Microgrid Components Research Articles

A multi-agent system approach for real-time energy management and control in hybrid low-voltage microgrids

The real-time operation of microgrids is crucial due to its ability to enhance energy resilience and efficiency. Continuous monitoring and adaptation to fluctuations in energy supply and demand ensure a stable and reliable electricity supply to local communities, even in the case of grid disturbances. This article presents an efficient and easily implementable real-time energy management and control system based on multi-agent systems for hybrid Low-Voltage Micro-Grids (LVMGs) using energy storage systems and renewable sources. The main objective of the proposed approach is to determine optimal setpoints for all microgrid components to improve overall efficiency and reduce electricity costs while satisfying multiple constraints. The well-defined flexibilities of all microgrid components to adjust their power are exploited to achieve optimal power allocation. The approach is tested and validated by developing a simulation environment using the Java Agent Development (JADE) framework, enabling the implementation of microgrid optimization through MATLAB/Simulink.

Cost-effective and optimal pathways to selecting building microgrid components – The resilient, reliable, and flexible energy system under changing climate conditions

Amidst the changing climate conditions, electricity retailers-led demand response programs and the emergency backup power for possible grid outages are often discussed in isolation, although several underlying linkages exist, which are important for how the existing building stock evolves with the energy transition. This study navigates through the linkages while investigating the levelized cost of electricity (LCOE)-based building microgrid components and undertakes a comparative analysis of energy optimisation models with and without emergency diesel generators. It also examines the building energy system’s resilience, reliability, and flexibility by using OpenStudio and HOMER Grid to develop energy simulation and optimisation models and other probabilistic models for a chosen building archetype in Sydney, Australia. This study finds that excluding the emergency diesel generator will require a larger battery storage system, increasing LCOE from A$0.17/kWh to A$0.20/kWh across climate scenarios. However, the larger battery storage system increases the probability of surviving outages (> 4 h) by around 10 % across climate scenarios. The LCOE increases up to 45 % for outages up to 8 h across climate scenarios and up to 85 % for outages up to 12 h. Additionally, for the building archetype, the probability of grid purchase (below the current average) is 0.78 in 2030, which drops to 0.55 in 2050. The probability of grid sales (above the current average) also drops from 0.69 to 0.46. Thus, while the narratives around the building energy system’s flexibility overstate the electricity exchange between the building and the grid, this study finds that increasing the on-site production utilisation rate and larger battery throughput contributes to demand response application and flexibility.

Integrating Bayesian inference and neural ODEs for microgrids dynamics parameters estimation

The integration of solar and wind energy sources in microgrids has witnessed significant growth, giving rise to distinct challenges due to their intermittent nature when it comes to achieving efficient microgrid control. However, estimating the parameters of the dynamic microgrid components facilitates capturing the complex and time-varying characteristics of renewable energy generation. This requires an accurate estimation of the parameters from the dynamic differential equations for effective modeling and control. In this research paper, we presented a novel methodology based on the integration of Bayesian inference and Neural ODEs. The Bayesian inference quantifies the uncertainty, and the Neural ODEs model the dynamic systems. By combining the strengths of both methods, we aimed to achieve a precise and robust parameter estimation of the dynamic microgrid components. The methodology is validated on a simulated microgrid that consists of a diesel generator, Solar PV array, double-fed induction generator, and a battery energy storage system. The results showed promised inferences estimation obtained from the parameter posterior distribution even in the presence of uncertainty. This can enhance our understanding of the dynamics of renewable energy systems and can contribute to the advancement of decision-making microgrid control strategies.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Component, design, and prospects of self-consistent energy systems for transport infrastructures

ABSTRACT This paper introduces a Transportation Self-Consistent Energy System (TSCES) to meet the increasing electricity requirements of transport infrastructure in various traffic environments, including highway, railway, and waterway. The TSCES consists of four primary components: power source, load, energy storage, and interconnected microgrid. The power source component can be categorized into natural energy endowment and energy-electricity conversion components. Load components can be classified, quantified, and forecasted based on diverse transportation facilities, traffic volume, and importance levels. Energy storage components necessitate thorough consideration of the technical attributes of various energy storage methods, along with the functional requirements of the traffic environment, power sources, and loads. Interconnected microgrid components establish connections among all components, followed by configuration optimization and choosing appropriate network topological structures based on techno-economic conditions, ensuring that all components adhere to constraints imposed by transportation space, regulations, and policy indicators. Additionally, the design procedure and architectural patterns of TSCES are presented, which indicates that the TSCES has excellent adaptability and research application value in the field of transportation.

Fuzzy Markov model for the reliability analysis of hybrid microgrids

This research presents a process for analyzing a hybrid microgrid's dependability using a fuzzy Markov model. The research initiated an analysis of the various microgrid components, such as wind power systems, solar photovoltaic (PV) systems, and battery storage systems. The states that are induced by component failures are represented using a state-space model. The research continues by suggesting a hybrid microgrid reliability model that analyzes data using a Markov process. Problems arise when trying to estimate reliability metrics for the microgrid using data that is both restricted and imprecise. This is why the study takes uncertainties into account to make microgrid reliability estimations more realistic. The importance of microgrid components concerning their overall availability is evaluated using fuzzy sets and reliability assessments. The study uses numerical analysis and then carefully considers the outcomes. The overall availability of hybrid microgrids is 0.99999.

Optimal load scheduling strategy for isolated building-integrated DC microgrid using binary PSO to maximize user comfort and minimize peak-to-average ratio

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings.

A physics-informed learning algorithm in dynamic speed prediction method for series hybrid electric powertrain

Engine-generator set (EGS) is an important energy supply component of high-voltage microgrid in series hybrid electric powertrain (SHEP). Sustained and steady energy supply from EGS is one of the conditions for the balanced energy between supply and demand. In some high-power processes, the balanced energy would be broken and the dynamic speed of EGS would be out of expectation, which can result in unstable working states of EGS. If the unstable working states of EGS can be known prior, it is significant for the research of unstable state identification and avoidance. Predicting rotational speed of EGS can warn of the previous issue in advance, while the insufficient data of unstable states would encounter overfitting problems in common prediction methods, so it is a challenge to improve the prediction effect of dynamic speed and then accurately predict the unstable states. Base on the above problems, a physics-informed learning algorithm with adaptive mechanism is proposed for EGS rotational speed prediction in this paper. First, a prediction problem related to the stability of SHEP running state is studied, which is found from engineering knowledge. Second, a new mechanism is proposed for physics-informed learning algorithm, and the physical information adopted to learning algorithm is more selective. Third, a professional adaptive function is originally formed according to speed characteristics, which bridge the information between physics and learning algorithm. By importing the experimental data, the prediction accuracy of proposed method in one of the test cycles is better than the results of baseline methods, specifically 27.11% and 3.49%, 11.90% and 7.94%, 53.83% and 27.62%. In summary, the proposed method can have better predictions against other baseline methods.

Anfis Based Energy Management System for Microgrids

Microgrids are typical low or medium voltage distribution networks that operate on an adaptive system. Which, employing intelligent energy management techniques, govern the power exchange between the main grid, locally distributed generators (DGs), and associated loads. This paper describes and evaluates the intelligent ANFIS based energy management approach for grid integrated microgrid networks. The ANFIS based intelligent controller estimates the power that must be generated by (or) stored in batteries, taking into consideration of power demanded by loads and available battery power by accounting the battery state of charge. To evaluate the power allocation an artificial intelligent based technique i.e., Sugeno-based fuzzy EMS is implemented. The stated fuzzy logic control enhances power distribution within the micro-grid components while minimizing the requirement for power transfer to/from the main grid.

Energy Management Strategy for a Net Zero Emission Islanded Photovoltaic Microgrid-Based Green Hydrogen System

Investing in green hydrogen systems has become a global objective to achieve the net-zero emission goal. Therefore, it is seen as the primary force behind efforts to restructure the world’s energy, lessen our reliance on gas, attain carbon neutrality, and combat climate change. This paper proposes a power management for a net zero emission PV microgrid-based decentralized green hydrogen system. The hybrid microgrid combines a fuel cell, battery, PV, electrolyzer, and compressed hydrogen storage (CHSU) unit aimed at power sharing between the total components of the islanded DC microgrid and minimizing the equivalent hydrogen consumption (EHC) by the fuel cell and the battery. In order to minimize the EHC and maintain the battery SOC, an optimization-based approach known as the Equivalent Consumption Minimization Strategy (ECMS) is used. A rule-based management is used to manage the power consumed by the electrolyzer and the CHSU by the PV system in case of excess power. The battery is controlled by an inverse droop control to regulate the dc bus voltage and the output power of the PV system is maximized by the fuzzy logic controller-based MPPT. As the hybrid microgrid works in the islanded mode, a two-level hierarchical control is applied in order to generate the voltage and the frequency references. The suggested energy management approach establishes the operating point for each system component in order to enhance the system’s efficiency. It allows the hybrid system to use less hydrogen while managing energy more efficiently.

An Efficient Metaheuristic Technique for Wireless Sensor Network Integrated Micro Grid Environment Using Improved Whale Optimization Algorithm (Iwoa)

Wireless Sensor Network (WSN) nodes require careful power management to maintain long term operation without regular battery changes. Battery replacement can be challenging and expensive in a microgrid scenario where sensors may be installed in remote or i naccessible areas. To extend the life of WSN nodes, energy efficient design and power management strategies should be used. A potential strategy for effectively monitoring, controlling, and managing the distributed energy resources (DERs) inside a microgri d is theincorporation of WSNs with microgrids. This research work uses Improved Whale Optimization Algorithm (WOA) for optimizing the routing and performance. The humpback whale's hunting style served as the inspiration for the WOA, a metaheuristic optimization algorithm. WOA is an optimization technique that is used with MG and WSNs. During the optimization phase, WOA establishes a balance between exploitation and exploration. To increase the lifespan of a WSN in a microgrid, effective energy management i s frequently needed. When considering the dynamic energy availability from renewable sources and the energy requirements of the microgrid components, WOA is used to optimise energy allocation and routing in WSNs. This may lead to increased energy efficiency and longer network longevity. The IWOA attains 97.5% PDR that is higher than the existing state of art techniques.

Design Optimization of a Grid-Tied Hybrid System for a Department at a University with a Dispatch Strategy-Based Assessment

In this research project, the optimal design and design evaluation of a hybrid microgrid based on solar photovoltaics, wind turbines, batteries, and diesel generators were performed. The conventional grid-tied mode was used in addition to dispatch strategy-based control. The study’s test location was the loads in the Electrical, Electronic and Communication Engineering (EECE) department at Pabna University of Science and Technology (PUST), Pabna, Bangladesh. DIgSILENT PowerFactory was employed to determine the power system-based behaviors (electrical power, current, voltage, and frequency) of the proposed hybrid system, while a derivative-free algorithm was used for the expense, optimal size, and emission assessments. While developing the microgrid, load following (LoF) and cycle charging (CyC) control were employed. The microgrid is supposed to have a 23.31 kW peak load requirement. The estimated microgrid’s levelized cost of energy (LE), its net present cost (NC), its operating cost, and its annual harmful gas emissions were estimated in this work. Additionally, since the microgrid is grid-connected, the amount of energy output that might be exported to the grid was also estimated, which will potentially increase during blackouts. The power system responses found in this study ensure that the various microgrid components’ voltage, frequency, current, and power outcomes are steady within the designated range, making the microgrid practical and robust.

A Cyber-Physical Testbed for IoT Microgrid Design and Validation

Microgrids are small power systems, often equipped with renewable energy sources, that are alternatives or supplementary to utility grids. Many studies have been conducted on the design and implementation of microgrids and their interconnects to utility grids, and investigations have been extended to the use of Internet of Things technology (IoT) to monitor and operate such power grids. However, the broad applications of the IoT technology itself also call for a green energy solution. This paper investigates how to power local IoT applications via an integration of a microgrid and the utility grid. Together, we call such a system an IoT microgrid. The goal of an IoT microgrid is to maintain the availability of IoT applications while saving energy costs, and this is achieved by sustaining IoT applications via local renewable energy from a microgrid and by mitigating the intermittent power supply using the utility grid. This paper characterizes the IoT microgrid and proposes a configurable cyber-physical testbed for its design and validation. The testbed incorporates the hardware-in-the-loop (HIL) approach, where real-time simulation is integrated with physical elements for quick prototyping of those components in an IoT microgrid. The paper concludes with an example implementation of the proposed testbed, which demonstrates its use for validating both an IoT microgrid and the IoT application it sustains.

Multi-objective optimization and sustainable design: a performance comparison of metaheuristic algorithms used for on-grid and off-grid hybrid energy systems

Alternative energy sources are needed for a sustainable world due to rapidly increasing energy consumption, fossil fuels, and greenhouse gases worldwide. A hybrid renewable energy system (HRES) must be optimally dimensioned to be responsive to sudden load changes and cost-effective. In this study, the aim is to reduce the carbon emissions of a university campus by generating electricity from a hybrid energy production system with solar panels, wind turbine, a diesel generator, and battery components. On the university campus where the hybrid energy system will be installed, the ambient temperature, solar radiation, wind speed, and load demands have been recorded in our database. Optimization algorithms were used to select the power values of the system components to be installed using these data in an efficient and inexpensive manner according to the ambient conditions. For optimal sizing of HRES components, gray wolf optimizer combined with cuckoo search (GWOCS) technique was investigated using MATLAB/Simulink. In this way, it has been tried to increase their efficiency by combining current optimization techniques. The cornerstone of our optimization efforts for both on-grid and off-grid models pivots on a constellation of critical decision variables: the power harvested from wind turbines, the productivity of solar panels, the capacity of battery storage, and the power contribution of diesel generators. In our pursuit of minimizing the annual cost metric, we employ a tailor-made function, meticulously upholding an array of constraints, such as the quotient of renewable energy and the potential risk of power disruption. A robust energy management system is integral to our design, orchestrating the delicate power flow balance among micro-grid components—vital for satisfying energy demand. Upon analyzing the outcomes of the study, it is apparent that the proposed Scenario 1 HRES effectively utilizes solar and battery components within the off-grid model, surpassing the efficiency of four other hybrid scenarios under consideration. Regarding optimization processes, the off-grid model exhibits superior results with the implementation of the GWOCS algorithm, delivering faster and more reliable solutions relative to other methodologies. Conversely, the optimization of the on-grid model reaches its optimal performance with the application of the cuckoo search algorithm. A comprehensive comparison from both technical and economic view points suggests the on-grid model as the most feasible and suitable choice. Upon completion of the optimization process, the load demand is catered to by a combination of a 2963.827-kW solar panel, a 201.8896-kW battery, and an additional purchase of 821.9 MWh from the grid. Additionally, an energy surplus sale of 1379.8 MWh to the grid culminates in an annual cost of system (ACS) of 475782.8240 USD, a total net present cost of 4815520.2794 USD, and a levelized cost of energy of 0.12754 USD/kWh. Solar panels cover the entire system, and the renewable energy fraction is 100%.

OPTIMAL SIZING OF SOLAR-WIND HYBRID MICROGRID USING IMPROVED GREY WOLF OPTIMIZATION ALGORITHM A CASE STUDY OF KADUNA - NIGERIA

This paper presents an improved grey wolf optimization algorithm (IGWOA) for optimal sizing of an isolated photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) hybrid microgrid. To demonstrate the effectiveness of the proposed approach, atmospheric data sets comprising of wind, solar, and temperature of Kaduna International Airport were collected from Nigerian Meteorological Agency while the load demand data was collected from Kaduna International Airport Electricity Distribution Center. The microgrid optimal sizing was formulated as a constrained single objective optimization problem. Constraints including, loss of power supply probability (LPSP), power balance, generation limits and battery state of charge (SOC) were imposed. Three simulation scenarios were considered. Firstly, the target allowable maximum LPSP was fixed at 25% and the algorithm was able to determine the optimal sizing of the hybrid microgrid components and minimize the initial cost from 169,880.00 USD to 112,356.40 USD per annum resulting in 34% savings in cost. Secondly, the effect of the target allowable maximum LPSP variation was investigated, and it was found that the total installed capacity of the system decreases with increase in LPSP thereby decreasing the total cost. Additionally, a novel electricity price index (EPI) was introduced in order to quantify the degree of optimality of the solution. The EPI was found to increase exponentially with increase in LPSP, resulting in an EPI of < 0.05USD/kWh at 20% LPSP. Lastly, to validate the proposed approach, a comparative analysis between the IGWOA and other algorithms was carried out, and the proposed IGWOA proved applicable.

Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer.

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.

Microgrid systems in U.S. energy infrastructure: A comprehensive review: Exploring decentralized energy solutions, their benefits, and challenges in regional implementation

This study presents a comprehensive review of microgrid systems within the U.S. energy infrastructure, focusing on decentralized energy solutions and their regional implementation. The primary objective is to explore the evolution, current state, and future prospects of microgrid technologies, assessing their technological, economic, and environmental impacts on regional energy infrastructures. Employing a systematic literature review methodology, the study synthesizes data from peer-reviewed journals, industry reports, and government publications. The search strategy involved keyword searches and manual screening, adhering to strict inclusion and exclusion criteria to ensure relevance and contemporary significance. Key findings reveal that microgrids are instrumental in enhancing energy security, integrating renewable energy sources, and providing economic benefits through decentralized solutions. Technological advancements in microgrid components and control systems have significantly improved efficiency and adaptability. However, challenges such as regulatory hurdles, technological integration, and financial constraints persist. The study concludes that microgrids represent a transformative solution for the future of energy systems, balancing reliability, sustainability, and economic viability. Strategic recommendations for industry and policymakers include developing clear regulatory frameworks, investing in advanced storage solutions, and incentivizing renewable energy integration. Future research directions emphasize enhancing microgrid interoperability with traditional grids, developing robust cybersecurity measures, and exploring innovative business models. In summary, microgrids stand at the forefront of revolutionizing the energy sector, offering a path towards a more resilient, sustainable, and equitable energy future, with ongoing advancements shaping the energy systems of tomorrow.

Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid

This article describes the development of an optimal and predictive energy management system (EMS) for a microgrid with a high photovoltaic (PV) power contribution. The EMS utilizes a predictive long-short-term memory (LSTM) neural network trained on real PV power and consumption data. Optimal EMS decisions focus on managing the state of charge (SoC) of the battery energy storage system (BESS) within defined limits and determining the optimal power contributions from the microgrid components. The simulation utilizes MATLAB R2023a to solve a mixed-integer optimization problem and HOMER Pro 3.14 to simulate the microgrid. The EMS solves this optimization problem for the current sampling time (t) and the immediate sampling time (t+1), which implies a prediction of one hour in advance. An upper-layer decision algorithm determines the operating state of the BESS, that is, to charge or discharge the batteries. An economic and technical impact analysis of our approach compared to two EMSs based on a pure economic optimization approach and a peak-shaving algorithm reveals superior BESS integration, achieving 59% in demand satisfaction without compromising the life of the equipment, avoiding inexpedient power delivery, and preventing significant increases in operating costs.

A control strategy incorporating multiple EV charging stations for an islanded micro‐grid energy management and voltage regulation

AbstractAn electric vehicle (EV) charging station control strategy (CSCS) is proposed for proper power‐sharing among various components of an islanded microgrid (MG), consisting of the photovoltaic (PV) system, the wind energy conversion system (WECS), variable load (critical and non‐critical), multiple charging stations (CSs) and the dump load (LD). The proposed control strategy is examined in three distinct cases with varying operating conditions owing to solar irradiance, wind speed and load variations. For proper energy management (EM) and voltage regulation (VR), the diverse EVs have varying battery capacities parked in the multiple CSs to either absorb or deliver power through a two‐way power flow between the islanded MG system and the EV‐based CSs, which is reliant on the supply and demand (S&D) energy difference. The simulation is executed via the MATLAB software and two different modes—excess power mode (EPM) and deficient power mode (DPM)—are employed to validate the proposed CSCS's competency. For a realistic assessment of the proposition, the results are also verified by using a hardware‐in‐loop (HIL) real‐time (RT) OPAL‐RT simulator (OP5700). Furthermore, the findings from MATLAB and the OPAL‐RT simulator are compared to establish the robustness of the proposed strategy.

Integrated Control and Energy Flow Management for Hybrid Grid-Connected Photovoltaic/Wind Systems with Battery Storage Using Fuzzy Logic Controllers

This paper focuses on the control and management of an energy conversion system within a microgrid setup comprising multiple renewable energy sources and a storage system, all connected to the AC-grid through DC/DC, AC/DC, and DC/AC converters. Various control objectives are defined, which depend on factors such as energy availability, load demand, storage system state of charge, and grid availability. To address these scenarios, an energy flow management system is developed in this work. The primary roles of this management algorithm include ensuring the safety of all microgrid components, enhancing the storage system’s lifespan, and improving energy efficiency. To achieve these objectives, multiple control loops are designed based on fuzzy logic controllers ensuring appropriate system operation modes. The effectiveness of this control and energy management strategy is evaluated using the MATLAB/Simulink/Simpower environment, demonstrating its capability to meet the specified objectives.