Microgrid Energy Management System Research Articles

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25% and 50% in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1%, 52.2%, and 20.4% in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51%, 11%, and 5.22%, respectively.

Power and Energy Management System of a Lunar Microgrid—Part II: Optimal Sizing and Operation of ISRU

Power and Energy Management System of a Lunar Microgrid—Part II: Optimal Sizing and Operation of ISRU

Power and Energy Management System of a Lunar Microgrid—Part I: Modeling Power Demand of ISRU

Power and Energy Management System of a Lunar Microgrid—Part I: Modeling Power Demand of ISRU

Efficient microgrid energy management with neural-fuzzy optimization

This research introduces a pioneering Energy Management System (EMS) for microgrids, integrating fuzzy neural networks and a modified particle swarm optimization (MPSO) algorithm. The key contribution lies in minimizing production costs while optimizing the use of renewable sources like photovoltaic (PV), wind turbines (WT), and energy storage. The novel approach considers time-dependent constraints, ensuring adaptability and superior system performance. Additionally, the study introduces an innovative demand response (DR) analysis using a neural-fuzzy network, enhancing customer response and energy cost dynamics. The MPSO algorithm addresses economic load distribution challenges, demonstrating superior performance in comparative analysis. This integrated approach offers a groundbreaking solution for sustainable and efficient energy planning in microgrids. The analysis demonstrates that the proposed method achieves higher energy savings (83%) compared to baseline levels of 72%, showcasing its superior efficiency. Comparative analysis with genetic and particle swarm optimization algorithms reveals consistently lower average expenses and increased cost-effectiveness with the proposed approach.

Optimal management of microgrid energy by considering demand side management plan and maintenance cost with developed particle swarm algorithm

This paper introduces a pioneering approach to power system optimization by integrating microgrids and demand-side management, with a focus on its novel contributions. Emphasizing cost reduction, the study targets effective demand management through load shifting. A unique microgrid energy management system is proposed for peak-shaving, leveraging the cost advantages of lower controllable unit expenses. The system actively involves microgrid units in responsive load management, reducing peak consumption during high-demand periods. Evaluation of various demand-side management techniques, especially peak-time rebate programs, is conducted through presented indices measuring the energy management solution's effectiveness. To address the complexity, a particle swarm-based algorithm is introduced for computational efficiency. Validation of the proposed method and model in diverse scenarios showcases significant cost reduction, demonstrating the practicality and innovation of the integrated microgrid and demand-side management strategy. The study highlights a substantial reduction in peak load through load response program participation. At a 20 % participation rate with a 10-cent incentive, there's a significant decrease in peak load during specific hours. With increasing participation and incentives, peak load reduction intensifies, reaching optimal results at a 40 % participation rate and a 20-cent incentive.

Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm

In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient , symmetric mean absolute percentage error, and agreement index (d).

Review of Energy Management Systems in Microgrids

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple distributed generation resources with different features are used in microgrids, managing these resources becomes an important problem. The generated power of solar photovoltaic modules and wind turbines used in microgrids is constantly changing with solar irradiation and wind speed. Due to this impermanent and uncertain nature of renewable energy resources, generally, energy storage systems are employed in microgrid systems. To control the distributed energy resources and energy storage units and sustain the supply and demand balance within the microgrid and provide sustainable and reliable energy to the loads, energy management systems are used. Many methods are used to realize and optimize energy management in microgrids. This review article provides a comparative and critical analysis of the energy management systems used in microgrids. The energy management system can be tailored for different purposes, which are also discussed in detail. Additionally, various uncertainty measurement methods are summarized to manage the variability and intermittency of renewable energy sources and load demand. Finally, some thoughts about potential future directions and practical applications are given.

Supply energy management methods for a direct current microgrid: A comprehensive review

AbstractThe revolution of microgrids (MGs) in the modern world has impacted the power systems to deal with the loads with optimal use of renewable energy resources (RERs). In the models consisting of energy management systems (EMS) and generation sources, there will be power fluctuations between the source and the load under certain conditions due to the unpredictable behavior of RERs. Therefore, a reliable scheme has to be implemented between the source and the load to ensure optimal performance of direct current (DC)‐MGs. This article has presented a review of the EMS models introduced by various researchers to enable optimal power supply and efficient operation of the DC‐MGs. A thorough illustration of the schemes adopted to provide efficient operations under grid‐connected and islanded mode of operations are provided. The effects and advantages of centralized and decentralized EMSs are discussed. The role of a battery energy storage system (BESS) in microgrid scheduling is presented and compared with a few other energy storage systems. The scheduling strategies implemented for optimal EMS using different algorithms and techniques are elaborated. In addition, the effects of battery degradation and the formulated solutions to improve the degraded models with improved battery life are also provided with cycle life and calendar life. Then, the lithium ion and lead acid BESS effect on microgrid EMS is presented. Finally, the energy management methods for DC‐MGs end with challenges, research gaps, and future direction.

Energy Management Systems for Microgrids: Evolution and Challenges within the Framework of the Energy Transition

Context: Microgrids have been gaining space and credibility in terms of research and real applications. Technological maturity and new regulations have allowed these types of systems to position themselves as a real alternative to increase the coverage of the energy service and improve its quality. One of the biggest challenges of microgrids is the management of resources and their synchronization with conventional grids. In order to overcome the inconvenience of synchronizing and managing the components of a microgrid, research on management systems has been conducted, which usually consist of a set of modules and control strategies that manage the available resources. However, these studies have not reached unanimity on the best method to perform these tasks, which is why it is necessary to perform a systematic collection of information and clearly define the state of research in energy systems management for this type of network. Method: Based on the above, a systematic mapping was carried out in this article, wherein a significant number of papers that have contributed to this area were compiled. Taxonomies were generated based on the nature of the variables collected. These variables correspond to the data or information that enters and/or leaves the microgrid management system, such as meteorological variables, power, priority loads, intelligent loads, economic, operating states, and binary outputs. Conclusions: It was observed that, despite the advances in studying different techniques and strategies microgird control and management, other factors that may affect performance have not been covered in a relevant way, such as the nature of variables and microgrid topology, among others.

Improved Energy Management System for Wind Power-Based Microgrid with EV Charge Station

This study aims to peak power shaving and reduce the cost of energy by using improved energy management system (EMS) in a microgrid. This study has three scenarios. In the first scenario, the EV charging station and the wind turbine operate standalone. In the second scenario, a microgrid structure including an energy storage system (ESS) and an EMS is established. The threshold algorithm has been used for the energy management. In the last scenario, fuzzy logic-based EMS is used for energy management. The impact of EV charge station and wind turbine on the grid and the cost of energy are analyzed for three scenarios. The results show that the use of microgrid-connected ESS significantly reduces the cost of energy and creates a more balanced load for the grid side. It is also proved that the EMS affects the energy losses. It is revealed that when the proposed fuzzy logic-based EMS is used, the energy cost is reduced by 78% and the peak power is reduced by 70%. Thus, it is seen that the proposed EMS successfully reduces both the cost of energy and the negative impact of EVs on the grid by balancing grid side load.

State-of-the-art review on energy management systems, challenges and top trends of renewable energy based microgrids

Due to the increasing demand for electrical energy worldwide and environmental concerns, modern power systems are looking for a fundamental change. These changes include reducing dependence on the primary electricity grid and using renewable energy sources on a large scale. The emergence of microgrids in electrical energy systems will improve the level of these systems due to technical, economic, and environmental benefits. In this research work, the authors have conducted extensive studies on control methods, types of power sources, and the size of microgrids and analyzed them in tabular form. In addition, the review of communication technologies and standards in microgrids, as well as the review of microgrid energy management systems to optimize the efficiency of microgrids, is one of the main goals of the authors in this article. Also, in this article, the top 10 trends of microgrids in 2023 have been examined to increase the flexibility of network infrastructure, which helps readers to improve their strategic decisions by providing an overview of emerging technologies in the energy industry.

Intelligent energy management system of a smart microgrid using multiagent systems

Intelligent energy management system of a smart microgrid using multiagent systems

A Modified Redundancy-Based Energy Management System for Microgrids: An SoC Enhancement Approach

A Modified Redundancy-Based Energy Management System for Microgrids: An SoC Enhancement Approach

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

A Rule-Based Bionic Energy Management System for Grid-Connected Community Microgrid Using Peer-to-Peer Trading With Rapid Settlement

A Rule-Based Bionic Energy Management System for Grid-Connected Community Microgrid Using Peer-to-Peer Trading With Rapid Settlement

Application of IoT and Non-Intrusive Load Monitoring Techniques in Microgrid Energy Management and Monitoring Systems

Application of IoT and Non-Intrusive Load Monitoring Techniques in Microgrid Energy Management and Monitoring Systems

Energy Management System in Smart Micro-Grid

This paper focuses on discussing an energy management system (EMS) for a smart microgrid integrating multiple renewable sources. The task of the EMS is to efficiently balance power generation and consumption by controlling various energy sources, including photovoltaic systems, energy storage units, engine generator set and the utility grid. An EMS optimizes power flow between the microgrid components and keeps the micro-grid stable, by using different control strategies. In this microgrid, the PV system serves as the primary energy source, while the other sources of electrical energy act as backups. The energy management system prioritizes supplying load demand from either the PV system or the other sources, with load shedding during peak hours if the supplied energy is insufficient. The approaches based on StateMachine and StateFlow are discussed in this paper for enhancing the energy management system's performance.

CuEMS: Deep reinforcement learning for community control of energy management systems in microgrids

Nowadays, energy management assumes a pivotal role in ensuring the stable operation of microgrids, facilitating enhanced monitoring, analysis, and optimization of energy utilization for end-users. The implementation of an energy management system (EMS) offers invaluable assistance to microgrid users, enabling them to cut electricity costs, ensure reliability, and provide an elevated level of comfort. However, as the scope of microgrid management proliferates, balancing between the comfort of community microgrid users with more devices and their profitability becomes increasingly challenging. To address this issue, this paper proposes a novel community control approach for EMS, called CuEMS, that utilizes deep reinforcement learning (DRL) to manage multiple appliances and energy storage systems (ESSs). The proposed approach optimizes energy profitability for operators while taking into account user comfort. It reformulates energy scheduling as a Markov decision process and learns the optimal scheduling strategy through deep Q-networks. The presented CuEMS approach is evaluated using real world data from Finland, and the results demonstrate that it can optimize the operation of multiple appliances and improve the profitability and user comfort compared to other methods.

Multi-agent-based real-time operation of microgrids employing plug-in electric vehicles and building prosumers

Real-time operation of microgrids holds significant importance due to its ability to enhance energy resilience and efficiency. Real-time control in conjunction with continuous monitoring and energy supply and demand fluctuations adaptation, ensures a stable and reliable power supply for local communities, even during grid disturbances or outages. Moreover, real-time operation supports demand response mechanisms, enabling users to actively manage their energy consumption and costs. Overall, it plays a pivotal role in modernizing and future-proofing contemporary energy infrastructure, making it more sustainable. In this paper, an efficient, easily applied multi-agent-based real-time energy management and control system for microgrids employing Plug-In Electric Vehicles (PEVs) and building prosumers is developed. The primary goal of this proposed approach is to determine the optimal power set-points for all microgrid components with the objective to reduce the overall daily operation expenses under fluctuating electricity price, while simultaneously satisfying a wide range of constraints. Well-defined flexibilities of all microgrid components to adjust their power are harnessed to execute optimal power dispatch. The implementation of the proposed method resulted in a noteworthy cost reduction of 14.3% when contrasted with a typical operation of the microgrid.