Microgrid_Digital_Twins_Concepts_Applications_and_Future_Trends.pdf

Received November 17, 2021, accepted December 22, 2021, date of publication December 27, 2021,
date of current version January 7, 2022.
Digital Object Identifier 10.1109/ACCESS.2021.3138990
Microgrid Digital Twins: Concepts,
Applications, and Future Trends
NAJMEH BAZMOHAMMADI1, (Member, IEEE), AHMAD MADARY 2,
JUAN C. VASQUEZ1, (Senior Member, IEEE), HAMID BAZ MOHAMMADI 3,
BASEEM KHAN 4, (Senior Member, IEEE), YING WU1,
AND JOSEP M. GUERRERO1, (Fellow, IEEE)
1Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark
2Mechanical and Production Engineering Department, Aarhus University, 8000 Aarhus, Denmark
3Radio Access Performance Engineering Department, TELUS, Calgary, AB T2A 4Y2, Canada
4Department of Electrical and Computer Engineering, Hawassa University, Hawassa 05, Ethiopia
Corresponding authors: Josep M. Guerrero (joz@et.aau.dk) and Baseem Khan (baseemkh@hu.edu.et)
This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (25920): Center for Research on Microgrids
(CROM).
ABSTRACT Following the fourth industrial revolution, and with the recent advances in information and
communication technologies, the digital twinning concept is attracting the attention of both academia and
industry worldwide. A microgrid digital twin (MGDT) refers to the digital representation of a microgrid
(MG), which mirrors the behavior of its physical counterpart by using high-fidelity models and simulation
platforms as well as real-time bi-directional data exchange with the real twin. With the massive deployment
of sensor networks and IoT technologies in MGs, a huge volume of data is continuously generated, which
contains valuable information to enhance the performance of MGs. MGDTs provide a powerful tool to
manage the huge historical data and real-time data stream in an efficient and secure manner and support MGs’
operation by assisting in their design, operation management, and maintenance. In this paper, the concept
of the digital twin (DT) and its key characteristics are introduced. Moreover, a workflow for establishing
MGDTs is presented. The goal is to explore different applications of DTs in MGs, namely in design,
control, operator training, forecasting, fault diagnosis, expansion planning, and policy-making. Besides,
an up-to-date overview of studies that applied the DT concept to power systems and specifically MGs is
provided. Considering the significance of situational awareness, security, and resilient operation for MGs,
their potential enhancement in light of digital twinning is thoroughly analyzed and a conceptual model for
resilient operation management of MGs is presented. Finally, future trends in MGDTs are discussed.
INDEX TERMS Artificial intelligence, automatic learning, big data, decision support system, digital twin,
Industry 4.0, microgrids.
I. INTRODUCTION
In recent years, with the advances in information and
communication technologies, digitalization and automation
have been profoundly influencing different industries. Major
advances in the internet of things (IoT), cyber-physical-
systemss (CPSs), artificial intelligence (AI), and big data
analytics (BDA) are the main drivers of this revolution
[1]–[3]. According to the industrial revolution paradigm or
Industry 4.0, the next-generation systems are the outcome of
The associate editor coordinating the review of this manuscript and
approving it for publication was Yu-Huei Cheng .
the evolution and convergence of new technologies such as
onboard computation, intelligent and fast controllers, big data
analytic, machine learning (ML), and IoT technologies [4].
With these advances, the real-time data streams can be con-
tinuously gathered, processed, and analyzed along with the
high-fidelity models to create a digital representation of a
complex system and provide a great insight into its current
and future operating status. Thus, a precise, up-to-date, and
dynamic virtual representation of the system is available for
real-time supervisory and control. This concept is known as
digital twinning and is increasingly receiving the attention of
academia and industry across sectors.
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N. Bazmohammadi et al.: Microgrid Digital Twins: Concepts, Applications, and Future Trends
Broadly, digital twins (DTs) are defined as software-based
abstractions of complex physical systems that are connected
to the real system via a communication link to continuously
exchange data with the real environment and establish a
dynamic digital mirror with a constantly running modeling
engine [1]. The original idea of creating a twin for a system
formed in NASA’s Apollo program in which to mirror the
conditions of the main vehicle in space, another vehicle iden-
tical to the main one was built on earth and called the twin.
The term DT was then introduced in 2012 in NASA’s inte-
grated technology road map under Technology area 11, where
DT was defined as An integrated multi-physics, multi-scale
simulation of a vehicle or system that uses the best available
physical models, sensor updates, fleet history, and so on,
to mirror the life of its corresponding flying twin, [5], [6].
Besides, the DT concept was proposed from the perspective
of product life cycle management by Dr. Grieves in 2003 and
later on in a white paper in 2014 [7]. In the aviation industry,
the twinning approach has been adopted a long-time ago to
train the operators in similar real flight situations.
Although the birthplace of twinning is in aerospace and
aviation industries, it rapidly found its applications in man-
ufacturing [2], [8]–[10], petrochemical [11], [12], and auto-
motive systems [13], [14], urbanization and smart cities [15],
[16], healthcare system [17] for elderly healthcare ser-
vices [18] and remote surgery [19], and power system indus-
try [1], [20], [21]. A review of the industrial applications of
DT can be found in [22]. In [23], DT is introduced as a key
aspect of smart manufacturing systems besides the three other
aspects of modularity, connectivity, and autonomy. In [11], a
ML-driven DT is developed for production optimization in
the petrochemical industry. The model is trained using the
data collected from the petrochemical industrial IoT systems,
business transaction driven systems, and data mapping based
on knowledge in business models. In [15], the smart city DT is
introduced as a tool for studying the dynamics governing the
complex interdependency between humans, infrastructures,
and technology and understanding cities’ response to changes
through implementing what-if scenarios.
In [24], DT is defined as a set of virtual information that
fully describes a potential or actual physical production from
the micro atomic level to the macro geometrical level. At its
optimum, any information that could be inspected from a
physical product can be obtained from its DT [24]–[26].
In [27], DT is characterized by the ability to simulate the sys-
tems in different scales of time relying on expert knowledge
and field experience aggregating through data collection.
Digital twinning has been also attracting the attention of
power system society during the last years. In [28], DT is
defined as the virtual image of the physical object in the elec-
trical power system, which makes the provided data usable
for various purposes in the control center. The differences
between DT and power system simulation, power system
online analysis, and CPSs are described in [20]. The key
difference between DT and other simulation or representation
methods is that DT is dynamic and intelligent by design.
Through establishing a bi-directional relation between the
digital and physical systems, the performance of both systems
can be continuously improved. The real-time data stream
will help to improve the twinning accuracy autonomously
and dynamically while a DT-driven decision support system
(DSS) can assist system operators to improve the physical
system performance [29].
From simulation perspective, DT is the next simulation
paradigm [30] as represented in Fig. 1 adapted from [30].
With the advent of DT, the utilization of the simulation
model is expanded over the entire lifetime of the sys-
tem/process [30]. An accurate and dynamic representation
of a microgrid (MG) is beneficial during the MGs whole
life cycle from planning phase to operation, maintenance,
and expanding stages. Having the microgrid digital twin
(MGDT) before MGs construction will provide the designers
with the opportunity of optimizing their design and analyzing
the consequences of their decisions in a low-cost low-risk
environment. Thus, with deploying MGDT concept, a closed-
loop can be formed from operation and maintenance back to
the design and development of MGs [6].
Taking the promising advantages of digital twinning, dif-
ferent companies started adopting DT in their solution strate-
gies. General Electric (GE), Siemens, ABB, and Rolls-Royce
are among the pioneers in this area. A DT interface for
managing wind farms has been developed by GE [31]
including the topography and environmental information of
the wind farms. Siemens has started to develop a digital
grid model-ELVIS- for the Finland transmission system in
2016. The digital model supports asset management, oper-
ation management, investment planning, and forecasting of
future energy consumption [32]. In addition, American elec-
tric power (AEP) transmission initiated a collaboration with
Siemens in 2017 to develop a DT-based solution for better
coordination of network model information across different
domains and to centralize management of the information.
This way, the time and cost caused by manual coordina-
tion will be reduced [32]. Siemens is also among the early
adopters of digitalization and industrial edge technology in
drive systems [33]. In marine systems, ABB is among the
DT adopters for remote monitoring and predictive mainte-
nance purposes. According to [34], the ABB marine remote
diagnostic system for monitoring and predictive maintenance
has considerably reduced their onboard visits. Rolls-Royce
Marine has also established a collaboration with a number
of leading maritime players to develop an open simulation
platform for creating DTs of existing and future vessels [35].
This paper aims to introduce the concept of MGDT and
present different steps of establishing a DT for MGs. Besides,
different services that can be provided by MGDTs during
the MGs’ lifetime are explored. Moreover, related state-of-
the-art studies that applied the DT concept to power system
applications and specifically MGs are reviewed.
The remainder of this paper is organised as follows. Estab-
lishing MGDTs is presented in Section II. In Section III,
DT applications in MGs including MGs design, control and
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FIGURE 1. Digital twin: the new simulation paradigm [30].
operation management, operator training, forecasting, state
of health (SoH) monitoring and predictive maintenance, fault
diagnosis, security, resiliency, and situational awareness and
expansion planning are discussed. Future trends of MGDTs
are discussed in Section IV. Finally, the paper is concluded in
Section V.
II. ESTABLISHING A DIGITAL TWIN FOR MICROGRIDS
The digital twinning framework consists of three parts, phys-
ical system, virtual system, and the data exchange between
these two systems. To build a DT, high-fidelity models are
integrated with the available multi-source data such as sensor
data, historical data, technical information, maintenance his-
tory, and so on [29]. The data is used to develop models of
the physical system and preserve the models’ accuracy under
different operating conditions. Thus, very realistic and up-
to-date perception of the state of operation of the system is
available for reasoning and decision-making purposes. In the
following, different steps of establishing DTs will be intro-
duced (see Fig. 2).
FIGURE 2. Establishing a digital twin.
A. MODELING OF PHYSICAL SYSTEMS AND PROCESSES
Modeling forms the basis of digital twinning [22]. The first
step in establishing a DT is building accurate models of the
real system or asset, which can mirror the behavior of the real
twin. To establish the virtual model, the best available knowl-
edge of the system dynamics should be used and integrated
with the available data. The data includes the historical data
obtained from the system under various operating conditions.
The complete model of a system is achieved by integrating
models of all subsystems and their interactions [36].
For modeling purposes, physics-based, data-driven, and
hybridization of both can be used. Physics-based models are
based on the first principle physical models and the exact
mathematical models of the system dynamics that explain
the system behavior. In case there is a lack of knowledge
about some parameters, the model is adaptively identified
based on the most recently obtained data reflecting the current
operating condition of the system. Heuristic techniques and
AI methods are widely used for parameter identification. This
approach is used in [37] to model a buck converter. In [38],
artificial neural network (ANN) is used to tune the parameters
of an inverter model.
Data-driven models can account for different phenomena
which are usually very hard to formulate mathematically.
They can also take into account the long-term historical data
that is very challenging to do in physics-based modeling [39].
However, huge data of the system in various operating con-
ditions are required to train the models using advanced ML
techniques. Besides, the model might generalize poorly in
unseen operating conditions and the accuracy might degrade
over time. Therefore, it is important that data-driven models
are continuously enriched with real-time data to embrace the
current state of the behavior of the system, to enhance the
model accuracy, and keep it as matched as possible to the real
counterpart.
Taking the advantages of both physics-based and data-
driven models, hybridization of both approaches is consid-
ered as a promising modeling solution for digital twinning
purposes. Constructing of DTs from a modeling point of view
is discussed in detail in [39].
It is worth noticing that a central aspect of the DT is
the ability to provide different information in a consistent
format [26]. Taking into account the purpose of deploying DT
and the intended application, various models with different
levels of abstractions could be developed. While complex
models feature higher accuracy, the computational time for
assessing the model will be the main barrier. Thus, in case
a system-level analysis is required, approximate reduced
order models with less complexity are highly preferred. For
instance, considering the hierarchical control of MGs [40],
the exact dynamics of different components is not needed
at the tertiary level known as energy management system
(EMS). In this case, having the information of energy flows
among various subsystems and the approximate input-output
power relation of different components is enough to guar-
antee the power balance at the system level. Besides, the
model provides the required information to evaluate the key
performance indicatorss (KPIs) such as the operating cost,
emission, reliability, and system losses among others. As an
example, simplified models of the MG’s components such
as photo voltaic (PV) systems (equivalent circuit models or
black-box models) accompanied with the field meteorologi-
cal data suffice for the short-term prediction of their available
power [41]. On the other hand, for studying the degradation
of PV cells, a detailed analysis of microscopic performance
limiters is needed [42]. It is worth mentioning that the inter-
operability of different services and sharing models and data
in an efficient and secure manner are among the key function-
alities of DTs.
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All the models are continuously updated and synchronized
to make sure that the DT closely tracks the behavior of
the physical system and there is no inconsistency between
different models. In this sense, twinning rate refers to the
rate at which the DT is updated based on the most recent
information of the physical system. After developing the DT
models, their fidelity should be carefully validated to ensure
reflecting the behavior of the physical twin before starting to
use them.
B. REAL-TIME DATA CONNECTION
Digital twinning relies on data for interlinking the digital
models with their physical counterparts. Data is gathered
through field measurements, IoT devices, and smart meters
from different system components, lines, buses, switches,
transformers, loads, storage systems, and so on. Besides, the
meteorological information such as the ambient temperature,
solar radiation, humidity, wind speed, and wind direction
are collected from the field or other data centers such as a
national/local weather station, adjacent interconnected sys-
tems, etc. However, handling a huge volume of data including
structured, unstructured, and semi-structured data received
from multiple resources with different resolutions is a chal-
lenging task.
After collecting the data, advanced data analysis tech-
niques are required to pre-process the noisy raw data and
enhance data quality. The relevant data is used to extract the
information required to update the models of different parts
of the system/process and share them with the unit/service in
need of it.
Data is transferred through reliable and secure commu-
nication systems. Identification of suitable communication
technologies is performed according to the communication
requirements of the target service and application. These
requirements can be classified into quantitative requirements
such as latency, reliability, coverage, data rate, and cost as
well as qualitative requirements including scalability, inter-
operability, flexibility, and security [43]. In this regard, dif-
ferent wired communication technologies, WiFi, WiMAX,
4G/5G, and satellite technologies or a hybrid communication
system can be considered for different purposes in DTs.
A detailed review of different communication technologies
and their specifications can be found in [43].
Fig. 3 presents the schematic view of a monitoring sys-
tem for real-time data collection of outdoor meteorological
parameters and renewable power production of a prosumer
including measuring devices, data acquisition system, com-
munication system, and servers. The weather station com-
prises an anemometer including both wind speed and wind
direction sensors, temperature and humidity sensors, a solar
radiation sensor, a UV radiation sensor, a pressure sensor,
and a rain sensor. Specifications of sensors are given in
Table 1. Besides, the prosumer meter is used to measure
real-time power production of the wind turbine (WT) and
PV system. Data is collected and transferred to be stored in
the server for further usage and analysis. As data is collected
TABLE 1. List of weather station sensors to measure meteorological
parameters [45].
from different sensors with different frequencies, unification,
alignment, and pre-processing of the raw data is of vital
importance to prepare the data for its intended application
such as WT and PV system monitoring and control, demand-
side management, EMS, etc. Interested readers are referred
to [44] for more information.
FIGURE 3. Schematic view of an exemplary monitoring system.
Real-time managing of a MGDT to keep it updated and
synchronized with the physical system is of vital importance.
Besides, the potential of MGDT to improve the situational
awareness (SA) of the system relies on the fast and efficient
processing of the large volume of real-time data for timely
detection of events before the system reaches critical condi-
tions or goes under a cascading catastrophe.
With recent advances in BDA, massive data could be pro-
cessed to extract valuable information from the raw data. As a
result, BDA plays an important role in digital twinning.
Thanks to the computing power and huge storage capa-
bilities of cloud computing, BDAs could be employed in
the digital twinning process for bad data filtration, dimen-
sion reduction, data fusion, data organization, data min-
ing, processing, and visualization [46]. Many big data-driven
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FIGURE 4. Physical system, digital system, and data exchange between them.
approaches have been proposed for different purposes in
smart grids over the recent years [46], [47]. In [48], cloud-
based BDA is used for the integration of discrete and contin-
uous technical and business data streams of wind farms. The
data visualization is done by using augmented reality (AR)
for wind farm monitoring and analysis.
Taking into account the sensitivity of the application to the
data transition latency and the level of integration of MGDT-
driven services (device-level, system-level, and so on), edge,
fog, and cloud computing platforms can be used. In this way,
real-time data connection and analysis can be implemented
at the desired level to support the data requirement of the
target service. For instance, embedded DT-based controllers
operation can be supported by employing device-level data
analysis to facilitate their prompt and decisive response.
A recent survey of deploying AI techniques at the network
edge and the proximity of the data source can be found
in [49]. Fig. 4 represents the interrelationship between the
physical system (process and components) of a MG and its
DT, and the data exchange between these two systems. Data
models represent an example of data that is gathered from the
components and transmitted to the MGDT.
C. DEVELOPING SYSTEMATIC WAYS TO
MODEL ADAPTATION
DT is a living model of a physical system, thereby preserv-
ing the models’ accuracy and consistency is of paramount
importance. However, this is a challenging task as the phys-
ical system operating conditions and the environment are
exposed to continuous changes over time. For instance, in a
MG, power consumption patterns of a region can change due
to several reasons such as changes in social and economic
circumstances, changing weather conditions, emergence of
new technologies and home appliances that can result in
divergence between the modeled output and the actual output
of the system. In this regard, continuous updating of the
models are among the main challenges of establishing a DT
for different systems/processes. Based on the real-time data
stream continuously collected through monitoring systems
and processed by data analysis methods, models are updated
throughout the systems’ lifetime.
Model adaptation can be implemented by tuning model
parameters/hyperparameters, engineering more features (in
case of using data-driven models) or optimizing the physi-
cal principles used for building the models [50] (in case of
deploying physics-based models). Besides, different trigger
strategies can be followed for model adaptation. Models can
be updated with a fixed periodicity that is defined based
on the observed changes in the historical data and experts’
knowledge of the system. Another more efficient strategy is
an event-based approach in which triggers are defined based
on the level of divergence between the model predictions and
the physical system outputs. The trigger event could also be
defined based on the drastic changes in the input data. The
model adaptation process will be triggered in case critical
boundaries are violated over a certain number of periods
as shown in Fig. 5. In this figure, each vertical line is a
measurement point. The retraining counter triggers when the
gap between the actual values and the model output (pre-
dicted values) is greater than the configured maximum error
tolerance and resets to zero if the maximum tolerance is not
crossed. Once the counter reaches a predefined tolerance n,
the retraining process is triggered. A general framework for
model adaptation is presented in Fig. 6. Regarding the model
selection in this figure, a model can be selected from a pool
of candidate models in a systematic process to best fit the
available data. The solution might be also to ensemble the
output of different models to reach the best performance.
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FIGURE 5. An example of model drift and triggering model updating
process.
FIGURE 6. A general flowchart for model adaptation.
The increase of models’ complexity and the high rate of
data arrival complicate the model adaptation task and call
for automating the process. Hence, built-in systematic ways
for automatic ingestion of real-time data and fast response to
model update triggers should be developed for DTs. Model
adaptation triggers should be defined in association with
DT-based services requirements and the fixed/time-varying
twinning rate. With the recent advancements in online and
incremental learning with ML algorithms and reinforcement
learning, efficient techniques can be developed for model
adaptation purposes. A review on continual lifelong learning
can be found in [51]. Statistical approaches using Bayesian
techniques have been also used for model updating inte-
gration [52]. In this approach, the posterior distribution of
model parameters is used as the new prior for updating the
knowledge of unknown system parameters based on the new
incoming data.
It is worth noticing that the MGDT is required to be
shared with different MG services with a variety of require-
ments. In this sense, sharing the required virtual models
with the desired level of abstraction and preserving the
consistency of different models are crucial. To share data
and inter-communication of DT-based services, efficient
FIGURE 7. DT modelling engine main functionalities.
and secure application programming interfaces (APIs) are
required to be deployed.
As a conclusion, adopting digital twinning approach,
instead of developing models, modeling engines with several
functionalities (see Fig. 7) are required to be developed.
The main steps of developing DT for MGs are summarized
in Fig. 8.
III. DIGITAL TWIN APPLICATIONS IN MICROGRIDS
During the last decades, with the increasing global concerns
over the depletion of natural resources and environmental
pollution along with technological advances in deploying
renewable-based energy sources, MGs have become an inte-
gral part of the modern power systems. According to the
US Department of Energy, a MG is defined as a group
of interconnected loads and distributed energy resourcess
(DERs) within clearly defined electrical boundaries that acts
as a single controllable entity with respect to the grid. A MG
can connect and disconnect from the grid to enable it to
operate in both grid-connected or island mode [53]. MGs’
mission is to enhance the performance of energy systems
in terms of system efficiency, life cycle cost, quality of
services, asset management, and sustainability. Accordingly,
optimality, autonomy, reliability, resiliency, safety, and being
environmentally friendly are among the main features of the
MGs operation.
During the last two decades, a large body of research has
been conducted to enhance the MGs operation addressing one
or some of these aspects [54], [55]. However, investigating the
role of the DT as a new tool to assist in the design, develop-
ment, and control of MGs and its effectiveness for enhancing
the performance of MGs is a quite new research area. In
this section, potential applications of the DT in MGs will
be explored and the recently published studies that applied
the DT concept to power system applications and specifically
MGs are reviewed. A general overview of the DT important
services in MGs is provided in Fig. 9.
A. MICROGRIDS DESIGN AND DEVELOPMENT
At the design and planning stages of MGs, the virtual models
can be first developed and even delivered in advance [6]. The
MGDT provides the designers with an advanced tool to assess
the MG performance from different points of view and under
various operating conditions. Therefore, the required changes
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FIGURE 8. Digital-twinning work flow.
FIGURE 9. Digital-twinning services in MGs.
can be made at the early stages of development [29]. Besides,
potential implementation risks can be identified and miti-
gated, thereby increasing the confidence in the final design.
NASA and US Air Force apply DT technology in vehicle
development in the product design phase [25]. Airbus Iron
bird is an example of developing a hardware twin integrating
the electrics, hydraulics, and flight controls of the aircraft in
an easy-to-access framework for design validation [56]. The
advantages of using MGDTs for the design purpose stems
from their capability to provide a high fidelity simulation plat-
form for designing, testing, and assessing MGs. Different sce-
narios ranging from normal operating conditions to extreme
events can be simulated to analyze the efficiency, reliability,
and resiliency of different designs. Besides, the appropriate
size and capacity of system equipment (generators, trans-
formers, lines, switches, converters, energy storage systems
(ESSs), renewable energy sources (RESs), and so on) and
the required reserve capacity can be efficiently determined.
This is extremely important in MGs applications in isolated
or hostile environments such as space MGs or terrestrial MGs
in remote areas and polar latitudes.
Regarding the planning (siting and sizing) of RESs, the
MGDT will be exposed to a similar environment that is
experienced by the real system simulated using the histor-
ical data. Besides, reliable models and advanced ML tools
are deployed to predict the output power of RESs. Digital
models will account for the uncertainty resulting from vari-
ations in wind speed, solar radiation, ambient temperature,
and other environmental characteristics. Thus, more accurate
investment plans can be made reducing the investors’ and
operators’ uncertainty to invest in green technologies. As a
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result, more renewable-based power will be introduced in the
system reducing electricity generation environmental impacts
and CO2 emission. The developed validated virtual model
could be handed over in advance to consider possible design
changes [6].
The MGDT provides an accurate representation of the
MGs’ load and its evolution over time. This is achieved
through deploying different very short-term, short-term,
medium-term, and long-term forecasting models of MGs
load [57] enriched with real-time data. Employing the
long-term load and RESs forecasting models with ESSs mod-
els provides the opportunity of revising the system design
in a low-cost environment. Besides, the degradation models
of RESs and ESSs are used to estimate their useful life
under realistic operating conditions. Such a comprehensive
reference model of the system will help design cost-effective
sustainable MGs with the lowest implementation risk.
In [58], a DT as a building information model is developed
to evaluate the net-zero energy building (NZEB) concept for
existing buildings. After creating the model, different analy-
ses are performed to calculate the cost-saving and payback of
NZEBs considering different technologies.
As a digital representation of the physical system, the
MGDTs can be employed in a variety of what-if scenarios
to simulate the state of the behavior of a MG in different
normal, emergency, or faulty operating conditions and record
the observed behavior. The result will be a valuable dataset,
which is difficult to obtain from the physical system with-
out compromising its safety. This dataset can be used as a
rich training dataset for training different ML models with
different purposes such as security analysis, fault detection,
and fault diagnosis or training human/autonomous operators.
The digital representation of the physical system, which is
used for simulating the system behavior in non-real-time
applications is called Digital sibling in [39]. In [59], a two-
phase fault diagnosis model is proposed. First, the model
is fully trained based on the train dataset generated by the
virtual model while at the second phase, the trained model is
migrated from virtual to physical space by using deep transfer
learning.
B. FORECASTING AND FLEXIBILITY IDENTIFICATION
Forecasting is one of the most significant tasks of MGs.
Specifically, considering the uncertain nature of the produced
power of RESs, developing efficient forecasters to determine
the MG available power is crucial. By improving the pre-
diction accuracy, the reliability of the energy supply will be
increased and the need for over-sizing system equipment and
deploying large-size reservoirs will be reduced. Therefore,
maximum resource utilization can be ensured. Further, accu-
rate predictions of the RESs available power will facilitate
their participation in ancillary services (frequency/voltage
regulation, reactive power support, black start services, etc.)
to ensure MGs reliable and secure operation [60], [61].
Different physics-based, data-driven, and hybrid tech-
niques for estimating the output power of RESs across various
timescales (very short-term [62], [63], short-term [64], [65],
medium-term [66], and long-term [67], [68]) can be found
in the literature [69]. In recent years, deep learning-based
methods are becoming increasingly attractive for predicting
meteorological parameters (wind, temperature, and radiation)
and RESs power estimation. Specifically, Recurrent Neu-
ral Network models consisting of Long Short-Term Mem-
ory (LSTM) and Gated Recurrent Unit (GRU), as well as
Convolutional Neural Networks (CNN) are among the widely
used data-driven techniques. An up-to-date overview and
classification of deep learning-based wind and solar power
forecasting methods can be found in [70].
In data-driven forecasting methods or physics-based and
hybrid models that require parameter tuning, adapting the
forecasting model with up-to-date data on an ongoing basis
results in an accurate dynamic forecasting method. Thereby,
an efficient data management system is needed to col-
lect, process, and systematically share the required data for
autonomous adaptation of the forecasting models.
Specifically, in case all the required data are not directly
collected from the field and need to be obtained from other
sources (total sky imagery, satellite imagery, meteorological
forecast, nearby sites, etc.), efficient interaction among vari-
ous data sources is essential (see Fig. 10). In this sense, DT is
recognized as an efficient tool to enhance the autonomy and
efficiency of forecasting techniques for MGs. In [60], a very
short-term temperature forecaster is proposed for estimating
the output power of PV systems. To do so, the temperature
dataset of nearby locations around the target site is also
used to improve the prediction accuracy. By adopting the
DT technology in this case, the interoperability of the nearby
solar farms and MGs will be enhanced through sharing the
up-to-date data efficiently. Besides, this example shows how
FIGURE 10. An example of using multiple-sources data for PV! power
forecasting in MGs.
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the DT of other systems in the proximity of a new-built site
can help to build its DT.
It is worth noticing that DT has a long history in meteoro-
logical institutes [39].
Similar to other systems that interact with human beings,
human behavior is one of the main sources of uncertainty
in MGs. With the increasing integration of residential WTs
and PV panels and decreasing the ESSs prices, consumers
take more active participation in MGs operation. Hence,
predicting their electricity usage patterns is becoming more
complex and challenging. Furthermore, with the introduction
of smart home appliances such as washing machines, dish-
washers, clothes dryers, electric water heaters, and air con-
ditioners, consumers can manage their power consumption
pattern more proactively and participate in demand response
(DR) programs.
In addition, increasing the integration of electric vehicles
(EVs) into MGs calls for advanced analytics tools to explore
their charging patterns. Also, with the growing vehicle-to-
grid (V2G) technologies, EVs offer many services as mobile
ESS to stabilize MGs [43].
Although the emergence of these new applications in the
consumers-side introduces new challenges and complicates
the operation management of MGs, it brings many advantages
to support their flexible and efficient operation. However,
effective utilization of these flexibility resources requires
up-to-date knowledge of the consumers’ facilities and their
power usage/generation patterns [47], [71]. Hence, a huge
amount of data is continuously collected from the metering
devices across the MG and processed for load profiling and
electricity usage pattern recognition. Besides, several studies
use the driving statistics and the collected data from charging
stations to model the charging patterns of EVs at homes or
public charging stations [72]. Accurate spatial and temporal
distribution prediction of EVs is very important for operation
management and planning of MGs and charging stations.
In this regard, the MGDT is expected to support the identi-
fication of the flexibility sources on the consumer-side and
power consumption patterns of MGs consumers in several
ways. First of all, the advanced data management system
offered by the MGDT enables the efficient organization and
process of the historical data and the real-time data stream
while preserving data integrity and privacy. Besides, the
MGDT provides a high-fidelity simulation platform for mod-
eling the prosumers’/consumers’ behavior. As a matter of
fact, the MGDT offers a dynamic and interactive platform for
modeling the consumers’ response to different stimuli such
as electricity price and DR incentives as well as predicting
their power consumption under different conditions (weather
conditions, time of day/week/year, etc.). In this sense, the DT
is perceived as a game-changing tool in human interactive
systems like MGs. Furthermore, the DT will facilitate the
systematic integration of different data sources and forecast-
ing methodologies. Using fusion techniques at the different
sensor, feature extraction, and decision levels will enhance
the accuracy of the forecaster outcome [73].
Electricity market price is another source of uncertainty
in MGs operations. Accurate forecasting of electricity prices
requires precise modeling of market dynamics and the inter-
action of different players which can be achieved in the light
of digital twinning.
C. CONTROL AND OPERATION MANAGEMENT
After validating the digital models accuracy, the MGDT can
be used as a powerful tool in a DSS for MG control and
operation management running in parallel with the physical
system [5]. The MGDT will assist the operators in MGs tran-
sients and steady-state assessment, detecting critical operat-
ing conditions, analyzing system performance, and making
fast decisions in response to changes in the system.
Besides, accessing the information of operating conditions
of individual components, their SoH, remaining useful life
(RUL), and degradation trend provided by the MGDT, the
supervisory controller can make the best operating schedule.
For instance, the EMS can reduce the stress on a storage
device by limiting its utilization and adjusting its operational
constraints (charging/discharging cycles, power limits, depth
of discharge, and so on) accounting for the RUL of the battery
received from its DT. These predictive actions can postpone
the maintenance and replacement time of equipment to a time
that results in the minimum cost and performance degradation
of the system. Besides, in case a second life is considered for
the component, for example using the battery of a satellite
or EV in a stationary application, the best transition time
can be scheduled- supporting the growing interest in circular
economy.
Using the MGDT, the performance of different control
strategies and operation management methods can be thor-
oughly studied. Exploiting the full potential of the mature
simulation environment, the effectiveness of the proposed
control techniques can be validated under various operat-
ing conditions. Even, the MGDT will help to evaluate the
effects of system operation management techniques on the
system lifetime and degradation trends. Hence, the required
corrections can be made in advance. This is one of the
most attractive applications of the DT in healthcare systems,
helping people to promote their lifestyles and take necessary
precautions to prevent disease to occur and prolong their
healthy life.
The DT will also improve the performance of remote
control systems. Remote real-time supervisory and control
centers can benefit from a highly reliable and dynamic model
of the physical system to adjust the control strategy. A good
example is a space MG (such as a spacecraft or a lunar habi-
tat [74]) where maintenance and replacement of system com-
ponents cannot be easily performed [75]. Hence, a reliable
control system, which can efficiently manage the resources
and distribute the power based on each subsystem operating
conditions, SoH and RUL is of vital importance.
In the light of DT-driven power generation and consump-
tion forecasting techniques, the uncertainty in the available
power of RESs and power consumption will be reduced. As a
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result, more efficient EMSs can be designed to improve MGs’
performance in terms of operation cost and environmental
impacts with maximum RES utilization. The DT will allow
modeling consumers behavioral patterns and their interac-
tion with the MG. Thereby, allowing the control centers to
implement advanced operation management and demand-
side strategies.
To ensure real-time operation of the controller in response
to dynamic changes, field programmable gate array (FPGA)
that provides low latency and massive parallelism can be used
for implementing the controllers [76]. In [20], a DT-driven
framework for online analysis of a large-scale power grid
(40k+ buses) is developed. The results show that using DT,
the response time from data acquisition to complete analysis
reduces from the current approximate time of 10 min to
60 sec. The proposed DT features in-memory and parallel
computing, complex event processing, and ML-based secu-
rity assessment.
D. OPERATOR TRAINING AND MG AUTONOMY
The MGDT provides an advanced platform to train the MGs
human and machine operators in a low-cost low-risk environ-
ment. Training the human operator in a dynamic environment
can result in broadening their experiences in controlling the
MGs operation especially under adverse and emergency oper-
ating conditions. Besides, the MGDT can be used to train the
human operators in MG maintenance services.
Thus, an efficient human-machine-interface (HMI) to
facilitate the simple interaction of the MGDT and human
operators should be developed. In this regard, virtual reality
(VR) and AR are attracting a grate deal of attention. A more
detailed discussion on VR, AR, and natural language pro-
cessing to implement the HMI can be found in [39] and the
references therein.
In autonomous systems, the automatic operator is trained
during the life cycle of the system by updating its experience
in a systematic learning process [20], [77]. Automatic learn-
ing will enhance the operators’ decision-making abilities
while the detrimental effects of wrong or inaccurate decisions
will be reduced.
System autonomy is defined as the capability of the system
in responding to unexpected events without the need for a
central reconfiguration and re-planning [23]. To improve
the autonomous characteristics of MGs, establishing a highly
reliable representation of the system as a reference model is
of vital importance. The MGDT will help the autonomous
control system improve its self-awareness by (a) representing
a holistic up-to-date dynamic view of the physical system
operating conditions and assets situation, and (b) providing
high-fidelity simulation platforms to simulate its evolution
and projecting its future condition. Relying on the built-in
feedback process of the DT, any discrepancy between the
physical system and its reference model will be detected
in a timely fashion, which could prevent the system from
severe damages. The built-in self-adaptive characteristic of
the MGDT will result in the adaptation of the digital model
and consequently the control strategies to the dynamic envi-
ronment in a systematic manner.
MGs self-healing which is also an important characteristic
of autonomous systems could be vastly improved with the
advent of digital twinning. In case of performance degra-
dation due to the fault occurrence or an unforeseen change
(load changes, generation loss, line outage, etc.) in the sys-
tem, a DT-based DSS can recommend or implement (in
case of fully autonomous systems) the required actions to
mitigate potential damages. A switching control strategy can
be implemented to change the operating strategy from normal
to abnormal and urgent operation and activate self-healing
mechanisms after detecting an anomaly in system operation.
For instance, by continuously monitoring the state of several
indicating parameters identified through what-if scenarios as
part of the training process. A hierarchical operation manage-
ment scheme for multi-microgrid systems during emergency
conditions is proposed in [78].
Equipment-embedded DT-based DSSs will support the
system self-healing through optimizing the operating strategy
at the device level without operator intervention. Edge and
fog computing platforms present a promising solution to
enhance self-healing due to the lower latency.
Last but not least, in case of communication failure or data
loss, a recent record of the system state of behavior provided
by the MGDT will help the operators to tolerate the abnormal
situation and restore the system to a normal (sub-optimal)
condition.
E. STATE OF HEALTH MONITORING AND
PREDICTIVE MAINTENANCE
Aging is an inevitable phenomenon in every physical system,
which can result in degrading the system performance and
increase the operating cost in the long term. Hence, capturing
system degradation and aging management are among the
main requirements of MGs. Besides, to make sure that all sys-
tem equipment meets the operating requirements, an effective
maintenance procedure is required. Therefore, MGs and in
general power systems require accurate inspection and timely
repairing and replacement of components to preserve the
service quality and continuity. In this sense, a large share of
the MGs cost is related to their periodic maintenance that
significantly increases for equipment and components, which
cannot be easily accessed, for instance offshore WTs or MGs
in geographical islands and rural areas.
To improve the MGs reliability and prolong their equip-
ment lifetime, advanced monitoring systems are required
to analyze components conditions during operation time.
Further, knowing the exact location of the system’s assets
(both personnel and components) will considerably enhance
operation management of the system especially under adverse
operating conditions.
The digital twinning concept can be applied to the condi-
tion and SoH monitoring of systems and equipment. From
an asset management point of view, DT is a powerful tool
for locating the assets and early detection of anomalies in
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asset performance and call for predictive actions. Hence,
periodic and preventive maintenance will be replaced with
predictive maintenance resulting in more efficient and less
costly maintenance procedures.
The MGDT will help to integrate the available information
obtained from analyzing historical and real-time data with
analytical models. Hence, informative indices representing
the current state of the assets and their future projection can
be evaluated and visualized through the HMI. Therefore,
it provides a platform to closely monitor SoH of the assets
and estimate their RUL. This information will be shared
with control centers and maintenance management systems
to adjust the operating strategy and prepare the optimum
maintenance schedule, respectively.
In [13], a physics-based DT with 3-D models are developed
for an automotive braking system for heat monitoring and
predictive maintenance purposes. In [79], a data-driven DT is
used to estimate the speed loss of the ship’s hull and propeller
due to the marine fouling. The deep learning method is used
to develop the DT using the data gathered from the onboard
sensors during different operational and environmental con-
ditions when the fouling is not present. The RUL of the power
converter of fixed and floating offshore WTs is estimated
using DT in [80]. Thermal loading of power converters due
to the environmental conditions, the mechanical structure of
WTs, and the electrical system are modeled to govern the
IGBT junction temperature and its fluctuations. In [81], the
DT concept is adopted for degradation assessment and SoH
monitoring of a Lithium-ion battery pack in a spacecraft. The
State of charge (SOC) of the battery cells is estimated by
using Kalman Filter - Least Squares Support Vector Machine
algorithm. While the SoH of the battery pack is evaluated
via the Auto Regression model-Particle Filter algorithm using
real-time and historical data. In [82], DT-based supervision
of automotive battery systems throughout different life cycle
stages of production, utilization, second life usage, and recy-
cling is presented. In [83], the DT concept is used to develop
an electric-thermal model of a battery system that, in com-
bination with an aging model, is used to monitor the battery
degradation and calculate the residual value for the potential
second-life applications. The DT-based SoH monitoring and
predicting the RUL of the traction motor of EVs is studied
in [84]. Both In-house and remote monitoring approaches
are considered. The model can assist the user and service
companies to find the best time to refill the bearing lubricant.
F. FAULT DIAGNOSIS
Fault diagnosis is an important task in MGs. Detecting the
fault occurrence, identifying the fault type, and prescribing
the required actions are among the most significant steps after
a fault event. An efficient fault diagnosis system improves
the MG reliability by reducing the system downtime and
associated consequences such as loss of load, outage cost,
and system stress among others. Since physical access to
the system is difficult in many cases due to the complex
installation, hazardous situation, and time-consuming and
costly procedure, reliable and cost-effective fault diagnosis
methods are highly required [85]. In this sense, the MGDT
as a high-fidelity model of the real system running in parallel
can be used to detect any malfunctioning of different parts of
the system as well as controllers and sensors. Besides, faulty
operation of the system can be detected by continuously com-
paring the system performance with the reference behavior.
Preparation of the DT to support fault injection is studied
in [86]. In [85], the DT concept is applied to develop a fault
diagnosis system for a PV energy conversion unit consisting
of a PV panel and the power converter. Ten different faults
in the PV panel, power converter, and electrical sensors are
considered. The authors also used the DT to create the fault
signature library. In [2], a DT reference model for fault diag-
nosis of a rotating machinery is proposed. Besides, to improve
the adaptability of the model, a model updating strategy
is provided. In [76], to detect abnormalities in the physi-
cal subsystems of a power converter, a controller-embedded
DT-based diagnostics monitoring system is proposed. The
digital models are embedded with the controller and ben-
efit from the computational capability of FPGAs. In [87],
a DT-based approach is proposed to localize the imbalance
state of the rotor system and predict the rotor temperature in
an electric drive train.
G. MICROGRID SECURITY, RESILIENCY, AND
SITUATIONAL AWARENESS
MGs have two interdependent layers namely the physical
layer and the cyber layer. Consequently, to ensure the secure
operation of the system, MGs should be protected against
potential threats in both layers [88].
System security is defined as reducing the risk of the
system critical infrastructures damages from natural disas-
ters or adversarial hazards (intrusions, malicious attacks,
etc.) [89]. Dynamic security analysis is essential for the safe
operation of MGs. Digital twinning will provide a platform
to identify and simulate possible attack scenarios in MGs.
Thus, timely detection of potential situations that might lead
to insecure operation can be achieved by either relying on
data-driven methods or projecting the system behavior using
the DT-based simulation platform. Accordingly, the required
remedial actions will be prescribed. Besides, the MGDT will
support constant improving of the MGs security considering
new threats in an automatic manner.
In [88], a DT named ANGEL is developed for the CPS
security of MGs. To mitigate component failures and cyber
attacks, the potential of ANGEL for protecting both the phys-
ical and the cyber layers of MGs are discussed. The IEEE
39-bus system is used as a benchmark. In the Agile secu-
rity (AgiSec) methodology introduced in [90], a comprehen-
sive attack graph representing all the potential cyber attack
paths is constructed and automatically updated. Based on
the attack graph, remediation requirements to avoid the most
probable attacks are identified. Integrating this method with
the DT concept will address AgiSec’s concerns over the lack
of comprehensive models of system processes. In [91], the
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real-time security risk assessment is studied in the State Grid,
China in a DT-based framework. The physical and virtual
systems are connected through the SCADA RTU system.
One of the key security and resilience aspects of MGs is
SA. The perception of a system and associated subsystems
in relation to its environment and projection of its states in
the near future is defined as SA [21], [46], [92]. Consider-
ing the growing complexity and inter-connectivity of energy
systems, SA is of vital importance for system operators and
DSSs. With enough SA, system operators will be able to
take the required actions on time to prevent fault propagation
and minimize its impacts on operation of their responsibility
area as well as adjacent interconnected networks [93], [94].
Effects of SA on the reliability of power systems are dis-
cussed in [93]. The MGDT supports SA of MGs in several
ways. First of all, it facilitates the handling of enormous
data in a systematic manner applying advanced data ana-
lytics techniques for data pre-processing, outlier detection,
storage, etc. as discussed in previous sections. Besides,
using high-fidelity models and DT-driven simulation plat-
forms support providing a more accurate picture of the system
and a higher level of comprehension of the current and future
state of the system. Such visibility can be complemented by
the DT HMI solutions (3D visualization, AR, VR, etc.) for
better interaction and training of system operators. Enhancing
cyber SA using digital twinning concept is studied in [95].
SA plays an important role in improving the MGs’
resilience. System resilience is defined as the ability of the
system to anticipate high-impact low-priority (HILP) events,
rapidly recovering from these events, and learning lessons for
adapting system operation and structure to be better prepared
for future events [96]. In [96], fundamental concepts of power
systems resilience and key resilience features of power sys-
tems at different states of event progress from pre-disturbance
to post-restoration states are thoroughly discussed. These
fundamental properties are Anticipation, Absorption, Recov-
ery and Restoration, and Adapting after damaging events.
To be able to anticipate the HILP events and rapidly recover
from them, MGs should boost their SA and operational flex-
ibility to take timely preventive, corrective, and restorative
actions [96], [97]. The MGDT enables operational flexibility
of MGs by providing them with:
• Improved SA and accurate up-to-date digital represen-
tation of the state of the system presented through
advanced visualization tools,
• An advanced asset (personnel, stationary and mobile
distributed resources, voltage control support equipment
(reactors and capacitors) [61], etc.) management system
with accurate information of assets location and status,
• A high-fidelity simulation platform to project system
behavior and assess the effects of preventive, corrective,
and restorative actions,
• Advanced highly trained DSSs to prioritize re-
energizing MGs lines and components and coordi-
nate restoration actions based on the adaptive training
techniques,
• Automatic update of event models using MGDT mod-
eling engines and adaptation of preventive, corrective,
and restorative actions by properly training of the DSS
for future events using advanced ML techniques.
Further, DT-DT communication enables advanced operation
coordination of neighboring MGs as well as interdependent
infrastructures such as electrical systems, gas networks, water
supply systems, transportation and communication systems,
etc. Considering the inter-dependency of different networks
is a critical and challenging task of restoration from outages
caused by natural disasters [97].
Regarding cyber resiliency, restoring the recently updated
models and relying on soft sensors in case of loss of sen-
sors can help operators to maintain/recover system operation.
Fig. 11 adapted from [96] represents a visual tool to show
’’qualitatively’’ how the MGDT can help to enhance the
resilience operation of MGs by reducing the level of degrada-
tion, speed of the resilience degradation (slope of lines), and
duration of different phases. It is worth noting that Fig. 11 is a
qualitative figure for visualizing how the improvements will
affect the resilience trapezoid, which needs to be supported by
a quantitative analysis that is the scope of future research of
the authors. The roles of MGDT in boosting MGs’ resiliency
in different phases of the catastrophic event progress are
summarized in Table 2. Using the MGDT, different kinds
of threats including natural (hurricanes, storms, earthquakes,
etc.), technical (grid outage, generator, power or communi-
cation line failure, ESS damage, etc.), and human-induced
hazards (cyber-attack, malicious attacks, etc.) can be ana-
lyzed. The results will help operators to have a more accu-
rate classification of abnormal situations and investigate the
system behavior under different adverse operating condi-
tions. Efficient mitigation strategies in pre-, during, and
post-fault/disaster phases can be devised and organized in
different forms such as rule-based methodologies, look-up
tables, and technical procedures. Therefore, more efficient
preventive actions, as well as absorption and recovery from
different system faults and disruptive events, can be taken.
Specifically, in situations where a catastrophic phenomenon
is propagating quickly, the reaction of system operators could
be significantly improved by receiving assistance from the
DT-based DSS. Besides, these operating strategies can be
used to train the DSS for future events. The proposed
DT-enabled conceptual model to enhance the MGs resilient
operation is presented in Fig. 12.
In [98], a digital replica of a power system is used to
detect the event of fault-induced dynamic voltage recovery
and predict the post fault dynamic behavior. It is proposed that
with the timely prediction of the fault dynamic in a faster than
the real-time digital replica, the appropriate control action
such as under-voltage load shedding could be determined.
For validation purposes, the real system is simulated in the
real-time digital simulator (RTDS) with RSCAD software,
while Digsilent PowerFactory software is used for simulating
the digital replica. The concept of DT is used for DERs
and distributed controller design in [99]. DT is deployed
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TABLE 2. MGDT role in boosting MGs resiliency.
in [100] for assessing the MGs controllers performance in
terms of reliability, resiliency, and efficiency. Digital twin-
ning approach is also followed in [101] to evaluate the MGs’
resilient operation and identify potential risks before con-
structing the MGs. Oak Ridge National Lab is studying the
cyber attack issues and physical damage imposed by weather
conditions. DT is used to cut the power in parts of the grid
that might result in cascading failures [102]. Edge-deployed
DTs are developed by ABB to provide a virtual simulation
environment for real-time performance assessment to boost
resilience operation of the MGs [103].
H. EXPANSION PLANNING AND POLICY-MAKING
Power systems are continuously undergoing changes in
the power generation capacity and technologies, which are
mainly due to the increase of the power demand and new
regulatory rules. In this regard, the expansion planning of
energy systems has always been among the key issues of
both academia and industry. Expansion planning is a strategic
decision that affects the economic benefits of power com-
panies and their level of competence. Therefore, the expan-
sion decision should be made with a sufficiently accurate
prediction of the system behavior in the long-term regarding
demand growth, technology trend, and possible changes in
regulatory rules among others. In this sense, the MGDT can
provide a highly reliable and less costly platform to model the
MG ecosystem and perform long-term simulation analyses to
find the best time and strategy for the expansion planning.
Furthermore, the main concern over making new poli-
cies or changing the current strategies has always been the
response of different subsystems and players being affected
by the associated consequences. Besides, the compatibility
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FIGURE 11. Microgrids resilience adopting MGDT concept.
FIGURE 12. Proposed DT-driven MG resilience enhancement conceptual model.
of the long-term outcomes of the new policies with the
policymakers’ intentions is required to be analyzed before
proceeding to implement them. In light of MGDTs, an effi-
cient testbed is provided to predict the system’s response to
different future scenarios in different time horizons. Relying
on a dynamic and highly reliable model, short-term and
long-term impacts of different incentives, DR strategies, and
electricity price schemes can be studied. Besides, opportuni-
ties and barriers for adoption of new technologies, such as
the substitution of conventional diesel generators by hybrid
ESSs and RESs or impacts of high integration of EVs and
the hosting capacity of electricity distribution grids can be
thoroughly analyzed. As a conclusion, Table 3 presents an
overview of recent studies on MGs and DERs with digital
twining approach.
IV. LOOKING TO THE FUTURE
Advances in information, communication, and sensor tech-
nologies make the DT a new paradigm for the digitalization
of many industries including power systems. However, tak-
ing full advantage of digital twinning in MGs requires
the synergy among different fields of expertise to create a
digital ecosystem interconnecting data, software, and hard-
ware [104].
• Considering the significant role of data in establishing
the DTs, well-developed infrastructures for collect-
ing high-quality and high-resolution data and analyz-
ing them are required. Although the existing IoT and
monitoring platforms in MGs provide a good founda-
tion, to enhance the efficiency of data analysis, sensor
nodes require to be enhanced to perform some local
analyses, which demands more advanced monitoring
infrastructures.
• To reduce the data transmission latency and high band-
width requirements and to enhance data privacy, edge
intelligence (implementing the AI at the network edge)
is recognized as a promising solution to perform data
analysis in the proximity of the data source [49]. In
MGs, due to the growing integration of DERs and
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TABLE 3. Classification of recent studies on MGs and DERs with digital
twining approach.
electricity users, edge computing has been attracting a
great deal of attention over the last few years. Fault
detection, monitoring SoH of the electrical equipment,
and power quality services are among the tasks that can
be totally/partially assigned to the edge [105]. Accord-
ingly, integrating the strength of edge technology and
DT in MGs is a promising research area for future
studies.
• Realizing the MGDT requires high-performance hard-
ware and software infrastructures for executing the AI
algorithms and solving mathematical models in the
required time. Using FPGAs and relying on parallel
computing and on-demand cloud services and graphics
processing unit (GPUs) [106] are among the current
solutions for this issue.
• Standardization of the DTs modeling, data storage [35],
[104], communication among different entities (DT-DT,
DT-service center, DT-data source, and so on), as well
as security of MGs’ data and digital assets deserve the
attention of industrial and research societies.
• Regarding cybersecurity, recent advances in the
blockchain technology in MGs provide a promising
solution for the advanced tracing of digital assets and
minimizing the risk of tampering with data records and
information [107]. Transparency and security provided
by blockchain technology will increase the trust for
data and information sharing among different MGs
applications and authorized entities. Thus, blockchain-
based data management for DTs need to be further
explored in the context of MGs.
• Another important feature of MGDTs, which demands
their modular design is related to DT-DT interconnec-
tion and communication requirement. In this sense, DTs
that are developed for the neighboring subsystems (such
as different MGs in a multi-microgrid system, neigh-
boring wind, and solar farms, etc.) or interdependent
infrastructures (such as electricity, transportation, natu-
ral gas, communication, water, and heating supply sys-
tems, etc.) could be efficiently linked and provide the
systems with an unprecedented level of interoperability
and synergy. Accordingly, a cooperation platform will
be created, which offers enormous potentials to boost
power systems efficiency, performance, and resilience
operation.
V. CONCLUSION
This paper aimed to introduce the MGDT concept and the
applications of digital twinning in MGs. The concept of DT
and its key characteristics were reviewed and the key enabling
technologies for digital twinning were explored. The need for
the MGDT stems from the growing complexity of electrical
systems and equipment, which requires their close inspection
and timely maintenance. Specifically, those assets, which are
not easily accessible, require real-time remote monitoring and
predictive maintenance. Furthermore, with the extensive inte-
gration of data acquisition technologies into the MGs and the
availability of high-frequency high-quality data, systematic
ways to manage the data are highly required. Accordingly, the
operation strategies can be dynamically adapted to improve
the system performance. The increasing penetration of RESs
into the MGs and the emergence of prosumers are also
demanding accurate dynamic forecasting techniques as well
as automatic learning of behavioral patterns of prosumers.
Finally, the growing dependency of other critical infrastruc-
tures such as healthcare, transportation, telecommunication,
water systems, etc. on electric systems demands a highly
reliable supply of energy with minimum service interruption
and downtime.
The MGDT in its fully developed form will provide a
well-structured and systematic way for information and data
management of systems’ assets, which facilitates their close
tracking during their lifetime. Besides, the information stored
in the standard format can be shared among authorized enti-
ties and stakeholders to be used for different analyses.
The MGDT will support the accurate prediction of RESs
power supply and prosumers’ behavior taking advantage of
well-structured historical and real-time data and high-fidelity
models. Benefiting from the enhanced SAs and predictive
maintenance provided by the MGDTs, the MGs resilience can
be noticeably improved and the system/asset lifetime can be
extended. Accordingly, the MGDTs will reduce the operation
cost, improve the performance of the underlying physical
systems, and enhance the sustainability of the MGs. Although
2298 VOLUME 10, 2022

many kinds of research have been conducted during the recent
years, digital twinning in MGs is still in its infancy and a long
way must be paved to take full advantage of its promise.
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NAJMEH BAZMOHAMMADI (Member, IEEE)
received the B.Sc. degree in electrical engineer-
ing and the M.Sc. degree in electrical engineering
(control theory) from the Ferdowsi University of
Mashhad, Iran, in 2009 and 2012, respectively, and
the Ph.D. degree in electrical engineering (con-
trol theory) from the K. N. Toosi University of
Technology, Tehran, Iran, in 2019. She is cur-
rently a Postdoctoral Fellow with the Center for
Research on Microgrids (CROM), AAU Energy,
Aalborg University, Denmark. Her current research interests include digi-
tal twins, modeling and control of dynamic systems, optimization, model
predictive control, and its application in energy management of hybrid and
renewable-based power systems and life support systems.
AHMAD MADARY received the B.Sc. degree
in mechanical engineering from the Iran Univer-
sity of Science and Technology and the M.Sc.
degree in mechanical engineering (applied design)
from Shiraz University, Iran, in 2009 and 2013,
respectively, and the Ph.D. degree in electrical
engineering (control theory) from Tarbiat Modares
University, in 2021. He was a Visiting Researcher
at the Computer Science Department, Aalborg
University, Denmark, in 2018. He is currently a
Technical Assistant with the Department of Mechanical and Production
Engineering, Aarhus University, Denmark. His research interests include
robotics (parallel and serial robotic manipulators), modeling, control, and
safety of dynamical systems, system identification, mechatronics, hybrid
systems, embedded controllers, and microcontrollers.
VOLUME 10, 2022 2301

JUAN C. VASQUEZ (Senior Member, IEEE)
received the B.Sc. and M.Sc. degrees from UAM,
Colombia, and the Ph.D. degree from UPC, Spain.
In 2019, he became a Professor in energy internet
and microgrids. Currently, he is the Co-Director
of the Villum Center for Research on Microgrids.
He has published more than 450 journal articles,
which have been cited more than 19000 times.
His research interests include operation, control,
energy management applied to AC/DC micro-
grids, and the integration of the IoT, energy internet, digital twin, and
blockchain solutions. He has been a Highly Cited Researcher, since 2017,
and was a recipient of the Young Investigator Award, in 2019.
HAMID BAZ MOHAMMADI received the bach-
elor’s degree in electrical engineering and the
Master of Management degree in artificial intel-
ligence. He is currently a Senior Mobile Network
Optimization Engineer and a Data Scientist with
TELUS Mobility, Canada. Before joining TELUS
in 2017, he had worked with Huawei and Erics-
son for 11 years. His past 15 years of experience
include the design and optimization of different
generations of mobile networks (2G to 5G), tech-
nical teams leadership, and building AI-based models to analyze the perfor-
mance of the networks. In his AI experience, he has contributed to several
AI projects inside and outside of the mobile networks domain, with a focus
on anomaly detection in geographically distributed timeseries data.
BASEEM KHAN (Senior Member, IEEE) received
the B.Eng. degree in electrical engineering
from Rajiv Gandhi Technological University,
Bhopal, India, in 2008, and the M.Tech. and
D.Phil. degrees in electrical engineering from the
Maulana Azad National Institute of Technology,
Bhopal, India, in 2010 and 2014, respectively.
He is currently working as a Faculty Member at
Hawassa University, Ethiopia. He has published
more than 100 research papers in well reputable
research journals, including IEEE TRANSACTIONS, IEEE ACCESS, Computer
and Electrical Engineering (Elsevier), IET GTD, IET PRG, and IET Power
Electronics. Furthermore, he has published, authored, and edited books
with Wiley, CRC Press, and Elsevier. His research interests include power
system restructuring, power system planning, smart grid technologies, meta-
heuristic optimization techniques, reliability analysis of renewable energy
systems, power quality analysis, and renewable energy integration.
YING WU received the Ph.D. degree from
Northwestern Polytechnical University, China,
in 2014. From 2006 to 2011, she was a Software
Engineer engaged in the research and devel-
opment of interactive system architecture and
large-scale complex network systems with the
Hewlett-Packard Global Research and Develop-
ment Center, Shanghai, China. She is currently an
Associate Professor at Xi’an Shiyou University,
China, and a Postdoctoral Fellow with the Center
for Research on Microgrids (CROM), AAU Energy, Aalborg University,
Denmark. Her research interests include the digitalization and distributed
control of networked energy systems, the Internet-of-Things-based micro-
grid architecture, and communication and control in the energy internet.
JOSEP M. GUERRERO (Fellow, IEEE) received
the B.Sc. degree in telecommunications engineer-
ing, the M.Sc. degree in electronics engineering,
and the Ph.D. degree from the Technical Univer-
sity of Catalonia, Barcelona, in 1997, 2000, and
2003, respectively. Since 2011, he has been a Full
Professor with the Department of AAU Energy,
Aalborg University, Denmark. In 2019, he became
a Villum Investigator by the Villum Fonden, which
supports the Center for Research on Microgrids
(CROM), Aalborg University. His research interests include oriented to
different microgrid aspects, including applications as remote communities,
energy prosumers, and maritime and space microgrids.
2302 VOLUME 10, 2022

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