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Abstract and Figures

The Digital Twins offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewable resources at the edge of the grid. The digital twins are leveraging technologies such as the Internet of Things, big data analytics, machine learning, and cloud computing, to analyze data from different energy sensors, view and verify the status of physical energy assets and extract useful information to predict and optimize the assets performance. In this paper, we will provide an overview of the Digital Twins application domains in the smart grid while analyzing existing the state of the art literature. We have focused on the following application domains: energy asset modeling, fault and security diagnosis, operational optimization, and business models. Most of the relevant literature approaches found are published in the last three years showing that the domain of Digital Twins application in smart grid is hot and gradually developing. Anyway, there is no unified view on the Digital Twins implementation and integration with energy management processes, thus, much work still needs to be done to understand and automatize the smart grid management.
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An Overview of Digital Twins Application Domains
in Smart Energy Grid
Tudor Cioara, Ionut Anghel, Marcel Antal, Ioan Salomie
Claudia Antal
Computer Science Department
Technical University of Cluj-Napoca
Cluj-Napoca, Romania
{tudor.cioara, ionut.anghel, marcel.antal, ioan.salomie,
Arcas Gabriel Ioan
Engineering Powertrain Systems
Bosch Engineering Center
Cluj, Romania
AbstractThe Digital Twins (DTs) offer promising solutions
for smart grid challenges related to the optimal operation,
management, and control of energy assets, for safe and reliable
distribution of energy. These challenges are more pressing
nowadays than ever due to the large-scale adoption of distributed
renewable resources at the edge of the grid. The digital twins are
leveraging on technologies such as Internet of Things, big data
analytics, machine learning, and cloud computing, to analyze data
from different energy sensors, view and verify the status of physical
energy assets and extract useful information to predict and optimize
the asset’s performance. In this paper, we will provide an overview
of the DTs application domains in the smart grid while analyzing
existing the state-of-the-art literature. We have focused on the
following application domains: energy asset modeling, fault and
security diagnosis, operational optimization, and business models.
Most of the relevant literature approaches found are published in
the last 3 years showing that the domain of DTs application in
smart grid is hot and gradually developing. Anyway, there is no
unified view on the DTs implementation and integration with
energy management processes, thus, much work still needs to be
done to understand and automatize the smart grid management.
KeywordsDigital twins, energy, smart grid, review, energy
assets modeling, business models, energy services
The increasing trend for intermittent Distributed Energy
Resources (DER) adoption and deployment at the edge of the
energy grid is posing serious challenges concerning the
operation, management, and control for safe and reliable
distribution of energy. Digital Twins (DT) offer promising
solutions for these challenges, being one of the top
technological trends according to Gartner [1]. The concept of
DT is used to designate the virtual model of a physical asset.
It was becoming increasingly popular with the digital
transformation of manufacturing and distribution brought by
Industry 4.0. Having a virtual model, one can apply data-
driven analytics, run simulations, and what-if analyses to
determine the asset behavior in real or hypothetical situations
or to decide on control and optimization actions. Then the
lessons learned can be applied to the physical asset manually
or using actuators.
Lately, the energy ecosystem digitalization has become an
important driver for achieving the targeted goals for
decarbonization, cleaner energy, and efficient and secured
resource management. Innovative technologies such as
blockchain, or digital twins are considered the key to the
transition of energy and utilities towards Energy 4.0.
transition process. In particular, DTs integrate and leverage on
technologies such as IoT, big data analytics, machine learning,
and cloud computing, to analyze data from different sensors,
view and verify the status of the physical energy asset and
extract information to predict and optimize the asset’s
performance. They are used to virtually represent DERs and
other energy assets and can be used in smart energy grid
scenarios to conduct various analyses and process simulations
concerning energy production, distribution, or consumption
behavior [2]. Their adoption is driven by the transition process
towards a sustainable and zero-carbon energy sector which
aims for optimal integration and usage of renewables, lower
carbon footprint, and improved energy efficiency.
In this context, the DTs allow the development of new
energy services and more decentralized business models
where citizens and energy resources are becoming key active
players acting as prosumers and contributing to grid
sustainability goals [3]. DTs can be applied for digitalizing
various grid management processes such as energy
production/consumption monitoring, load prediction,
decision-making for energy management, balancing the
supply and demand, energy security, and operation
optimization [3-5]. All the above features make DTs one of
the most promising emerging technology for fueling smart
energy grid development and decentralization, while at the
same time supporting the implementation of the next
generation of energy services. Their implementation and
uptake are facilitated by cloud computing which plays an
important role in integrating the energy assets from electrical
power systems with the data networks, offering potentially
unlimited computational and storage capacity. Consequently,
lately, several papers have been published on DTs
applications in the smart energy grid.
In this paper, we will provide an overview of the
application domains of digital twins in the smart grid while
analyzing existing state-of-the-art literature. For organizing
the review, we will focus on the most relevant application
domains as defined by ETIP SNET [2]:
Asset Model (DTs for energy and performance
assessment and management),
Fault Model (DTs for diagnosis of errors/faults and
security issues),
Operational Model (DTs for optimal energy
distribution, energy efficiency, and cost reduction),
Business Model (DTs for innovative energy services
and new business models).
The rest of this paper is structured as follows. Section II
describes a general architecture for integrating DTs in the
smart grid and the technology enablers, Section III presents
DTs application domains in the smart grid organized in four
relevant ones, while Section IV presents the paper’s
Figure 1 shows an architecture for integrating and using
DTs in smart grid applications. Several steps need to be
implemented by leveraging on state-of-the-art technologies.
The first step is the creation of the DT model based on the
actual measurements done on the energy assets [6]. Various
characteristics can be of interest such as active of reactive
power, frequency, current, energy flexibility, etc. The energy
asset model is constructed by using physics or engineering
models, statistics, etc. Augmented reality models can be used
for improving the human understanding of the energy assets'
operational states [7].
The next step addresses the coupling and integration of the
DT model with the physical assets using energy sensors and
actuators. The IoT sensors will be used to acquire various
information regarding the energy assets operation while the
actuators are used to enforce actions for control and
optimization. The data monitoring will provide the software
infrastructure for processing and aggregating the data received
from the energy sensors.
Fig. 1. DTs in smart grid applications
The third step aims to provide the technological support
for running data-driven analytics on the data collected about
energy assets. The smart grid can generate huge amounts of
heterogeneous data thus big data analytics and machine
learning are usually employed to efficiently handle them [8],
[9]. Data generated by the electricity network, weather, or
geographical data can be used to improve energy services
delivery. The amount of data that is acquired may pose
challenges regarding efficient storage and analysis using
conventional approaches [10]. Additional challenges are
given by the velocity at which new data is generated and
moved, the variety in terms of heterogeneity of data that can
be integrated, and finally the potential noise and lack of
reliability of the acquisition process.
By twining data with accurate DT models of energy assets,
valuable insights or information can be extracted and
integrated within many smart grids’ applications like
operation maintenance, energy prediction, security and
protection, errors detection, etc. In this context, machine
learning can offer a series of benefits such as efficient load
forecasting, better support for decision making or pattern
detection. This kind of algorithms may be integrated with all
processes and components that assure the smartness of the grid
and offers better reliability in guaranteeing the quality and
efficiency in energy delivery and may support the
implementation of advanced services such as demand
response. Machine learning allows for real-time data analysis
and prediction of prosumers' energy demand or energy
generation by only looking at the energy profiles. To improve
accuracy and reduce model uncertainty, the energy-related
features, such as demand, generation, the baseline can be
fused with non-energy related vectors and contextual features
determined using simulations of energy assets DTs.
Finally, the gathered insights are used for optimizing and
controlling the operation of the energy assets. Model-based
predictive control can be employed on both linear and non-
linear systems [11], [12]. The energy assets are measured and
compared to the DT model built and can be used on what if
decision making to optimize the future operational states. DT
models and predictive control offer promising solutions for
optimizing energy efficiency, improving the performance of
the energy assets, and deciding on strategies to anticipate
negative events that can affect the energy grid resilience.
A. Energy assets modeling
Energy assets modeling has an important role for smart
grid operators since it allows assessing and evaluating the
performance of the grid ecosystem and provides new ways for
better managing the energy demand, generation, and
distribution. Major industry players such as ABB [20] or
Honeywell [19] have successfully approached the
management of grid assets using DTs.
DTs allow the implementation of complex performance
assessment models and for virtually visualize the grid
resources energy behavior [3]. The development of real-time
analysis of complex energy systems is proposed in [13]. The
authors define a framework for DT representation by virtually
modeling the grid snapshot states using DTs and applying
machine learning algorithms to conduct performance
assessments. In [17] the authors propose a platform for smart
city energy management leveraging on DTs. Smart meters are
used to gather assets data that is fed to DT virtual models of
buildings with the final goal of benchmarking the building
performance in terms of energy efficiency. In [25] the authors
propose the development of a DT-based framework for energy
demand management. The real-time data gathered from
buildings is virtualized through DTs while ML models are
used to offer insights to grid operators. Simulations on the DT
models allow to observe demand curves and to safely manage
grid response.
In other domains such as manufacturing, DTs can help to
virtualize resources for achieving fine-grained energy
consumption management and evaluating the energy
performance of specific equipment by conducting simulations
[22]. DTs are key technology enablers to build accurate
models of the physical grid and carry out simulations to
determine potential energy service interruptions [26]. In this
way, the running of tests directly on the grid is avoided and
continuous assessment platforms can be developed.
In the case of renewable energy resources, the
development of DTs poses challenges such as independent
monitoring of their operation to reflect their real-time
functionality, the different time and scale granularity, or the
intermittence of generation [16]. Mathematical models are
defined to represent measurable parameters of photovoltaic
systems and to further run simulations to evaluate their
generation performance [14]. Similarly, wind farms and wind
turbine management can be optimized by representing these
assets as DTs, estimating in real-time their performance, and
managing their health [15]. Characteristics and operational
parameters of such assets are represented in virtual spaces and
then further processed. DTs are used for representing energy
storage systems virtually and assessing their performance to
define operation scheduling plans before proper operation [4].
They are built upon real operation parameters of energy
storage systems and simulations are used to define
charging/discharging schedules based on ML techniques and
decision trees.
Electric Vehicles (EVs) are growing in numbers posing
serious concerns for smart grid operators. Studies highlight the
added value of DTs of EV assets especially for delivering
intelligent charging management. Using complex simulations,
the grid can understand different types of negative scenarios
such as congestion management and can avoid them by better
administrating the charging stations [24]. In other approaches,
DTs are used for modeling fleets of EVs and simulating their
behavior to better manage their charging schedules [23].
Parameters such as energy consumption, charging capacity,
and charging frequency are used to create virtual models.
Time-series simulations are used for evaluating the
performance of a network of EV charging stations and to make
decisions for design and architecture optimization in terms of
density and location.
Finally, a rather common approach for DT modeling found
in the state of the art is to use semantic web and ontologies.
They allow building multilayer architectures for the specific
configurations of the smart grids and for various energy assets
[18]. Consumers' load profiles, generation infrastructures,
power supply systems can be all modeled using hierarchical
structures provided by ontologies and fed as input to reasoning
processes to assess their performance. Moreover, the usage of
semantic web technologies for modeling energy assets allows
for achieving a certain level of genericity for the modeling and
simulation solutions being able to be easily adapted to
different energy assets or types of smart grid architectures
B. Fault and security diagnosis
As the smart grid, operational complexity is increasing
faults may occur in various components of the electrical
infrastructure [38]. The main challenges are to timely
diagnose and eventually avoid them. Otherwise, they can
cause instabilities, energy distribution problems, losses that
may culminate with power outrage [37]. At the same time,
cyber-attacks or natural disasters can also lead to failures,
privacy breaches, and even blackouts [39]. For example, a
coordinated attack on the energy grid of Ukraine has caused a
power blackout that affected 225,000 households [40].
Even though DTs have promising features for addressing
all these aspects for energy grids few relevant approaches can
be found in the literature. In the case of dynamic systems like
the smart grid, a balance should be kept between fault
estimation and state estimation [6]. DTs can be used to predict
potential failures, detect security issues that may affect the
safe operation of energy assets, and implement mitigation
actions or predictive maintenance processes [36], [37], [38].
Machine learning models and discrete-time models are
proposed to detect potential warnings at the edge of the grid
while transient state estimators may predict future operational
faults [34]. Digital twins of grid batteries are proposed by
fusing the degradation modes with machine learning
techniques. This approach enables the identification and
diagnosis of battery faults as well as the more advanced
control of battery usage prolonging their lifetime [35]. A DT
model for fault detection of drivetrains in offshore wind
turbines is defined in [39]. Besides fault detection, the DTs are
used to diagnose the faults and for implementing a predictive
maintenance process. Mathematical models and analyses are
used for fault diagnosis in the case of different energy assets.
In [14] a digital twin of a Photovoltaic System energy
conversion unit is described, while the fault is detected using
error residual vectors, enabling real-time detection.
Even though DTs have great potential for managing more
efficiently the energy assets security challenges need to be
addressed [42]. Digital twins are proposed and used as a
solution for analyzing potential threats to the smart grid and to
assess its resilience. The DT model the grid operation to detect
and avoid energy services disruptions [41]. The smart grid
cybersecurity standards and potential threats are reviewed,
and the authors propose the use of DTs to mitigate the lack of
standardization. In [5] DT is used as a solution to mitigate
coordinated attacks onto a network of interconnected power
grids. Concepts like the IoT shadow and cyber-physical DT of
the micro-grids network are mathematically formalized while
an agent-based resilient control algorithm is implemented to
detect attacks. Software-defined networks and digital twins
are successfully used in [41] to improve the resilience of the
smart grid in case of attacks such as distributed denial-of-
service or packet delay.
C. Grid operation and control
For optimizing energy consumption, reducing costs, and
improving efficiency, DTs can act as an intermediary layer
above the energy grid resources through which control
decisions can be validated and enforced in real-time.
Combined with trending technologies such as blockchain,
they can be used to run predictions or simulations to estimate
how a physical system acts in a specific situation and to
improve its operational control [33]. In the case of large-scale
distributed energy grids with lots of interconnected energy
assets joining DTs with the decentralized nature of blockchain
technology and smart contracts can further improve the smart
grid operation and control starting from twinning the
resources and adding them to blockchain networks [51].
DTs can pave the way for building platforms that involve
complex event processing, ML, and that are mainly used to
digitize the grid dispatching rules and optimize the energy
distribution. DTs for Net Zero Energy Buildings are
considered a solution to optimize the renewable usage and
efficiency in energy consumption [27]. They deal with
thermal models, energy usage, and cost analysis as DT
components to optimize the energy efficiency of apartments.
Simulation can be carried out also for the thermal design of a
heating and cooling system of buildings using DTs [28]. For
optimal energy, distribution approaches propose modeling
distribution transformers as DT and computing medium
voltage (MV) waveforms for identifying problems that may
arise and congestion points [29]. In [30] the authors combine
distributed network concepts with DT to evaluate the
performance of smart grid distribution network transformers
operation to defects and fix them before real-world
deployment. Similarly, in [31] DTs are used to optimize
energy resource usage and energy distribution paths. The
model is combined with particle swarm optimization (PSO)
algorithms optimization and times-series based simulations
are carried out. To analyze the behavior under critical events
in the power grid DT can be combined with decision-making
techniques based on neural networks [32]. Such rule-events
approaches can be assessed on DTs of the real-world
distribution system assets to detect the critical events and to
enforce intelligent control.
The complexity of the energy system is addressed using a
system of system engineering as a methodology in which each
sub-system integration can be optimized using DTs [52]. In
[53] the authors propose an integrated energy system for
testing and evaluating the impact of demand-response
equipment on energy distribution. The simulation
environment is modeled using a system of systems approach
while hardware in the loop is used to run and control the
simulation. Finally, in [54] an optimization process is
implemented on top of a thermal and electrical system of
system model of a DC. System-level flexibility and nonlinear
optimization processes are used to grid the DC energy demand
towards a target goal.
D. Business models
Traditionally a business model should define how an
enterprise will deliver value to customers, how customers will
pay for the value, and how the enterprise will convert those
payments to profit [43]. In this context, DTs have been
proposed for managing customer profiles, expectations and
raise their awareness about the new energy services that are
made available [3]. Data regarding customer preferences,
satisfaction, and behavior are used to construct DTs and fed to
train and run big data analytics to generate reports for
customer awareness. Indeed, DTs are currently seen as a key
asset for customer engagement. Using historical customer
data, a DT profile can be constructed and used as a baseline
for creating energy customer-targeted service offers. This kind
of digital twins enabled process is applied in various domains
such as transportation, online shopping, and energy grid [44],
[24], [25].
In the energy domain the flexibility profiles of non-grid-
owned energy assets, devices such as heat pumps, EVs, hot
water boilers, are not coupled with information relating to the
user’s wishes in terms of comfort, convenience, and wellbeing
[40]. A user’s DT profile seamlessly incorporates their
flexibility profiles representing the selected flexibility assets
can increase the “smartness level” of buildings which will lead
to increased participation in energy flexibility services [45],
[17]. In this way, the small-scale energy prosumer is enabled
to trade their energy flexibility and participate in flexibility-
driven demand response (DR) programs. Moreover, the DTs
may drive the design and implementation of cross-value chain
DR business models, which enable increased consumers’ DR
participation and hence enable better grid management, while
contributing to electricity grid reinforcement and obtaining
lower energy prices for consumers [47].
In this context, the business models are based on open
horizontal integration across the entire socio-economic
ecosystem of the smart grid. All interested energy
stakeholders will be enabled to cooperate at the digital level
taking advantage of prosumers-owned energy flexibility
assets. The business models will exploit digital global
awareness of both energy demand and supply at the edge of
the grid level facilitated by the DTs integration to define and
deliver new customized energy services leveraging on
increased levels of energy flexibility.
DTs facilitate the design and the development of
customized energy services to appropriate clusters of
prosumers organized in communities and the investigation of
novel virtual aggregation business models [48], [49]. New
sharing economy models can be validated in smart grid
scenarios by leveraging on DTs of flexibility assets [46]. In
this case, the consumers, the owners of flexible assets, and
energy stakeholders will cooperate to achieve community-
level welfare or energy grid sustainability objectives [47]. In
that respect monetary and non-monetary incentives are split
among the different local energy stakeholders, consumers and
grid operators will be motivating energy consumers'
participation in such energy communities. Also, the incentives
can be used for additional investments in community levels
energy assets for unlocking even more flexibility or for
increasing the levels of renewable energy locally used [16]. In
that respect, the DTs of energy assets will allow the
investigation of business innovation using various
configurations of shared asset ownership at the local
community level.
DTs can be used for accurately estimating community-
level flexibility and to find local synergies using simulations
with other energy carriers such as gas, water, and heat for
increasing the levels of flexibility committed [3]. Shared
assets like energy storage systems lack solutions for digital
integration which prevents fine-grained asset monitoring and
cost-effective and efficient and reliable operation [4]. These
can be facilitated by the usage of DTs and data-driven
analytics to analyze the performance of the individual assets
concerning performance degradation and utilization rate [22].
On top value stacking energy services, offers can be defined
at the interplay among energy, mobility, health, or ambient
assistive living.
Finally, the DTs can facilitate the implementation of
virtual energy aggregation business models. In this case,
prosumers may join in virtual coalitions such as Virtual Power
Plants to deliver energy services to grid operators or to trade
energy on different markets [47], [48]. The main drivers, in
this case, are the increasing need to optimize the output from
multiple local generation assets (i.e. wind turbines, small
hydro, photovoltaic, back-up generators, etc.) and the increase
of constituents’ prosumers profit. The DTs can be used for
analyzing and predicting the energy profiles [10] of such
coalitions that mix different energy generation assets which
have different energy generation models and scale with a view
of reducing the associated uncertainty and optimally deliver
energy services.
In this paper, we presented an overview of the most
relevant application domains of DTs in the energy field by
conducting literature analysis, describing classifying the
approaches found. After searching several databases, 52
papers on DT and energy have been selected and classified
according to four application domains: energy asset modeling,
fault and security diagnosis, grid operation optimization, and
control and business models.
Even though DTs offer promising features in managing
several aspects of the smart energy grid a relatively low
number of papers can be found in the literature addressing the
DTs applications in the energy domain. There is no consensus
or unified modeling and implementation process for DTs
integration with smart grid management processes. Each
reviewed paper is focused on the development of specific
aspects of features of DTs tailored for different energy assets,
smart grid configurations, or energy services.
Anyway, since most of the literature approaches identified
are from the last two or three years (over 81%) we can
conclude that DTs applications in the energy grid are
gradually developing the researchers starting to explore in-
depth critical aspects of their implementation. But the ultimate
goal of understanding and replicating completely the behavior
of the physical energy asset and automating smart grid
management is still far away.
This work has been conducted within the BRIGHT project
grant number 957816 funded by the European Commission as
part of the H2020 Framework Programme and it was partially
supported by a grant of the Romanian Ministry of Education
and Research, CNCS/CCCDIUEFISCDI, project number
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