Microgrid Energy Management System Research Articles

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

An overview of AC and DC microgrid energy management systems

<abstract> <p>In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing.</p> </abstract>

Microgrid energy management and monitoring systems: A comprehensive review

Microgrid (MG) technologies offer users attractive characteristics such as enhanced power quality, stability, sustainability, and environmentally friendly energy through a control and Energy Management System (EMS). Microgrids are enabled by integrating such distributed energy sources into the utility grid. The microgrid concept is proposed to create a self-contained system composed of distributed energy resources capable of operating in an isolated mode during grid disruptions. With the Internet of Things (IoT) daily technological advancements and updates, intelligent microgrids, the critical components of the future smart grid, are integrating an increasing number of IoT architectures and technologies for applications aimed at developing, controlling, monitoring, and protecting microgrids. Microgrids are composed of various distributed generators (DG), which may include renewable and non-renewable energy sources. As a result, a proper control strategy and monitoring system must guarantee that MG power is transferred efficiently to sensitive loads and the primary grid. This paper evaluates MG control strategies in detail and classifies them according to their level of protection, energy conversion, integration, benefits, and drawbacks. This paper also shows the role of the IoT and monitoring systems for energy management and data analysis in the microgrid. Additionally, this analysis highlights numerous elements, obstacles, and issues regarding the long-term development of MG control technologies in next-generation intelligent grid applications. This paper can be used as a reference for all new microgrid energy management and monitoring research.

Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning

With the development of microgrids (MGs), an energy management system (EMS) is required to ensure the stable and economically efficient operation of the MG system. In this paper, an intelligent EMS is proposed by exploiting the deep reinforcement learning (DRL) technique. DRL is employed as the effective method for handling the computation hardness of optimal scheduling of the charge/discharge of battery energy storage in the MG EMS. Since the optimal decision for charge/discharge of the battery depends on its state of charge given from the consecutive time steps, it demands a full-time horizon scheduling to obtain the optimum solution. This, however, increases the time complexity of the EMS and turns it into an NP-hard problem. By considering the energy storage system’s charging/discharging power as the control variable, the DRL agent is trained to investigate the best energy storage control method for both deterministic and stochastic weather scenarios. The efficiency of the strategy suggested in this study in minimizing the cost of purchasing energy is also shown from a quantitative perspective through programming verification and comparison with the results of mixed integer programming and the heuristic genetic algorithm (GA).

Optimal Control of Microgrid Integrated with PV, WT and BESS

Distributed renewable sources (DES) are widely deployed to satisfy the requirements of increasing energy demand, slowing down the consumption traditional energy and reducing the side effect on environment. However, with the increasing amount of DES connected into the microgrid, the stability, robustness and economy of grid will encounter with some challenges. So, this paper will discuss the energy management system (EMS) of microgrid integrated with wind turbine (WT) system, photovoltaics (PV) system and battery energy storage systems (BESS) to realize the stability, enhance the robustness and reduce the cost of microgrid. This paper first reviews the current relative works. Secondly, the mathematical model of microgrid is constructed which is followed by the different microgrid control strategies. Then, the simulation with results is provided.

A distributed real-time energy management system for inverter-based microgrids

A novel distributed real-time energy management strategy for inverter-based microgrids based on dynamic algorithms with a low computational burden and without the requirement of offline forecast or a central optimizer is presented in this paper. A dynamic economic dispatch problem is solved while a stability performance is guaranteed. The algorithm is able to dynamically adjust the power references under sudden load changes, generator disconnection, and communication failures. In steady-state, the proposed real-time energy management strategy guarantees optimality conditions and frequency regulation. Finally, the effectiveness of the proposed strategy and its compatibility with the conventional proportional-resonant inner controllers and primary droop controllers are validated in experiments. • An experimental setup of a distributed real-time energy management for microgrids. • Stability and optimal power tracking are guaranteed under several failure scenarios. • Compatibility with the proportional-resonant and droop controllers are validated.

Critical Reliability Improvement Using Q-Learning-Based Energy Management System for Microgrids

This paper presents a power distribution system that prioritizes the reliability of power to critical loads within a community. The proposed system utilizes reinforcement learning methods (Q-learning) to train multi-port power electronic interface (MPEI) systems within a community of microgrids. The primary contributions of this article are to present a system where Q-learning is successfully integrated with MPEI to reduce the impact of power contingencies on critical loads and to explore the effectiveness of the subsequent system. The feasibility of the proposed method has been proven through simulation and experiments. It has been demonstrated that the proposed method can effectively improve the reliability of the local power system—for a case study where 20% of the total loads are classified as critical loads, the system average interruption duration index (SAIDI) has been improved by 75% compared to traditional microgrids with no load schedule.

Microgrid Energy Management and Methods for Managing Forecast Uncertainties

The rising demand for electricity, economic benefits, and environmental pressures related to the use of fossil fuels are driving electricity generation mostly from renewable energy sources. One of the main challenges in renewable energy generation is uncertainty involved in forecasting because of the intermittent nature of renewable sources. The demand also varies according to the time of day, the season, the location, the climate, and the availability of resources. Microgrids offer a potential solution for the integration of small-scale renewable energy sources and loads along with energy storage systems and other non-renewable sources. However, intermittent generation and varying demand need to be matched to provide stable power to consumers. Therefore, it is crucial to design an energy management system to effectively manage the energy sources and supply loads for reliable and efficient operation. This paper reviews different techniques proposed in the literature to achieve the objectives of a microgrid energy management system. The benefits of existing energy management systems and their challenges are also discussed. The challenges associated with uncertainties and methods to overcome them are critically reviewed.

Solar photovoltaic power forecasting for microgrid energy management system using an ensemble forecasting strategy

ABSTRACT Solar photovoltaic (PV) power production is always unpredictable due to intermittent solar irradiance and weather parameters which pose a major challenge in microgrid energy management systems (M-EMS). An accurate solar PV power forecasting strategy is therefore important for the competent M-EMS that can lead to financial gains for customers and the power industry. In this paper, an ensemble forecasting strategy has been proposed to predict the solar PV power for M-EMS based on the combination of four different forecasting methods, i.e. tunicate swarm algorithm (TSA)-based least-square support vector machine (LSSVM), TSA-based multilayer perceptron neural network (TSA-MLPNN), whales optimization algorithm (WOA)-based LSSVM, and WOA-based MLPNN (WOA-MLPNN). The output of each forecasting model is aggregated through the Bayesian model averaging (BMA) method. The historical data of solar PV power and influencing factors obtained from the N-78 Griffith University Nathan campus have been employed to analyze and verify the performance of the proposed strategy. The simulation is carried out using MATLAB-2018 and the comparison of proposed ensemble strategy has been drawn with several competitive strategies. The simulation results indicate that the proposed forecasting strategy minimizes the root means square error by 29%, 33%, 27%, 33%, 58%, 12%, 26% and 43% in comparison with TSA-MLPNN, TSA-LSSVM, particle swarm optimization (PSO)-based artificial neural network (ANN), PSO-LSSVM, backpropagation neural network, ensemble strategy-1, ensemble strategy-2, and ensemble strategy-3, respectively.

Review of Recent Developments in Microgrid Energy Management Strategies

The grid integration of microgrids and the selection of energy management systems (EMS) based on robustness and energy efficiency in terms of generation, storage, and distribution are becoming more challenging with rising electrical power demand. The problems regarding exploring renewable energy resources with efficient and durable energy storage systems demand side management and sustainable solutions to microgrid development to maintain the power system’s stability and security. This article mainly focuses on the overview of the recent developments of microgrid EMS within the control strategies and the implementation challenges of the microgrid. First, it provides energy management strategies for the major microgrid components, including load, generation, and energy storage systems. Then, it presents the different optimization approaches employed for microgrid energy management, such as classical, metaheuristic, and artificial intelligence. Moreover, this article sheds light on the major implementation challenges of microgrids. Overall, this article provides interactive guidelines for researchers to assist them in deciding on their future research.

Design of Graphical User Interface for Artificial Intelligence-Based Energy Management System for Microgrids

Design of Graphical User Interface for Artificial Intelligence-Based Energy Management System for Microgrids

Recent Developments and Trends in Energy Management Systems for Microgrids

Energy transition is the path toward transforming the global energy sector from being fossil-based to being zero-carbon, driven by the need to reduce energy-related CO2 emissions so as to counteract climate change[...]

Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner.

Energy management of renewable energy-based microgrid system with HESS for various operation modes

This paper proposes a ramp-rate control approach for a grid-connected MG with a hybrid energy storage system. Distributed energy sources (DERs), such as solar photovoltaic (PV) and wind, combined with energy storage (ES) and controllable loads, are critical to a power grid that can handle the intermittent nature of renewable energy sources. Therefore, the complexity of the system is increasing as researchers move towards a more renewable based power grid. An energy management system (EMS) for microgrids must consider the power available in RESs as well as the storage capacity of energy storage devices (ESSs). Modern MGs include a wide range of converters for a variety of applications, including distributed generation interconnection, grid integration, energy storage management systems, and demand management, among others. So, the ramp-rate control smooths fluctuations in photovoltaic power, which increases system reliability. In the proposed system, 80 V DC is used to supply high and low power DC loads. The suggested system can extract the maximum amount of energy from RESs, maintain efficient ESS management, and achieves quick DC-link voltage regulation with settling time of 230 ms throughout all operating modes. These conditions are met by the energy management system, which gives the MG with operational capability and ensures its reliability. The MG with proposed features and EMS was validated using the MATLAB/Simulink environment, and results were obtained using the Hardware-in-the-Loop (HIL) experimental test bench. The proposed small-scale RES-based MG can be used to develop and test algorithms for a wide range of MG applications.

Using quantum spherical fuzzy decision support system as a novel sustainability index approach for analyzing industries listed in the stock exchange

This study aims to identify critical factors of the sustainability index (SI) and evaluate the industries listed in the stock exchange based on the performance of this index. For this purpose, a new fuzzy decision-making model has been created. Firstly, 12 different criteria for the SI are weighted by decision using trial and evaluation laboratory (DEMATEL) methodology based on quantum spherical fuzzy sets (QFS) with golden cut. Secondly, SI performances of the 10 industry alternatives are ranked by extended technique for order of preference: according to similarity through an ideal solution (TOPSIS) with QFS. The main novelty of this study is to identify significant sustainability criteria for the companies to join this index with an original fuzzy decision-making methodology. For this purpose, the analysis results help the companies to act without incurring extreme costs. It is determined that the usage of renewable energy has a significant impact on all other criteria, and while using clean energy, the companies get a chance to improve other criteria of SI. Additionally, based on the weighting results, the use of renewable energy is the most critical criterion to be included in SI. Similarly, recovery of the resources also has a significant contribution. Finally, the ranking results indicate that communication and IT is the most successful industry for SI. Hence, it would be appropriate for businesses to attach importance to the use of renewable energy. In this way, the carbon emission problem would be minimized, and the image of businesses will rise in the eyes of both consumers and investors. In this framework, companies can join microgrid energy management systems to handle high-cost problems of renewable energy investment projects. Owing to microgrid energy applications, the costs of using renewable energy can be shared with other companies, and since this situation can reduce the cost per unit, it will be possible to increase the use of renewable energy.

A novel cost‐effective voltage fluctuations management in a droop‐controlled islanded microgrid

This paper presents a new energy management system (EMS) for an islanded microgrid (MG) to increase power system security cost-effectively. The small size of MGs, variations in renewable energy sources (RESs) output power, and low inertia of power electronic interfaced distributed energy resources (DERs) convert voltage fluctuations into a vital issue for the MG EMS. This paper proposes a two-stage stochastic multiobjective framework to simultaneously attain economic, environmental, and technical aims. In the first stage, many scenarios related to RESs output power variations, load demand changes, and DERs contingency outages are generated using Monte Carlo simulation. Afterwards, generated scenarios are reduced and applied to the second stage. A novel objective function based on the expected voltage fluctuations (EVFs) caused by active and reactive power variations is proposed, which is minimized alongside the active and reactive costs and MG emission. The proposed model is applied to a test MG, and active and reactive power and primary and secondary reserve scheduling are correctly accomplished for five different cases. Precise analysis confirms the notability and effectiveness of MG voltage fluctuations management in energy and reserve scheduling. Numerical results show more than 60% reduction in voltage fluctuations.

A novel grid-connected microgrid energy management system with optimal sizing using hybrid grey wolf and cuckoo search optimization algorithm

Renewable energy systems, particularly in countries with limited fossil fuel resources, are promising and environmentally sustainable sources of electricity generation. Wind, solar Photovoltaic (PV), and biomass gasifier-based systems have gotten much attention recently for providing electricity to energy-deficient areas. However, due to the intermittent nature of renewable energy, a completely renewable system is unreliable and may cause operation problems. Energy storage systems and volatile generation sources are the best way to combat the problem. This paper proposes a hybrid grid-connected wind-solar PV generation Microgrid (MG) with biomass and energy storage devices to meet the entire value of load demand for the adopted buildings in an intended region and ensure economic dispatch as well as make a trade in the electricity field by supplying/receiving energy to/from the utility grid. The control operation plan uses battery storage units to compensate energy gap if the priority resources (wind turbine and solar PV) are incapable of meeting demand. Additionally, the biomass gasifier is used as a fallback option if the batteries fail to perform their duty. At any time, any excess of energy can be utilized to charge the batteries and sell the rest to the utility. Additionally, if the adopted resources are insufficient to meet the demand, the required energy is acquired from the utility. A Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO) algorithm is adopted for achieving optimal sizing of the proposed grid-connected MG. To assess the proposed technique’s robustness, the results are compared to those obtained using the Grey Wolf Optimization (GWO) algorithm. The GWCSO method yielded a lower total number of component units, annual cost, total Net Present Cost (NPC), and Levelized Cost Of Energy (LCOE) than the GWO algorithm, whereas the GWCSO algorithm has the lowest deviation, indicating that it is more accurate and robust than the GWO algorithm.

Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant

The economic renewable energy generations have been rapidly developed because of the sharp reduction in the costs of solar panels. It is imperative to forecast the three-phase load power for more effective energy planning and optimization in a smart solar microgrid installed on a building in the Linyuan District, Taiwan. To alleviate this problem, this article proposes a convolution neural network bidirectional long short-term memory (CNN-Bi-LSTM) to accurately predict the short-term three-phase load power in building the energy management system in the smart solar microgrid with the collected data from advanced metering infrastructure (AMI), which have not been investigated before. The three-phase load-predicting methodology is developed using weather parameters and different collected data from AMI. The project evaluates the performance of the CNN-Bi-LSTM model by utilizing hyper-parameter optimization to attain the optimum parameters. The prediction models are trained based on hourly historical input features, selected based on the Pearson correlation coefficient. The performances’ optimal structure CNN-Bi-LSTM are validated and compared with the bidirectional LSTM (Bi-LSTM), LSTM, the Gated Recurrent Unit (GRU), and the recurrent neural network (RNN) models. The obtained optimized structure of CNN-Bi-LSTM demonstrates the effectiveness of the proposed models in the short-term prediction of three-phase load power in a smart solar microgrid for building with a maximum enhancement of 68.36% and 8.81% average MSE, and 30.26% and 36.36% average MAE during the testing and validating operations.

Microgrid Energy Management during High-Stress Operation

We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements.

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user’s lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household’s daily electricity cost.