Microgrid System Research Articles

Sustainable PV-hydrogen-storage microgrid energy management using a hierarchical economic model predictive control framework

Hydrogen-based renewable microgrid is considered as a prospective technique in power generation to reduce the carbon footprint, combat climate change and promote renewable energy sources integration. The photovoltaic-hydrogen-storage (PHS) microgrid system cleverly integrates renewable clean energy and hydrogen storage, providing a sustainable solution that maximizes the solar energy utilization. However, the changeable weather conditions and fluid market make it challenging to manage energy balance of the system. Moreover, in view of the fact that the existing energy management systems often ignore the dynamic synergy of microgrids, a hierarchical economic model predictive control (HEMPC) framework is proposed to realize the optimal operation of PHS microgrid. First, a precise nonlinear model of the PHS microgrid is established and the logic variables are introduced to capture the hydrogen devices’ short-term properties, i.e., the start-up/shut-down of electrolyzers and fuel cells. Then a comprehensive economic cost function, including internal power demand tracking cost, system operation cost and contract deviation cost, is considered in the proposed two-level HEMPC framework in order to address challenges such as fluctuating weather conditions, dynamic market environments, and the often-overlooked dynamic synergy of microgrid components. Under the proposed framework, a mixed-integer nonlinear optimization problem is solved by the long-term EMPC in the upper level to regulate the start-up/shut-down of hydrogen devices and the state of charge in the battery, and the short-term EMPC in the lower level reoptimizes the power demand tracking cost while tracking the optimal reference signal from the long-term EMPC, thereby improving overall control system efficiency. The simulation results along with qualitative and quantitative analysis show that compared with rule-based control, the proposed HEMPC is effective in managing the equipment power output, realizing dynamic synergy and enhancing the economic performance.

Smart Microgrid : Power Monitoring And Management System With Self-Healing Ability

This research presents a smart microgrid power monitoring and management system with self-healing ability. Solar power plants have been integrated into the microgrid, which previously relied on DG and PLN power supply. However, the system’s monitoring and control are limited. This study develops an effective, efficient and reliable smart microgrid using integration of online monitoring and control system, power management system and self-healing ability. Online monitoring and control system is effectively to monitor and control the microgrid, can detect and analyze potential fault on grid at ease. Power management system is necessary to enhance efficiency and reliability to optimize power distribution. Then, with self-healing ability, enable to detect, to prevent, to isolate and to recover fault on grid automatically, make the smart microgrids more efficient and reliable. Through the Siemens TIA Portal, the study compared the simulation results of a conventional microgrid system with a smart microgrid system incorporate with online monitoring and control system, power management system, self-healing ability. Using some fault scenarios, MTTD value, MTTR value and Availability Rate value are obtained. As result, the applied smart microgrid system decrease the duration to detect and to recover fault on microgrid efficiently, thus the reliability also increased significantly.

Maximum Power Point Tracking Enhancement for PV in Microgrids Systems Using Dual Artificial Neural Networks to Estimate Solar Irradiance and Temperature

Maximum Power Point Tracking Enhancement for PV in Microgrids Systems Using Dual Artificial Neural Networks to Estimate Solar Irradiance and Temperature

A battery degradation-aware energy management system for agricultural microgrids

A battery degradation-aware energy management system for agricultural microgrids

An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model

Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energy schedules once operating in grid-connected mode, such systems are vulnerable to malicious attacks from the viewpoint of cybersecurity. With this in mind, this paper explores a novel advanced attack model named the false transferred data injection (FTDI) attack aiming to manipulatively alter the power flowing from the microgrid to the upstream grid to raise voltage usability probability. One crucial piece of information that the model uses to change the system and cause the greatest amount of damage while concealing the attacker’s view is the voltage stability index. Saying that the power transaction between the microgrid and the upstream grid is within the broad scope of bilateral exchange at any given moment is noteworthy. Put otherwise, with respect to the FTDI assault, the microgrid’s power direction is just as significant to the detection system as the transferred power value. Therefore, once the microgrid is running in the grid-connected mode, the false data detector needs to concurrently detect changes in the value and direction of power. To overcome this problem, the paper presents a learning generative network model, based on the generative adversarial network (GAN) paradigm, to recognize the change in probability values that is maliciously aimed. To this end, a studied microgrid system including the wind turbine, photovoltaic, storage, tidal turbine, and fuel cell units is performed on the tested 24-bus IEEE grid to satisfy the local load demands. Comparative analysis indicates notable gains, such as scores of 0.95%, 0.92%, 0.7%, and 10% for the Hit rate, C.R. rate, F.A. rate, and Miss rate in order to evaluate the GAN-based detection model within the microgrid.

Optimized Multi‐Scale Attention Convolutional Neural Network for Micro‐Grid Energy Management System Employing in Internet of Things

ABSTRACTThe combination of micro‐grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi‐scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN‐MGEMS‐IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias‐aware Ensemble Kalman Filter (RBAEKF) for pre‐processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN‐MGEMS‐IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi‐micro grid systems (IoT‐PEM‐MMS), a micro‐grid system infrastructure implementing IoT for efficient energy management in buildings (MSII‐IoT‐EEM) and a hybrid deep learning‐based online energy management scheme for industrial microgrids (HDL‐OEM‐IM). The findings highlight the impact of the proposed OMACNN‐MGEMS‐IoT method in enhancing energy efficiency and cost‐effectiveness in microgrid systems.

Optimization of Low-Carbon Operation in a Combined Electrical, Thermal, and Cooling Integrated Energy System with Liquid Carbon Dioxide Energy Storage and Green Certificate and Carbon Trading Mechanisms

The liquid carbon dioxide energy storage system (LCES), as a highly flexible, long-lasting, and environmentally friendly energy storage technology, shows great potential for application in integrated energy systems. However, research on the combined cooling, heating, and power supply using LCES in integrated energy systems is still limited. In this paper, an optimized scheduling scheme for a low-carbon economic integrated energy system is proposed, coupling LCES with power-to-gas (P2G) technology and the green certificate/carbon trading mechanism. Mathematical models and constraints for each system component are developed, and an optimization scheduling model is constructed, focusing on the economic and low-carbon operation of the integrated energy microgrid system. The objective function aims to minimize total system costs. A case study based on a northern China park is conducted, with seven scenarios set for comparative optimization analysis. The results demonstrate that the use of the combined cooling, heating, and power LCES system reduces total costs by USD 2,706.85 and carbon emissions by 34.57% compared to the single-energy flow operation. These findings validate the effectiveness of the proposed model in optimizing system costs and reducing carbon emissions.

Navigating the Intersection of Microgrids and Hydrogen: Evolutionary Trends, Challenges, and Future Strategies

Growing interest in sustainable energy has gathered significant attention for alternative technologies, with hydrogen-based solutions emerging as a crucial component in the transition to cleaner and more resilient energy systems. Following that, hydrogen-based microgrids, integrated with renewable energy sources including wind and solar, have gained substantial attention as an upcoming pathway toward long-term energy sustainability. Hydrogen, produced through processes such as electrolysis and steam methane reforming, can be stored in various forms including compressed gas, liquid, or solid-state hydrides, and later utilized for electricity generation through fuel cells and gas turbines. This dynamic energy system offers highly flexible, scalable, and resilient solutions for various applications. Specifically, hydrogen-based microgrids are particularly suitable for offshore and islanded applications, with geographical factors, adverse environmental conditions, and limited access to conventional energy solutions. This is critical for energy independence, long-term storage capacity, and grid stability. This review explores topological and functional-based classifications of microgrids, advancements in hydrogen generation, storage, and utilization technologies, and their integration with microgrid systems. It also critically evaluates the key challenges of each technology, including cost, efficiency, and scalability, which impact the feasibility of hydrogen microgrids.

Integrating hybrid artificial ecosystem with P&O MPPT for enhanced fuel cell performance in microgrid systems

Integrating hybrid artificial ecosystem with P&O MPPT for enhanced fuel cell performance in microgrid systems

Consensus control for heterogeneous battery energy storage systems in AC microgrids without power measurements

Consensus control for heterogeneous battery energy storage systems in AC microgrids without power measurements

Optimal Scheduling of the Microgrid Based on the Dynamic Characteristics of the Natural Gas Pipeline Network and the Thermal Network Along with P2G-CCS

In the power system, the integration of power-to-gas and carbon capture systems (P2G-CCS) within the microgrid enables the conversion of electrical energy into hydrogen or methane while simultaneously capturing CO2 emissions from power generation units. This approach significantly mitigates carbon emissions and supports the transition to a low-carbon energy system. Concurrently, the dynamic properties of the gas–thermal network exert a critical influence on the flexibility of system scheduling and the regulation of multi-energy coupling. Hence, this paper puts forward an optimal configuration strategy for microgrids with consideration of the dynamic characteristics of the gas–thermal network. Firstly, mathematical models for the dynamic characteristics of the gas network and the heat network were established and incorporated into the microgrid system. Secondly, in conjunction with the P2G-CCS coupling system, an optimization scheduling strategy was formulated with the aim of minimizing the total operational costs of the power grid, the natural gas network, and the heat network. An enhanced African vultures optimization algorithm (AVOA) was put forward. In the end, by setting different scheduling scenarios for conducting a comparative analysis, an appropriate optimization configuration scheme was selected, and the validity of the proposed method was verified through simulation with the improved case study.

A Rule-Based Modular Energy Management System for AC/DC Hybrid Microgrids

Microgrids are considered a practical solution to revolutionize power systems due to their ability to island and sustain the penetration of renewables. Most existing studies have focused on the optimal management of microgrids with a fixed configuration. This restricts the application of developed algorithms to other configurations without major modifications. The objective of this study is to design a rule-based modular energy management system (EMS) for microgrids that can dynamically adapt to the microgrid configuration. To realize this framework, first, each component is modeled as a separate entity with its constraints and bounds for variables. A wide range of components such as battery energy storage systems (BESSs), electric vehicles (EVs), solar photovoltaic (PV), microturbines (MTs), and different priority loads are modeled as modules. Then, a rule-based system is designed to analyze the impact of the presence/absence of one module on the others and update constraints. For example, load shedding and PV curtailment can be permitted if the grid module is not included. The constraints of microgrid components present in any given configuration are communicated to the EMS, and it optimizes the operation of the available components. The configuration of microgrids could be as simple as flexible loads operating in grid-connected mode or as complex as a hybrid microgrid with AC and DC buses with a diverse range of equipment on each side. To facilitate the realization of diverse configurations, a hybrid AC/DC microgrid is considered where the utility grid and interlinking converter (ILC) are also modeled as separate modules. The proposed method is used to test performance in both grid-connected and islanded modes by simulating four typical configurations in each case. Simulation results have shown that the proposed rule-based modular method can optimize the operation of a wide range of microgrid configurations.

Full-bridge DC-DC Converter based on Linear Active Disturbance Rejection Control

Dual Active Bridge (DAB) converter has been a research hotspot in recent years. It is widely used in distributed power generation system, DC microgrid system and power management system. Traditional PI controllers do not perform well in the face of nonlinear loads and external disturbances, so a more advanced control method is needed to improve the stability and performance of the system. In this paper, a control strategy based on active disturbance rejection control (ADRC) is proposed to improve the control performance of full-bridge DC-DC converter. First of all, the small signal mathematical model of full-bridge DC-DC converter is established, and the function relationship between input, output, control quantity and disturbance quantity in the system is obtained. Then, the extended state observer of ADRC core module is designed, and its parameters are adjusted and simulated, compared with the traditional PI controller. The simulation results show that the full-bridge DC-DC converter based on LADRC has faster dynamic response and better disturbance immunity, which can effectively improve the stability and robustness of the system.

Study on the Optimal Allocation of Supercapacitor Capacity for Oil Drilling Winch Energy Recovery Microgrid System

The application of supercapacitor energy storage device in the microgrid system of ultra-deep well oil drilling rig can effectively recover the regenerative energy generated when the winch lowers the drilling tools. Firstly, a microgrid system model of super capacitor energy recovery for ultra-deep well oil drilling rigs was established; the theoretical recoverable energy during a complete operation of ultra-deep well drilling rigs was calculated; the economic efficiency of energy recovery was calculated by taking into consideration of a variety of economic factors, such as storage cost, pollutant emission reduction benefit, and storage loss, etc. was taken into account as the objective function of energy management and capacity allocation optimisation of super capacitor energy storage devices. Using genetic algorithm to solve the supercapacitor capacity configuration, when the well depth is 9000 m, the economic efficiency can reach 26.4% by using 5×377 supercapacitor combination; when the well depth is 12000 m, the economic efficiency can reach 32.7% by using 5×650 supercapacitor combination. It provides a corresponding reference to help improve the economic efficiency of the microgrid system for energy recovery of oil drilling rigs in ultra-deep wells.

Research on the Sustainability Strategy of Cogeneration Microgrids Based on Supply-Demand Synergy

With the continuous adjustment of energy structure and the improvement of environmental protection requirements, combined heat and power microgrids (CHP-MG) have received widespread attention as an efficient and economical way of utilizing energy. The complexity of energy supply relationships and energy coupling within the microgrid system necessitates optimizing the power output of each equipment unit. In this paper, an optimization strategy for a multi-energy microgrid system is proposed based on the efficient energy supply of cogeneration microgrids: decoupling the thermoelectric connection by using the energy storage equipment on the supply side, utilizing the flexibility of the electrical loads and the diversity of the system’s heating methods, and reducing the electrical loads and changing the selection of the heating methods on the demand side. The optimization model in the paper is mainly based on mixed-integer linear programming and demand-side management theory, which simulates the system operation under different scenarios so as to find the optimal equipment output and load management strategies. Simulation results show that the optimized CHP-MG system can ensure a reliable power supply while effectively reducing operating costs, improving energy utilization and promoting sustainable operation of the energy system. The optimized microgrid system offers significant advantages in terms of economic efficiency and energy management when compared to conventional CHP systems. These findings provide actionable insights for policymakers, system operators, and researchers aimed at driving the development of efficient and sustainable energy management solutions.

Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method—Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models—exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)—are developed, each reflecting a different price elasticity of demand. These models are integrated with a flexible elasticity matrix to assess the dynamic consumer response to fluctuating electricity prices. The study evaluates four operational scenarios, focusing on grid participation, DER utilization, and the impact of real-time pricing (RTP), time of use (TOU), and critical peak pricing strategies. Quantitative results demonstrate the significant cost-saving potential of integrating DRPs with microgrid operations. In the optimal scenario, the GRSO achieved a minimum generation cost of 882¥ for the base load profile. Further, when critical peak pricing (CPP) was applied, the generation cost was reduced to 746¥, representing a 15.4% reduction. For a scenario where the grid’s participation was limited, the logarithmic-based demand response model decreased the generation cost to 817¥, while full grid interaction led to higher cost reductions. Additionally, our results show a significant reduction in peak load, with load factor improvements of up to 87.7% across the studied demand profiles. Furthermore, limiting the grid’s upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in terms of both execution time and convergence, making it a viable solution for real-time microgrid optimization. In conclusion, the proposed GRSO-based framework provides an efficient approach for microgrid cost minimization, achieving up to a 15.4% reduction in operational costs and notable environmental benefits by reducing emissions. This study highlights the importance of dynamic demand response strategies and grid participation for sustainable and cost-effective microgrid management.

Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Adaptive Energy Management Strategy for Sustainable xEV Charging Stations in Hybrid Microgrid Architecture

Integrating electric vehicles (EVs) into power grids presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. The system dynamically optimizes the coordination of renewable energy sources solar photovoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.

Integrated Energy Optimization in Manufacturing through Multiagent Deep Reinforcement Learning: Holistic Control of Manufacturing, Microgrid Systems, and Battery Storage

Integrated Energy Optimization in Manufacturing through Multiagent Deep Reinforcement Learning: Holistic Control of Manufacturing, Microgrid Systems, and Battery Storage

An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system.

The cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal. An incentive-based demand response (IBDR) is introduced in the proposed work to close this gap and promote load curtailment during peak hours. IBDR policy rewards participant customers with incentives for load curtailment which in turn lowers emissions and generation costs. Furthermore, a trade-off approach ensures both environmental and economic sustainability by striking a balance between cost reduction and emission reduction. Considering the fact in view that the 30-40% of the microgrid customers are willing to participate in the IBDR program, six different scenarios that have been analysed, each of which involves various levels of grid participation and different approaches to pricing in the electricity market. These scenarios also include the implementation of demand response programmes. Differential evolution algorithm was used as the optimization tool for the study. The results achieved for all the scenarios demonstrate the suitability and effectiveness of implementing the suggested IBDR strategy in terms of cost savings. According to numerical results reported, the generating cost decreased by 10-13% with the inclusion of IBDR. Additionally, a 6-8% reduction in peak and 4-5% improvement in load factor was also realised as a positive impact of the IBDR policy. The weighted economic emission dispatch algorithm offered a balanced solution that considered both the minimum generation cost and emissions for various load models in the microgrid system.