ENERGY MANAGEMENT AND HARMONIC MITIGATION OF HYBRID RENEWABLE ENERGY MICROGRID USING COORDINATED CONTROL OF MULTI-AGENT SYSTEM

In this paper, a novel energy management method that is based on a Multi-Agent System (MAS) is presented for hybrid Distributed Energy Sources (DES) in a microgrid. These DESs include Photovoltaic (PV), wind energy systems, and Fuel Cell (FC) in the Microgrid (MG). The MG is responsible for supplying both active and reactive powers, allowing it to serve variable linear and non-linear loads. The MAS that has been proposed and is based on a decentralized control structure offers control not only for the energy management of the Distributed Generation (DG) but also for the management of power flow between the MG and the power grid that is connected to the MG. This control is offered by the MAS. The main objective of the control strategy is to manage the amount of energy that is transferred between the power grid and the MG concerning the supply conditions of the required internal energy via DES, which will ultimately result in a reduction in the dependence on the MG on the grid. For current harmonic compensation, a Static Compensator (STATCOM) with a Fuzzy Logic (FL) based Instantaneous Reactive Power control scheme is used. On the other hand, a discrete controller is utilized to manage the energy of the MG. The findings of the simulation and the experiments demonstrated that the implementation of the suggested Energy Management System (EMS) has good performance as a novel energy management solution for a hybrid distributed power generating system and harmonic compensation

Energy management in microgrids with renewable energy sources: A literature review

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

A Fuzzy Logic Based Power Management System for a Hybrid Microgrid

A fuzzy logic-based power management system for a hybrid microgrid is designed in this paper. This designed system is capable to manage the generations considering the load conditions. Consequently, a balance is maintained between generations and load. Furthermore, the system is capable to monitor and manage the storage system charging and discharging as well as to support the external load by sharing power as per considering the situation. Moreover, to make this system more efficient it is such designed that, the storage system will not take any energy from the conventional generation units of the microgrid. Additionally, it is also ensured that, no power will be delivered to the external load utilizing storage or generator. The “Fuzzy Logic Designer” toolbox of MATLAB is used to observe the relationship between the input and output variables, after establishing the fuzzy sets and rules. Furthermore, transferring the fuzzy data sets and rules to MATLAB Simulink and performing different case studies, the effectiveness of the designed system is verified numerically

Intelligent control of battery energy storage for microgrid energy management using ANN

In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique

Fuzzy logic-based controller of the bidirectional direct current to direct current converter in microgrid

Microgrids are small-scale power networks that include renewable energy sources, load, energy storage systems, and energy management systems (EMS). Lithium-ion batteries are the most used battery for energy storage in microgrids due to their advantages over other types of batteries. However, to protect the battery from the explosion and to manage to charge and discharge based on state-of-charge (SoC) value, this type of battery requires the use of an energy management system. The main objective of this paper is to propose an intelligent control strategy for energy management in the microgrid to control the charge and discharge of Li-ion batteries to stabilize the system and reduce the cost of electricity due to the high cost of grid electricity. The proposed technique is based on a fuzzy logic controller (FLC) for voltage control. The FLC is based on the measured voltage of the direct current (DC) bus and the fixed reference voltage to generate buck/boost converter signal control. The proposed technique has been simulated and tested using MATLAB/Simulink software which illustrates the tracking of desired power and DC bus voltage regulation. The simulation results confirm that the proposed systems can diminish the deviations of the system's voltage

Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.Comment: 14 pages, 4 figures, published on IEEE Transaction on Smart Grid 2022 Nov 15. See: https://ieeexplore-ieee-org.utk.idm.oclc.org/stamp/stamp.jsp?arnumber=995140

A review of networked microgrid protection: Architectures, challenges, solutions, and future trends

The design and selection of advanced protection schemes have become essential for the reliable and secure operation of networked microgrids. Various protection schemes that allow the correct operation of microgrids have been proposed for individual systems in different topologies and connections. Nevertheless, the protection schemes for networked microgrids are still in development, and further research is required to design and operate advanced protection in interconnected systems. The interconnection of these microgrids in different nodes with various interconnection technologies increases the fault occurrence and complicates the protection operation. This paper aims to point out the challenges in developing protection for networked microgrids, potential solutions, and research areas that need to be addressed for their development. First, this article presents a systematic analysis of the different microgrid clusters proposed since 2016, including several architectures of networked microgrids, operation modes, components, and utilization of renewable sources, which have not been widely explored in previous review papers. Second, the paper presents a discussion on the protection systems currently available for microgrid clusters, current challenges, and solutions that have been proposed for these systems. Finally, it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation