A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

Abstract

The concept of the digital twin has been adopted as an important aspect in digital transformation of power systems. Although the notion of the digital twin is not new, its adoption into the energy sector has been recent and has targeted increased operational efficiency. This paper is focused on addressing an important gap in the research literature reviewing the state of the art in utilization of digital twin technology in microgrids, an important component of power systems. A microgrid is a local power network that acts as a dependable island within bigger regional and national electricity networks, providing power without interruption even when the main grid is down. Microgrids are essential components of smart cities that are both resilient and sustainable, providing smart cities the opportunity to develop sustainable energy delivery systems. Due to the complexity of design, development and maintenance of a microgrid, an efficient simulation model with ability to handle the complexity and spatio-temporal nature is important. The digital twin technologies have the potential to address the above-mentioned requirements, providing an exact virtual model of the physical entity of the power system. The paper reviews the application of digital twins in a microgrid at electrical points where the microgrid connects or disconnects from the main distribution grid, that is, points of common coupling. Furthermore, potential applications of the digital twin in microgrids for better control, security and resilient operation and challenges faced are also discussed.

1. Introduction

The worldwide utilization of energy is estimated to increase by 50% from now onwards till 2050 [1]. Since the demand is expected to rise at a high rate in the future, the integration of renewable energy resources (RERs) into the grid can contribute to meeting the increased demand. However, including RERs in the grid is the most challenging process because it raises concern about the system’s stability [2]. Extensive analysis has led researchers to conclude that combining RERs with microgrids (MGs) aids in resolving the stability issues in the power system [3].

MG is a low-voltage network capable of providing uninterrupted power. It consists of energy resources (solar, wind, biogas), energy storage (battery, super-capacitor, electric vehicle [4,5]) and power converters (AC/DC, DC/AC, DC/DC) [6] which coordinate to form a localised power system network which can be flexibly operated in islanded or grid-connected mode [7]. A concise synopsis of MGs can be found in [8]. MGs are connected to the grid at the point of common coupling (PCC) through a circuit breaker. The power converter [9] plays an essential role in the integration of microgrid components. All sources and loads are controlled by a microgrid controller and managed by the energy management system. The control of MG can be centralised or decentralised. The energy management system (EMS) is a vital part of MGs for reducing operating costs [10]. It is essential for MG to have operational control in order to ensure that it works efficiently. MGs are promising to improve reliability [11], resiliency [12] and power quality [13], and can be integrated with clean energy resources. Deployment of MG faces many issues, such as standardised interconnection policy, opposition by utilities to adding more DERs and utility regulation [14]. The process of establishing the MG is a complex activity.

A virtual model is an excellent tool for monitoring MGs. It is easier to collect data through IoT technology which helps in develop a data-driven model of various physical entities. IoT refers to the collective network of electrical devices, such as sensors and wearable devices, connected through the internet to facilitate data collection and information sharing [15]. It makes monitoring, optimizing and analyzing the system more accurate [16]. The IoT [17] has opened up new possibilities in the technological realm. Recently, digital twins have been seen as a stepping stone toward the goal of digitalizing the electrical grid. According to Barbara Rita et al. [18], the DT can be defined as an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin. The concept of DT has its dependency on IoT that has been well described in [19]. The growth of a DT is dependent on a massive amount of data. Integration of DT in IoT elevates the standard for the data requirements of the physical twin, which is essential for the development of DT. It can also facilitate communication between digital and physical systems, bridging the gap between the two worlds. The theoretical, practical, technological and commercial dimensions of creating and maintaining DTs are mentioned in [20].

Although many studies related to DT have been carried out, to the authors’ best knowledge, no literature exists that compiles the research on microgrid digital twin (MGDT) progress, DT usage for energy management, difficulties inherent in the developing MGDT and its applications. Considering the fast-paced advancements in power systems research and practical deployments and increasing utilization of microgrids, a comprehensive survey of the current state of the art in DT technology for microgrids will be of benefit to researchers and practitioners. Therefore, the aim of this study is to review the current state of knowledge concerning the creation of a digital twin of a microgrid, along with the challenges encountered and the potential benefits. The objective of this paper is to come up with the consolidation of major works related to MG digitization. Although the islanded microgrid may include diesel/gas generators for the backup power supply, the use of the same is limited in the grid-connected microgrid. Further, as the world is focusing on greener technologies, the use of diesel/gas generators will be eliminated from the microgrids. Therefore, the DT of the diesel/gas generators for its operation is not considered in the present review work.

Figure 1 depicts the taxonomy of the papers that were reviewed, which span across the various sections of this paper under different topics and themes. Section 2 gives an overview of various requirements, technology enablers and different stages of the digital twin development. Section 3 presents the application of digital twin technology for microgrid components. Digital twins for energy management operation and challenges and application of microgrid digital twins (MGDT) are discussed in Section 4 and Section 5, respectively. Finally, Section 6 concludes this work.

2. DT Development

The power system is the most complex human-made system, and faces many challenges regarding the connection of RERs, integration of power electronics equipment, electric vehicle integration and energy management, which demands regular monitoring and maintenance [21]. The main grid is now being merged with MGs because of the inexorable benefits of MGs. Its integration demands a more efficient tool so that the problems faced can be dealt with efficiently. Due to this kind of complexity, it is important to utilise the simulation model efficiently; the model needs to have the capability to deal with higher complexity and be of spatiotemporal nature. The digital twin technologies have the potential to address the above-mentioned issues and can come up with an exact virtual model of the physical entity of the power system. Palensky et al. in [22] explained how to connect digital twin technology and the power system and mentioned some applications. Zhou et al. [23] proposed a novel architecture in light of DT for online power grid analysis. The validation of this model has been carried out with a test on a vast scale network of 40K+ buses taken from the Chinese national grid that tracked operation state only in sub-second delay compared to the old tracking system. DT can be considered a tool that will be dynamic and intelligent in design, accurate and exact in its outcomes because of the closed-loop nature of the digital twin.

2.1. DT Design Requirements

The development of the digital twin comprises three steps: modelling the physical system, developing a virtual twin of the system and establishing communication between virtual and physical systems. When integration and interaction of all these sub-systems are efficiently achieved, only absolute modelling of a system can be realised [24]. Modelling can be physics-based (a.k.a model-based), data-driven or a combination of both. The availability of low-priced sensors which can capture different types of data is the reason for the popularity of the data-driven approach. H. Pan et al. [25] discussed the basic concept of DT and the architecture for DT modelling.

With the development of measuring devices and data collection technologies, a massive amount of data are available, which can help to develop the digital twin. Data can be collected from various sources like sensors, historical events, maintenance history, health records, performance data, etc. [26]. The sources of energy data are supervisory control and data acquisition (SCADA) systems, smart meters, phasor measurement units (PMUs), advanced metering systems, digital fault recorders (DFR), etc. Different data measuring power, current, voltage, power factor and weather information can be collected through electrical or field devices to train a model. For the system with higher complexity, the data-driven approach can be more precise than the physics-based approach [27]. After being collected, data must be wrangled to improve the quality; e.g., missing data need to be imputed. To do so, various algorithms are utilised. In the context of DT, smart data analysis, employing machine learning (ML) and artificial intelligence (AI), is likely to play a significant role.

A real-time data connection is required for data and information exchange. Communication provides self-operating and distributed monitoring, management and expansion. The developed infrastructure for communication assures connectivity among all the devices in the microgrid. Various communication protocols are required to ensure the information is transmitted properly and undistorted between different entities. Middleware is a software layer that manages data from different interfaces and provides a standard interaction platform to exchange information within devices [28]. MGDT should keep updating itself and be in sync with the existing physical system, which will improve the situational awareness of the system [29].

A block diagram for achieving the digital twin of the microgrid is presented in Figure 2. It can be perceived from the figure that real-time data are collected from physical entities through sensors. Accordingly, data are used to communicate and create a virtual twin to analyse, control and provide feedback to the physical system. Many big companies, such as General Electrics, Siemens, ABB and Rolls-Royce, are developing digital twin models of different electrical equipment [30].

2.2. DT Enablers and Its Stages

The development of DT is dependent on the application of a variety of technologies, which are outlined in Figure 3. Massive data used in DT development require an efficient tool to grab reliable information from those data and is conducted with the help of big data analysis. Big data analysis can be carried out on cloud or non-cloud data. Cloud computing is emerging as an effective concept due to its advantage regarding data storage. Pargmann et al. [31] developed an approach to collaborate technical and business data using cloud computing and DT technology.

The reliability of the DT model depends upon its rationalisation and adaptation to the changes in the environment and parameters. In an MG, there are continuous changes in operating conditions, such as changing weather conditions, which affect solar and wind power production, load variation and integration of different equipments. Therefore, updating the DT model is critical to maintaining its accurate prediction capability. The model needs to be improved regularly through mathematical equations and constraints. After the computer model is updated, it must be validated based on the data from physical experiments. Various methods of model updating and validation strategies are mentioned in [32]. The human-like capability of lifelong learning can help keep the model current. However, this type of understanding in a model is a difficult task. A review on this kind of continuous lifelong learning using ML was discussed in [33].

The author in [1] has discussed the conceptual idea of establishing the DT of a power plant. According to the author, the three basic steps of DT are modelling, data communication and parameter tuning. The basic stages for developing a digital twin of any physical entity are shown in Figure 4.

3. DT Applications for MG Components

Microgrids can be seen as scaled-down versions of a centralised power system. An AC microgrid is depicted in Figure 5. It shows the general structure of an AC microgrid which consists of ESS, energy sources, DC loads and power converters.

The combination of multiple MGs in the same network [34] has many advantages, such as increased reliability, efficient exploitation of RERs and improved energy efficiency over single MGs. The integration of MGs into the main distribution network enhances the smart grid formation [35]. Research on the use of the digital twin concept to transform the traditional grid into a smart grid has been discussed in [36,37]. A study of innovative digital twin platforms for smart grid systems [28] shows that the DTs generate reports continuously, enabling early fault warnings, proactive elimination and minimising loss. Some approaches [38,39] link smart grid technology and digital twin domains. The headway to a smart city formation goes through the development of a smart grid in the city. The authors of [29] established a theoretical way to develop a microgrid digital twin (MGDT) and analysed the different applications of DT in MG.

Works related to MG components involving DT technology have been consolidated in the following subsections.

3.1. Solar Power

Solar power demand has been increasing extravagantly [40]. Although the construction of solar panels is a one-time investment, it requires high-quality maintenance to achieve its highest performance. Therefore, it is important to simulate and model it in advance for better operation, maintenance, planning, deployment, forecasting, fault diagnosis and asset performance management. The anatomy of groundwork, which has been performed in the area of solar panel digital twin, shows the importance of analytics to analyse asset data [41]. The photovoltaic (PV) panels, inverters, meters, environmental units, energy storage, plant and power grid can be designed with digital twins for solar power. Predictive analysis uses digital twins, which can be used to gain a better understanding of the failures and take preventive maintenance decisions.

Photovoltaic systems are exposed to different faults due to their complex outdoor installations, increased number of power electronics elements and ageing, which can impact PV system performance and reliability. Assessment of photovoltaic module failures can be carried out with the use of digital twin technology. A holistic digital twin approach to fault detection and identification for PV systems was developed in [42] as shown in Figure 6. In this approach, a physics-based digital twin was built to estimate the panel current and voltage. The difference between the estimated values and values measured from the physical twin helps the system detect and identify PV installation faults in real-time effectively.

The generated power by solar PV varies greatly because of its dependence on weather conditions. Therefore, forecasting power production will help in ensuring the reliability and availability of power. The forecasting model uses historical solar PV power data, solar irradiance, rainfall, temperature, etc., to forecast solar PV power output. Different machine learning algorithms [43] are applied to obtain the best prediction.

J. Shi et al. [44] proposed an algorithm to forecast the one-day-ahead power output of photovoltaic systems. Generally, there are two ways of forecasting PV power output; one is based on sunshine intensity, and another is based on system output. The intensity of sunlight is affected by a variety of factors, making it a nonlinear problem [44]. In a photovoltaic energy system, the hot spot is considered one of the main issues of PV modules, as local overheating can lead to module damage. Many data-driven approaches have recently been applied to find the hot spots in PV modules [45].

3.2. Wind Energy

Wind turbines are of two types, onshore wind turbines and offshore wind turbines. Due to adverse weather conditions they face, it is not easy to handle them manually. They are fitted with various sensors that continuously measure characteristics, such as wind speed, humidity, vibration and spindle temperature, resulting in continuous data streams [46]. Wind speed sensors are more error-prone, which deteriorates the performance of wind turbines and leads to faulty conditions. To detect fault sensors, Yang Li et al. [47] proposed a data-driven digital twin estimating the wind speed for the downwind turbines based on the wind speed measurements at the upwind turbines and their spatiotemporal correlation. The residual between the estimated and measured speeds is used to identify a possible fault. In [48], a condition monitoring approach for drivetrains on floating offshore wind turbines is proposed, utilising a DT framework. The data-driven DT uses a torsional dynamic model, online measurements and fatigue damage estimation to estimate the drivetrains’ remaining useful life (RUL). The proposed methodology has been simulated and tested to monitor the health of the turbine. However, only the mechanical aspect was considered in the model, while electrical aspects, such as power produced by wind, losses, etc., were not.

Sivalingam et al. [49] have established a physics-based methodology for predicting the RUL of electrical components, especially power converters. SCADA data are used to derive the wind turbine’s wind profile, such as wind speed, temperature, yaw angle and electrical power generation. The suggested methodology has been tested for both fixed and floating wind farm applications. In [50], a digital twin was developed based on the turbine’s geometric and aerodynamic properties to monitor the health of onshore wind turbines continuously. Data from the manufacturer catalogues were used to calibrate the system.

3.3. Biogas Energy

In biogas plants, organic materials are transformed into biogas by physical and biochemical processes in anaerobic environments. As there is a lack of essential waste analysis, there may be decreases in the optimization of production processes and output stability. Machine learning and deep learning can help in the key waste selection and prediction improvement for biogas production [51,52]. Spinti et al. [53] proposed a digital twin approach to optimising boiler performance in uncertain conditions by combining Bayesian inference from science-based models and machine learning with decision theory. To implement it online in real-time, the simulated data extracted using the Bayesian analysis are evaluated using a Gaussian process regression as a fast and robust surrogate model.

Elmaz et al. [54] used four regression techniques to model the biomass gasification process of carbon monoxide, carbon dioxide and hydrogen peroxide outputs. These developed models can be utilised for predicting outputs in simulation platforms and real-world applications. For biogas production prediction, an RNN-based deep learning model was developed with the hybrid architecture of dual-stage attention, long short-term memory and variable selection networks. Wang et al. [55] studied different waste inputs and operating conditions that affect biogas production using industrial-scale anaerobic co-digestion data from 8 years, combining it with a tree-based pipeline optimisation tool. The significance of this work was that the tree-based pipeline optimisation tool was used with a larger data set than any previously catalogued work. But a true DT of biogas with a proper communication channel has not been achieved.

In the near future, IoT sensors may help collect data from the biogas field, integrating the recorded temperature, humidity, pressure, etc., in a dataset. The massive dataset can improve the training and provide a highly stable model. This learning can be implemented in MGs to reduce waste production and improve economic efficiency. The above-discussed RERs are three main energy resources integrated into the MG system.

3.4. Battery

Electrical utilities must supply energy continuously to meet the real-time demand of consumers. However, when energy demand decreases, such as in off-peak hours, the energy produced exceeds the demand. Therefore, using the battery as an energy storage device can store extra energy for future use and help maintain the energy in the microgrid. Storage is crucial to diversifying energy sources and providing renewable energy to the market. But their internal status is hard to measure. Traditional ways of estimating a battery’s internal states, such as state of charge (SOC) and state of health (SOH), are challenging to use with degrading batteries. Without a battery management system (BMS), safety, dependability, lifespan and affordability are compromised. With more battery cells and larger battery systems, wiring connection becomes more complex and expensive. To monitor battery health accurately, Li et al. [56] developed a model-based digital twin on the cloud with an adaptive extended H-infinity filter and particle swarm optimisation for SOC and SOH estimation.

Apart from SOC and SOH status, battery carbon emission is also a major concern. Electric vehicles and grid-scale energy storage are just two examples of how batteries will be critical in our low-carbon future. Even though the main problem remains in maximising the life and effectiveness of these devices, there is an opportunity for more intelligent control of battery systems with the emergence of ML methodologies [57].

A fully physics-based DT of a combustion engine in a power plant is demonstrated in [58] for the first time, including battery storage. A real-time engine model is constructed from a detailed, one-dimensional model, which is then reduced to a fast-running model. This digital twin concept offers predictive capabilities and advantages over previous black-box engine approaches, facilitating self-optimising integrated and coordinated grid-power plant control.

3.5. Electric Vehicle

Using emerging technologies, such as IoT, wireless networking and artificial intelligence (AI), the digital twin technology is improvising its application in the vehicle sector. Even being new to the vehicle industry, some practical ideas and theoretical work can be found.

Bhatti et al. [59] explained digital twin technology’s origins and evolution. This review helps understand the technical function of digital twins in each categorisation. The main purpose of this paper is to consider an electric vehicle as ESS. The electricity in an electric car battery is stored in the form of chemical potential. Batteries can accept, store and release power at any time. ESS cell voltage or charge imbalances are developed due to undercharging, overcharging and temperature profiles. ESS cell voltage life will be prolonged by reducing imbalance and temperature impacts [60]. In addition, some other issues related to electric vehicle batteries are power electronic interfacing, sensitive energy management system, charging interfaces, etc.

The authors in [61] have presented a consolidation of modern battery and battery management technologies for hybrid and pure electric vehicles, along with the progress and obstacles they have faced. A proper architecture [62] is required to implement a digital twin of the battery management system. The application of this architecture can be considered as a blueprint for a domain-specific meta-model of high-voltage battery systems and related processes over the whole life cycle. Through this meta-model, an actual system can be created. The research work [63] proposes a digital twin paradigm for the BMS, shown in Figure 7, to estimate and anticipate battery conditions with only a voltage sensor.

The battery’s health needs to be monitored because it degrades over time. In [64], the digital twin model of lithium-ion battery is proposed to predict the battery’s performance deterioration accurately by simulating the battery’s discharge process. Data from the observable parameters are used to indicate battery health (HI). The LSTM approach, with the temporal measurement as a HI, is used to create a battery digital twin. The digital twin model of the battery’s true capacity can be obtained by virtually draining it. Results from a series of experiments demonstrate the viability of this approach in dynamic operating situations. The drastic increase in demand for electric vehicles requires new and advanced infrastructures for charging. Yu et al. [64] discussed the idea of establishing a cognitive charging station infrastructure with power generation, energy storage and charging networks. DT and parallel intelligence (PI) enables smart and cognitive charging station architecture.

Various methods for modelling energy storage systems have been summarised. However, there is still a need for further study. DT is a novel method for this research work. In MG, the power converter is the third most crucial part. The converter in the MG system establishes connections among the various other parts of the system.

3.6. Power Converters

The power converter plays a vital role in the integration of components of the microgrid. Most of the MG’s generating sources (PV, wind turbine), storage devices and loads require power electronics interfacing devices. A literature survey on power electronic converters for MGs is mentioned in [9]. Power converters include DC converters, AC converters and back-to-back converters. When MGs are connected to the grid, converters behave as current sources sustaining MGs. On the other hand, it behaves as a voltage source while working in islanded mode [12]. Renewable energy sources generally produce DC as output power. To feed that power to the main AC grid, DC/AC converters are needed [6]. As a result, converters are critical to developing microgrids, and, therefore, special attention must be paid to them. The use of data-driven approaches and digital twin models can solve various challenges relating to power electronic equipment, such as device faults, health conditions, remaining life, optimisation and control.

When it comes to the protection of power converters, the part that is most likely to be affected by a malfunction is the power switch. Therefore, fault analysis is being phased out in favour of a data-driven method that makes use of ML to facilitate rapid diagnosis and protection from additional harm. Fault characteristics provide opportunities for data collection. DC–DC converters can now be monitored using a digital twin based on linear differential equations and the linearisation of the 4th order Runga–Kutta method [65]. The suggested approach can identify the internal parameters of the buck converter and create a digital twin buck converter with the same operation waveforms as the physical one, according to both theoretical and experimental results. With the suggested method, both MOSFET and capacitor can be monitored without the use of any extra circuits [66]. In this paper [67], a data-driven technique to deploy a local model network (LMN) for the identification of a DC–DC converter has been proposed. To have a deeper understanding of the efficacy of the suggested strategy, a DC microgrid is taken into consideration. Comparisons to a traditionally tuned PI control indicate that the proposed method is superior in both of the test conditions. As explained in the wind turbine section, [49], a credible physics-based model has been developed for forecasting the remaining useful life (RUL) of power converters in variable-speed wind turbines.

An approach to diagnostic monitoring of modular power electronic converter systems within subsystem control layers is proposed in [68]. This solution makes use of real-time probabilistic DTs that are integrated into the controllers of the system. A case study has also been presented using a digital twin-based diagnostics concept. Using dynamic neural networks, Wunderlich et al. [69] proposed an innovative method to create real-time models of power electronic converters. The proposed modelling methodology is evaluated against existing real-time modelling approaches as well as ML approaches proposed in the past and is proven to be superior to both sets of methodologies. The findings demonstrate that the model is very accurate.

Power converters function as the connecting switches between elements of the MG. The discussed works showed that DT can provide better real-time monitoring and optimised functioning of the power converters. A tabulated summary of the DT of microgrid components, with references and brief descriptions, is provided in

Table 1. Summary of the DT of Microgrid Components.

Application	References	Description
Microgrid Components: Energy sources
Solar power	[42,43,44],	Different conditions such as fault assessment, forecasting power production and prediction of hot spots in PV modules using ML and DT techniques are mentioned in these papers.
Wind Energy	[46,47,48,49,50]	DT of offshore and onshore wind turbines for predicting RUL of components, monitoring of health, wind speed sensing and online condition monitoring are studied.
Biogas Energy	[51,52,53,54,55,70]	Dynamic forecasting using DT and predictive analytics, optimised operation of biomass boiler and data-driven approaches for biogas output prediction on the largest data set are discussed.
Microgrid Components: Energy storage
Battery	[56,57,58,62]	These papers focus on the emergence of ML technologies for better control of battery, the development of DT for BMS on the cloud, the architecture of DT to know the life cycle of battery and the formation of power plant’s DT including battery storage.
EV	[59,60,61,62,63]	These papers discuss ESS cell voltage life, architecture for DT of BMS, battery voltage assessment using regression model, health monitoring of battery and infrastructure management of charging stations using an intelligent system.
Microgrid Components: Power Converter
	[6,9,12,65,66,67,68,69,71,72]	A data-driven approach to determine the capability of the buck-converter, diagnosis of the open switch problem and monitoring and identification of the DC–DC converter is covered in these references. Further, in [72], the pathway of establishing real-time models of power electronic converters is discussed.

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Generally, the simulation studies and modelling on grid-connected microgrids are carried out considering physics-based, data-driven and hybridisation modelling techniques. These models are available on simulation platforms, such as MATLAB Simulink and Real Time Digital Simulator (RTDS). The simulation study on the impact of the operation of a grid-connected microgrid on the rest of the power system network does not mimic the real-world scenario due to the utilisation of a fixed microgrid model. The MGDT can address this challenge as it is considered to be closer to the real-world scenario. The present approach of microgrid simulation relies on fixed models of various components of microgrids. Therefore, the integrated system simulation results generally do not mimic the actual response on the ground. The data-driven approach helps in creating better models of the DT components. When integrated and connected with the rest of the power system network, we may get the simulation response close to real-world scenarios. The scope of the present work is to bring out the state of the art related to MGDT. DT relies on data to connect the digital models with the MGs. Such data are collected via field measurements, IoT devices and smart meters, lines, buses, switches, transformers, loads, storage systems, etc. Therefore, the MGDT can better mimic the real operation of the microgrid when connected to the simulation platform. Hence, MGDT can provide better insight into the impact of an event in the microgrid, such as a fault, on the rest of the grid operation. The necessity of continuously updating the models is a key challenge when establishing an MGDT for different systems/processes. Models must be continuously updated throughout the systems’ lifetime from real-time data streams collected through monitoring systems.

Microgrid is a small self-contained power system network having renewable energy sources (mostly wind and solar), a battery energy storage system and controllable loads. A microgrid acts as a single entity when interacting with the rest of the electrical power grid. As the structure and operation of the microgrid system are well established in the electrical power system literature, it has not been reviewed in the present manuscript of the paper. Being a relatively new concept, the present work focuses more on reviewing the concept of DT of the microgrid. DT of the microgrid is developed at the point of interconnection of the microgrid with the rest of the electrical grid to understand better the impact of microgrid operation on the rest of the electrical grid. The establishment of MGDT comprises a digital twin of each section of the microgrid. For the formulation of DT, the planning layout includes data from each unit of MG that needs to be collected to develop a model, model adaptation, algorithm formulation, bi-directional exchange of data between the physical and virtual model and model validations. The goal of MGDT is to improve the efficiency, life cycle cost, service quality, asset management and longevity of energy systems.

4. DT for EMS Operation

In order to effectively manage and optimise MG operations, digital technology in recent years has played a major role. DT enables real-time synchronisation and monitoring of the energy system [73]. Therefore, energy digital twins (EDT) are being considered to assist in the energy-efficient design of MG. DT technology as a digital enabler has a lot of promises. The primary goals of a successful EDT are to maximise efficiency, reduce energy consumption and open up a path toward effective de-carbonisation while keeping associated expenses low. Microgrid and multi-microgrid operators struggle with energy management. Khavari et al. [10] developed a hierarchical hybrid EMS to control energy in multi-microgrid systems. Simulation findings show that the suggested strategy can boost microgrid profits and distribute common line capacity fairly while removing congestion.

O’Dwyer et al. [72] presented a sustainable energy management system (SEMS) that can govern, schedule, forecast and coordinate energy assets. The SEMS is then combined with a digital-twin simulation environment. It allows scenario-based analysis to be more concrete. In the proposed investigation [74], intelligent recommendation algorithms and digital twins are used to develop new energy services. The study examines the beneficial correlations between recommendation provisions and customer demand-side energy behaviour. Many tools and supporting technologies discussed in this paper can help optimise resource use.

New scheduling methods are presented in [75], which uses deep learning and DT. An energy and data flow model is the basis of the DT model. A case of Morocco’s building energy management system employing DT to improve energy consumption was explored. DT demonstrates the futuristic building’s behaviour and attributes. Following the advent of technologies such as AI, big data, machine learning, etc., the development of methods that save more precise amounts of energy gained momentum [76]. Significant amounts of work are being carried out to enhance energy efficiency and maximize the amount of energy that can be saved. In this regard, Teng et al. [77] integrated the research works pertaining to data-driven approaches to energy saving and presented a way to create a digital twin-based energy-saving system.

Granacher et al. [78] created a digital twin of energy systems that transforms decision-maker demands and preferences into an optimisation-based model and delivers useful solutions. Multi-criteria decision analysis is useful for analysing competing objectives using multi-objective optimisation and Pareto charts. The current study tackles decision assistance in complex superstructure optimisation by adding a digital twin. It consults the decision maker throughout solution synthesis and exploration.

Different approaches have been proposed recently for various purposes, including the data-driven approach to measuring energy saving and emission reduction, energy management systems and statistical learning approaches for energy savings. An intelligent factory outlook for predictive maintenance and intelligent energy savings has also been studied. Utilising these different approaches, MGDT can simplify the regulation and control of MG’s operation.

5. Challenges and Applications of MGDT

5.1. Challenges in Developing MGDT

Some major challenges in implementing MG [8] include optimal power flow, stability, protection, resynchronization after fault, integration of RER, harmonics, etc. A detailed study on issues related to MG development is mentioned in [14]. DT has been used in various aspects; however, several obstacles need to be overcome to produce digital twins of MG. The creation of a digital twin relies heavily on data. Acquiring, cleaning and processing sensor data for the development of MGDT is one of the major challenges. Furthermore, the time skewness of the data due to different data reporting rates of sensors makes the overall problem more complex. Modelling, communicating and updating activities contribute to DT’s growth. Sensor data’s spatial and temporal resolution, communication delays, data volume, data generation rate, data diversity, data validity, data access and processing speeds are all major obstacles in achieving MGDT development [27].

In addition, data security for the DT poses another challenge from a cyber security point of view. Technologies enabling real-time, bidirectional communication between the actual and virtual worlds in MG are required. The involvement of IoTs has increased cyber-attacks. Because there is bi-directional communication in DT, the security of the microgrid will also need to be monitored. The rise of artificial intelligence and machine learning may answer this problem and aid in creating useful algorithms.

Furthermore, there is a need for increased transparency and interpretability in the DT of MG. Therefore, it is essential to deliver the digital twin to the user in a way that makes it indistinguishable from the real asset and easier to use.

The integration of microgrids with the existing power system has been challenging and requires constant updates. To analyse the changing demand, the authors in [79] discuss the modifications made in IEEE 1547 integration standards. This article outlines various microgrid frameworks and discusses challenges and implementation complexities associated with microgrid and power system integration. The finding will help future researchers to improve overall performance, have a better modelling framework, smooth integration, better efficiency and higher scalability and redundancy. In addition, our survey will provide a model or a framework base for future research in microgrid performance and analysis. Our analysis will help researchers to initiate new research work in the micro-grid domain to mitigate the complexity of the grid, security and redundancy.

5.2. Applications of MGDT

MG was created to boost the efficiency of the power system. Growth in the MG industry is driven by the rising demand for clean energy and the need for reliable power [80]. Enhanced dependability, resiliency and power quality are the three main selling points of MGs from the perspective of grid operators [81]. DT is a novel tool that may be used to aid in the design, development and control of MGs to optimise their performance. In [29], the application of MGDT has been explained which covers almost all aspects related to it. Based on the latest research on DT’s use in electricity networks, some of the possible applications in MGs include:

• Health Monitoring: The health monitoring of equipment used in the energy sector is very important as they face various wear and tear situations such as environmental conditions, thermal expansions, vibrations, etc. It is easier to keep industrial equipment running well if there is a primary awareness about its current state of health. Capturing system degradation, performance and ageing data of physical systems are essential requirements of health monitoring [27]. The MGDT can also be used to know the remaining utilised life of the electrical equipment so that the life expectancy can be known. High-fidelity DT models can estimate maintenance needs for power carriers, circuit breakers and other industrial equipment [22].
• Forecasting: Forecasting power availability in MGs will help maintain the supply–demand balance, which is the primary and basic user demand. With the uncertainty of RES, establishing effective predictors to determine the available power of MG is vital. Accurate estimates of available power will enable their participation in services like reactive power support, voltage regulation, etc., to ensure MG’s reliable and secure operation.
• Fault detection: Power grids are frequently prone to faults due to their complexity. Therefore, fault diagnosis is a crucial part of MGs. After a fault event occurs, it is crucial to detect the occurrence, determine the type of problem and prescribe the necessary measures. An efficient fault diagnostic system is required in which DT can help in developing such a model [22]. This will increase the MG dependability by reducing the system response time [81] and associated implications, such as loss of load, outage cost and system stress. A field-programmable gate array DT is used for real-time monitoring and fault diagnostic of power electronic transformer in [82].
• Control Management: The management through MGDT can be seen in two sections. First, the maintenance of optimal power flow [83] within the MGs. Second, as the DT is completely made out of available data, it is important to take good care of the data and do the best possible channelisation of those data. Exploiting the full capability of the mature simulation environment, the effectiveness of the suggested control strategies may be verified under various operating scenarios. Hence, the essential changes can be performed in advance [29].
• Decision Making: The MGDT will facilitate decision-making by supplying information such as the remaining life of equipment, equipment health, weather reports, scheduled power and available power. Information gathered by the MGDT model will aid operators in making judgements.
• Cyber security: Keeping sensitive information safe is very important in the modern era of widespread digitisation. As power grids have become increasingly dependent on digital means of communication and control, safeguarding information has become essential. A protective setting is crucial for MG’s trouble-free operation. Kandasamy et al. [84] created a virtual replica of a physical test-bed for cyber security research on smart grids to address its limitations.

The applications of DT for MG have been explained above. Further, topic-related references with a brief description of the content in the paper are mentioned in

Table 2. Summary of Application of MGDT.

Application Type	References	Description
Health Monitoring	[49,85,86,87]	In these works, the authors have discussed monitoring and predictive maintenance models by using physics-based DT or Data-Driven-based DT.
Forecasting	[88,89,90,91]	Using different techniques, such as physics-based, data-driven, hybrid, output power estimation of wind and solar systems can be found in these works. In [87], deep learning-based power output forecasting methods have been consolidated.
Fault-Detection	[42,92,93,94]	DT-based fault diagnostic methods for Distributed PV systems, rotating machinery and power converters have been studied.
Control Management	[23,59,72,83]	In these works, it has been explained that EMS forms an advanced management system that can operate MG environment with the best possible management of power flow on a physical level and can also manage information on a cyber level.
Decision Making	[23,75,78,95]	These papers discuss how decision-making procedures can be made easy with the help of DT technology. The predictive nature of DT helps speed up the decision-making process.
Cyber Security	[61,84,96,97,98,99]	The DT-based methods have been established for power system security which is needed to protect smart grids.

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6. Conclusions

The present work consolidates the studies related to “Digital Twin” and “Microgrid” with the aim of bringing together previous efforts in applying digital twins in microgrid components and highlighting the complementary nature of these two entities. The literature on DT of MG components regarding energy resources, energy storage and power converters was presented to provide insights into the potential of DT in this field. Furthermore, DT for energy management system (EMS) operations was explored, highlighting its role in efficiently managing MG operations and enabling various energy services such as scheduling, forecasting, energy saving, decision-making and optimisation. The discussion also covered the significant challenges and primary applications of MGDT, which served as a foundation for future research on microgrid performance and analysis. By conducting a comprehensive analysis, we intend to assist researchers in initiating new studies within the microgrid domain and addressing related challenges.

Digital twins for microgrids offer real-time monitoring, predictive analytics and optimisation capabilities. They enable operators to monitor system performance, anticipate issues, optimise energy management, detect faults and foster the integration of microgrids to the main grid. Digital twins also facilitate testing, scalability, interoperability and virtual experimentation. As technology advances, digital twins will play a crucial role in enhancing the management and performance of microgrid systems, improving grid resilience, efficiency and sustainability. To promote the adoption of more MGDTs in the future, researchers and practitioners of the field should focus on the implementation stage, namely, how data collection should be implemented to enable MGDT to work seamlessly while reducing complexity and avoiding cyber security issues.