for controlling active and reactive powers. An evaluation between these two different control strategies has been carried out using simulation results in the MATLAB environment. Also, the attributes of the synchronous machines due to changes in the point of damping and inertia parameters are shown. For the accuracy of the simulation results, the small signal model of the studied system has also been implemented in MATLAB/Simulink. Integrating the VSG in the microgrid, in addition to reducing frequency and voltage deviations, also improves stability.

A hybrid renewable energy sources (RES) microgrid supported by battery storages (BS) is designed in this paper to feed residential demands including electric vehicle (EV), electric boiler (EB), and household loads. A gas boiler (GB) is also considered in the proposed microgrid to evaluate an entire thermal bus through the GB, EB, and an underfloor heating system. A constant-speed wind turbine (WT) and a PV power system are considered as RES to reduce the dependency of energy on the grid. Great importance is placed on the thermal and electrical power balance between sources and loads; hence, a fuzzy logic (FL)-based energy management system (EMS) is designed in this respect. The EMS was evaluated using Simulink under various weather conditions and thermal/electrical demands. The results show that the proposed EMS controlled the BS power and satisfied the requested demands under all conditions, while hardly intervening in the grid power.

The considerable research interest in microgrid clusters (MGCs) is owing to their ability to integrate diverse alternating current (AC) and direct current (DC) technologies for consumption, generation and storage, along with their inherent benefits in enhancing the reliability, efficiency, and resilience of energy systems. In this sense, this work presents a novel dynamic control for a grid-connected MGC, which includes an IEEE three-bus system interconnected with one DC microgrid (MG) (composed of a wind turbine, an ultracapacitor, electrical loads, a fuel cell, and an electrolyzer) and two AC MGs (composed each one of photovoltaic generators, electrical loads, and an electrical battery). The novel dynamic control consists of local controllers for each technology of the MGC and a dynamic hierarchical energy management system that coordinates an optimal power dispatch with the objective of optimizing the power losses in the transmission lines within the MGC. To evaluate the dynamic control, the system is studied under variations in the solar radiation, wind speed, and dynamic electrical loads. The dynamic control and the system exhibit robust behavior across the different simulation scenarios implemented.

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 energystored 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.

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 response to growing energy demands and concerns about climate change, microgrid clusters (MGCs) are gaining widespread attention. Their ability to integrate technologies for energy consumption, generation, and storage in AC, DC or a compound of both offer a high degree of flexibility and resiliency. This research introduces a new control strategy for an MGC composed of two MGs interconnected to a local grid via a point of common coupling. The first one is a DC MG comprising an ultracapacitor (UC), wind turbine (WT), DC loads and hydrogen system. The second is an AC MG with a battery bank, AC loads and photovoltaic (PV) generator. The control strategy employs local controllers for each device and distributed control via two control agents, coordinating the power distribution among the energy storage systems (ESSs) of the MGC. This system is tested and validated on a real-time experimental setup, using two Raspberry Pi microcontrollers and an OPAL-RT unit, under different working conditions. The results obtained in this work show that the control strategy implemented in the MGC works correctly.

While multi-energy microgrids (MEMGs) offer a promising approach to reduce energy consumption through coordinated integration of various energy vectors, research has primarily focused on static studies. These studies aim to optimize a particular cost function but neglect the dynamic aspects of the system operation. This paper presents a dynamic model of an MEMG comprising of electricity and thermal vectors. A novel dynamic fuzzy logic-based energy management system (EMS) is investigated, aiming to ensure energy balance (electric and thermal), optimize renewable energy utilization, and reduce the reliance on the local electricity grid and gas. Both the EMS and MEMG have been evaluated under different weather conditions and a 4-hour variable load profile. Furthermore, the EMS effectiveness has been verified through a real-time experiment using an OPAL-RT4512 unit and a dSPACE MicroLabBox prototype. The results show that the proposed fuzzy logic-based EMS outperforms a conventional EMS based on machine states (states-based EMS), achieving a notable reduction in electricity grid consumption of 80%, as well as a consumption reduction of 7.4% in the gas boiler and 5.4% in the electric boiler. Furthermore, the control performance results in a remarkable reduction in ITAE (42.57%), ITSE (89.10%), IAE (54.36%) and ISE (57.55%) for the hot water temperature control, and in ITAE (17.06%), ITSE (52.50%), IAE (31.19%) and ISE (29.99%) for the heating control.

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.

The paper describes the design and evaluation of a multi-energy microgrid (MEMG) that incorporates renewable energy sources (RES), battery storage (BS), fuel cells (FC), electrolyzer (ELZ) as hydrogen vector, and thermal vector to meet residential energy demands. The focus of the paper is on developing an optimal energy management system (EMS) using the ‘fmincon’ optimization algorithm to economically dispatch power between electrical and hydrogen sources while maintaining a balance between thermal and electrical power across all components of the microgrid. The EMS is evaluated using Simulink under different weather scenarios and varying thermal/electrical demands. The results indicate that the proposed EMS effectively controls the power flow from the BS, FC, and ELZ ensuring that minimum costs are imposed on the MEMG while the demands are met under all conditions. Additionally, it suggests that the MEMG is largely self-sufficient, requiring minimal intervention from the grid.

The main approach on multi-energy microgrid (MEMG) study have been focused on optimization problems, without considering the dynamic control and real-time energy dispatch. This paper presents a new fuzzy-logic control method for a MEMG consisting of electricity, hydrogen, heating/cooling vectors. A PV power plant is the main renewable energy source selected. The thermal sources comprise an electric boiler and an absorption chiller, supplied by renewable energy. Furthermore, a gas boiler is considered to control the hot water thermal circuit. A hybrid energy storage system (ESS) is incorporated, encompassing a battery and a hydrogen system. The fuzzy-logic based energy management system (FL-EMS) is presented to dynamically perform a coordinated operation between the different energy vectors. Fuzzy logic algorithm is based on the PV energy and state-of-energy (SOE) of the ESS to dynamically assess the temperature control of the thermal sources. To assess the control efficacy and FL-EMS, a simulation lasting 4.5 hours was conducted under diverse operational conditions involving solar irradiance, heating, cooling, and electrical demand. The funding shows the efficacy of the FL-EMS in reducing dependency on the local grid, thus demonstrating the appropriateness of this approach for MEMGs.

This paper provides a dynamic control for a multi-energy microgrid (MEMG) comprising heating, cooling, hydrogen, and renewable power vectors connected to a utility grid. A gas boiler is responsible for controlling the hot water thermal bus, whereas an electric boiler manages the hot water demand. An absorption chiller is employed in the cooling circuit to fulfill the cooling load. Furthermore, a battery and a hydrogen system comprising a fuel cell, electrolyzer, and hydrogen tank are considered as energy storage systems (ESSs) for the MEMG. The renewable power is provided through a PV power plant. A new energy management system based on operating states (state-based EMS) is designed to provide three different control scenarios: low temperature (LTM), normal temperature (NTM), and high temperature (HTM). The main target of the presented EMS is to adjust the thermal sources with the aim of avoiding the consumption of the local grid. A 4-hour simulation performed in MATLAB/Simulink, encompassing diverse scenarios, effectively validates the control response of the proposed MEMG. The results illustrated the applicability of this approach within the context of MEMGs.

A multiagent reinforcement learning (RL) algorithm is used in this paper as the energy management system (EMS) of an energy stored quasi-Z-source cascaded H-bridge multilevel inverter with (ES-qZS-CHBMLIs) for grid-connected photovoltaic (PV) cells. The duty of the new EMS is meeting the requested power while sorting or releasing power between the battery energy storages (BES). A photovoltaic plant with 4.8 kW capacity in 3 cascaded modules configuration connected to a single-phase grid is simulated in MATLAB to assess the performance of the suggested RL algorithm. For each module, different battery parameters, variable grid references and varying irradiance values are considered to evaluate the suggested EMS. In order to control DC-link voltage, dq dimensional current components, quality of grid power, and optimal operation of the power sources conventional control methods are applied..

Multi-energy microgrids (MEMGs) are becoming an effective way to reduce greenhouse gas emissions. Proper coordination among two or more energy vectors in this type of system can improve its efficiency and provide greater independence from large grids. This work presents a novel dynamic energy management system (EMS) for an MEMG that consists of heat and electricity vectors. The thermal network is composed of a gas boiler, an electric boiler and a heat load. On the other hand, a PV system, a battery bank, an electric load and a connection with the grid constitute the electrical network. In general, the EMS evaluates the PV power and electric demand and adjusts the temperature of the water in the electric boiler to avoid excessive dependency on the local grid. The MEMG and EMS were evaluated through a 4.5 hours-simulation and various conditions of sun irradiance, heat, water, and electric demand. The results show the suitability of the EMS for reducing the dependency on the local grid.

A nonlinear programming solver is used in this paper to optimize a proposed objective function to handle the energy management system (EMS) of a grid-connected quasi-Z-source cascaded H-bridge multilevel inverters with energy storage (ES-qZS-CHBMLIs) for photovoltaic (PV) plants. The solver (fmincon) optimizes a constrained nonlinear multivariable function based on maximizing the battery energy storage system (BES) efficiency. The proposed EMS is responsible for producing the demanded power while dispatching energy among the BES. Considering different battery parameters for each cascaded module working under variable grid references and PV powers, the efficiency is optimized for different conditions. Common control topologies are used for balancing the voltage level on the DC link, enhancing output power quality, reliability, and ensuring optimal operation of the PV power plants. A grid-connected single-phase configuration is designed in MATLAB-Simulink to evaluate the effectiveness of the proposed EMS. Every cascaded qZSI is connected to a 4.8 kW PV power plant, operating in different irradiance conditions, to validate the proposed configuration and the control system.

Balancing DC capacitor voltage of many submodules (SMs) is one of the important issues in modular multilevel converter (MMC) systems.In addition, the balance of thermal stress between SMs should be considered to equalize the lifetime expectation of semiconductors and to enhance the current capability of MMC systems.However, it is complicated to balance all the various factors satisfactorily at the same time.Recent machine learning (ML) techniques can achieve optimal results through learning using numerous data acquired in complex environments.Therefore, this paper proposes a new modulation based on reinforcement learning (RL), which is a subclass of ML methods, to optimally balance the capacitor voltage and thermal stress of SMs.A deep Q-network (DQN) agent, which is one of the RL algorithms, is applied in accordance with a nearest-level modulation (NLM), and main features of the DQN agent are described in this paper.The effectiveness of the proposed modulation based on RL is verified by simulations results.

In this paper, passive reinforcement learning (RL) solved by particle swarm optimization policy (PSOeP) is used to handle an adaptive neuro-fuzzy inference system (ANFIS) type-2 structure with unsupervised clustering for controlling the pitch angle of a real wind turbine (WT). The proposed control scheme is based on gain-scheduled reinforcement learning recurrent ANFIS type 2 (GS-RL-RANFIST2) pitch angle controller to maintain the rotor speed at its rated value while smoothing the output power and the performance of the pitch angle system. The practical application of the proposed controller is evaluated by using FAST tool for a real 600 kW WT equipped with a synchronous generator with a full-size power converter (CART3, located at the National Renewable Energy Laboratory, NREL), whose results are compared with those obtained by a gain corrected proportional integral (GC-PI) controller. The results demonstrate that the GS-RL-RANFIST2, which sets the nonlinear characteristics of the system automatically and waves more uncertainties in the windy conditions, allows to increase the energy capture and smooth the output power fluctuation, and therefore, to improve the control and response of theWT. (C) 2020 Elsevier Ltd. All rights reserved.

The effective utilization of wind energy conversion system )WECS( is one of the most crucial concerns for the development of renewable energy systems. In order to achieve appropriate wind power, different pitch angle methods are used. Recurrent Adaptive Neuro-Fuzzy Inference System (RANFIS) is utilized in this paper in a new effective design to improve the performance of classical and adaptive Proportional Integral (PI) controllers applied for the pitch control purposes. Adaptive-online performance and high robustness coverage are the main advantages of the suggested controller. The effectiveness of the proposed method is verified by a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST7). At any given wind speed, the proposed controller has outperformed PI, Adaptive Neuro-Fuzzy Inference System (ANFIS), and RANFIS based controllers, reducing the mechanical stress of drive train while presenting suitable aerodynamic power tracking and maintaining the rotational speed of the rotor under the rated value.