Digital twins and their use in future power systems[version 1

REVIEW

Digital twins and their use in future power systems [version 1

peer review 1 approved 1 approved with reservations]

Peter Palensky Milos Cvetkovic Digvijay Gusain Arun JosephElectric Sustainable Energy TU Delft Delft 2628CD The Netherlands

Corresponding author Peter Palensky (palenskyieeeorg)Author roles Palensky P Supervision Writing ndash Original Draft Preparation Cvetkovic M Writing ndash Original Draft Preparation Gusain D Writing ndash Original Draft Preparation Joseph A Writing ndash Original Draft PreparationCompeting interests No competing interests were disclosedGrant information The author(s) declared that no grants were involved in supporting this workCopyright copy 2021 Palensky P et al This is an open access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly citedHow to cite this article Palensky P Cvetkovic M Gusain D and Joseph A Digital twins and their use in future power systems [version 1 peer review 1 approved 1 approved with reservations] Digital Twin 2021 14 httpsdoiorg1012688digitaltwin174351First published 22 Sep 2021 14 httpsdoiorg1012688digitaltwin174351

First published 22 Sep 2021 14 httpsdoiorg1012688digitaltwin174351Latest published 22 Sep 2021 14 httpsdoiorg1012688digitaltwin174351

v1

Abstract The electric power sector is one of the later sectors in adopting digital twins and models in the loop for its operations This article firstly reviews the history the fundamental properties and the variants of such digital twins and how they relate to the power system Secondly first applications of the digital twin concept in the power and energy business are explained It is shown that the trans-disciplinarity the different time scales and the heterogeneity of the required models are the main challenges in this process and that co-simulation and co-modeling can help This article will help power system professionals to enter the field of digital twins and to learn how they can be used in their business

Keywords power systems digital twin co-simulation

Open Peer Review

Approval Status

1 2

version 122 Sep 2021 view view

Trevor Hardy Pacific Northwest National

Laboratory Richland USA

1

Gert Mehlmann Friedrich-Alexander

Universitaet Erlangen-Nuernberg Erlangen

Germany

Timo Wagner Friedrich-Alexander

Universitaet Erlangen-Nuernberg Erlangen

Germany

2

Any reports and responses or comments on the

article can be found at the end of the article

Digital Twin

of 14

Digital Twin 2021 14 Last updated 22 MAR 2022

Origin and history of digital twinsDigital transformation features a number of powerful tech-nologies such as machine learning and the Internet of Things1 One such technology that recently gained visibility is the concept of the digital twin This article will introduce you to this concept and review the benefits of applying this concept in future power systems

Travelling through the brief history of the digital twin concept its theoretical foundations emerged over the past few years from various diverse disciplines such as production engineer-ing aerospace engineering automotive engineering electrical engineering computer technology and information science2 The concept of twins dates back to NASArsquos Apollo program As a part of this program identical space vehicles were built3 During the mission the earth-based one acted as twin and was used to mirror the flight conditions using the available flight data thus assisting the astronauts and engineers in critical situations The Iron Bird is another famous example of a rdquohardwarerdquo twin which was a ground-based engineering tool used by the aircraft industries to incorporate optimize and validate vital aircraft systems4 With the advances in the simulation technology the physical components in the Iron Bird were replaced by its virtual counterparts and this allowed the system designers to test various product life cycle scenarios even when the actual physical components were not available Extending the idea further to all the phases in the product life cycle management leads to the development of a complete digital model of the physical system the digital twin (Figure 1)

The term digital twin was first introduced by Grieves in 20025 as a new concept in product life cycle management Even though it was initially termed as Mirrored Space Models in 20036 the concept later evolved into information mirroring models in 20057 and eventually into digital twins in 20118 In

2012 the concept of digital twin was revisited by the National Aeronautics and Space Administration (NASA) They defined the digital twin as a multiphysics multiscale probabilis-tic ultra-fidelity simulation that reflects in a timely manner the state of a corresponding twin based on the historical data real-time sensor data and physical model9 In 2016 Grieves10 defined the digital twin as a set of virtual information constructs that fully describes a potential or actual physi-cal manufactured product from the micro atomic level to the macro geometrical level and at its optimum any information that could be obtained from inspecting a physical manufactured product can be obtained from its digital twin

In the preliminary stage of the concept development the dig-ital twin was defined to include only three parts a) the physical product in real space b) the virtual product in virtual space and c) their connections in the form of data and information11 The most recent concept of the digital twin in 201912 proposes that digital twin should comprise a five-dimensional model with a physical part virtual part connection data and services The digital twin concept evolved leading to special notions such as airframe digital twin (ADT)13 and experimental digital twin (EDT)14 The ADT consolidates different models such as structural models flight dynamics models materials state evolu-tion models etc required for the design of an aircraft and uses high-performance computational simulations as virtual health sensors and forecasts maintenance needed for individual aircraft An EDT proposed in 14 combines the approaches of the digital twin and virtual testbeds With the new advancement of technology the digital twin concept has undergone expo-nential growth in various multidisciplinary applications such as Industry 40 or power systems13ndash19

Architecture and variants of digital twinsThe term digital twin gained popularity in the recent months also fueled by tech trend reports such as 20 The Internet of Things and the digital transformation in general make this

Figure 1 In contrast to classical maintenance the digital twin provides insight into the behavior of the entire system

Page 2 of 14


concept interesting and accessible for a whole new range of sectors and applications19

In this respect it is important to define what a twin is and which variants are currently in use A digital twin is usually a description of a process or a system enhanced with (live) data2 The description itself can range from a plain schematic of the system up to a dynamic numerical simulation model but the moment that we link it to real-world data it becomes a digital twin (Figure 2)

Both the operators and the twin receive environmental data (temperature market prices etc) and process data (measure-ment values states) The application (eg operators users controllers) uses this information to act upon the system (eg closed loop controls changing the system in some way) The application also operates the twin via the manage-ment (MGMT) part of the twin in order to start stop initialize fast-forward or rollback the twin By taking these actions the twin can be used for decision support21

The data handling part of the twin uses measurement data to synchronize22 its state periodically (this is needed because an imperfect system description will deviate from reality after a while) to update the model (parameter identification) or to generate and update the entire model in a data driven way Data might come from geographically distant places so each measurement sample requires a rich set of metadata such as synchronized time stamps and semantic tags that describe its meaning and origin

Starting from the historical domain-specific digital twins in the previous section we can define the following usage of digital twin variants (see also Figure 3)

bull Dashboards a mere display or mirror of the process as it is currently perceived (measured) sometimes also referred to as rdquodigital shadowsrdquo23

bull Static (sizing) twins used to describe and optimize the design and dimensions of a system24 This can range from a data sheet to a (static) formula

bull Twins for dynamic design used to describe and optimize a process and its dynamic behavior Com-plex interactions can be observed (eg two control-lers interacting) and the models can be physics-based or data driven25

bull Anomaly checks A twin capable of executing its model in real-time ie on the fly can be compared to the real process or system and thus expose abnormal behavior such as faults It needs to be synchronized with reality at all times otherwise the model states would diverge26

bull Forecast and scheduling twins These twins can pre-view or forecast future scenarios using predicted envi-ronmental variables (eg weather or usage patterns A multiverse of potential futures can be launched on a fleet of such twins in order to derive a robust deci-sion that performs well in a number of different scenarios27

bull Twins for control These twins show future dynamic behavior which in turn can be used in a model- predictive type of control If multiple scenarios are needed in short time parallel twins can evaluate them28

If twins provide (ie calculate or just display) their out-put within a guaranteed time that is equal or smaller than the

Figure 2 A digital twin system description enhanced with data (flow) The digital twin gets updates from its environment and is used by an application MGMT represents the system management

Page 3 of 14


Figure 3 Usage of the different digital twin variants depending on timing and model depth

intended operational time step it is said that the digital twin can perform in rdquoreal timerdquo29 If this time is substantially smaller than the intended time step then the twin can provide answers so much ahead of time that they can be used to derive control or scheduling decisions If the calculation of the out-put takes much more time than operations expect it can only be used for planning and design purposes30

The level of model fidelity in Figure 3 can be

bull Topology the system is just described in structure and how components are connected and related to each other but no mathematical description of its physical or functional mechanisms is embedded An example would be a map of a factory on a wall displaying all assets and devices in it maybe with live sensor values embedded (lamps dials)

bull Static a static model (based on algebraic equations or simple if-then code) sets inputs states and out-puts into a deterministic perspective This model can be used in a step-by-step way resulting in a quasi-static model that shows some behavior over time An example would be an economic model of a certain market

bull Dynamic dynamic equations or code describe the system It contains all internal modes and states can be triggered by external and internal events and exposes potentially complex behavior An example would be the electromechanical model of an electric engine

Digital twins are a classical example of cyber-physical systems31 since they are the information and communication

technology (ICT) constructs interacting with the real world somehow Sometimes they not only are but even represent a cyber-physical system3233 If the real-world system for instance contains a physical process and a digital distrib-uted control system the twin probably also has to model the communication links of the network based controls34

Depending on the computational magnitude of the twin it can be implemented rdquoin the fieldrdquo on an embedded system or hosted rdquoin the cloudrdquo running off powerful data centers35 see Figure 4 According to 36 the model uses can be

bull White box The topology components and parameters are known Its structure is identical to reality

bull Grey box As white box but parts are unknown and need to be learned derived or measured Its structure resembles reality to some extent

bull Black box The entire model needs to be learned While the twin behaves as its real counterpart its internal structure is of entirely different nature (eg a statistical model or neural network that represent a waste water plant)

Since white box models are an idealistic construct most of the time grey or black box models are used and some parts of the model require supporting methods such as machine learning37

If a system contains a certain level of complexity or is even a system of systems it is hard to create a twin that correctly imitates all its behavior Since complex systems can show substantially different behavior if a parameter is just slightly

Page 4 of 14


changed the only way to keep the twin matched with reality is periodic synchronization via measurement data streams

Power system digital twinsThe power system is one of the most complex cyber-physi-cal systems that humans have ever made and digital twin serves as a most promising technology15 that enables its transition towards a new generation of industry 40 and internet of things But the challenges to be faced en route this transition is manifold

To effectively utilize the full potential of the digital twin technology in the power system a holistic approach is required to address various challenges such as modeling data manage-ment storage computational requirements and scalability Even though high-performance computing facilities and emerg-ing technologies such as cloud computing38 could serve as a stepping stone to deal with most of these challenges the challenges related to modeling and data management require more than engineering skills to solve Furthermore efficiently balancing the trade-off between the accuracy of predictions by digital twins and optimizing computational complexity required for various types of modelsdata will be challeng-ing Extensive research has been carried out very recently in this regard15

The digital twin concept is envisioned as the next evolution-ary step in the control center technology of the power system18 The implementation of the digital twin concept in the power system control room will enhance the traditional systems with additional functionalities such as dynamic observability assessment and advanced decision support Thus various processes such as system stability analysis system planning model validation and disturbance analysis which were a part of the offline regime of the power system operation can now be incorporated as a part of online power system operation39 as described in Table I Moreover the situational aware-ness systems in traditional control rooms that were using low dimensionality models40 will be replaced by more complex frameworks The digital twin frameworks proposed for power systems in the recent literature can be classified based on the approach they support as a model-based approach1617 or a data-driven approach41 or a combination of both39

The power system digital twin framework with a model-based approach is based on the white box modeling Functions Traditional power system Twin-enabled power system method wherein automated simulation of an application-specific model along with a decision support system serves as a core engine of the digital twin The computational complexity added by the simulation execution time of such a digital twin system

Figure 4 The digital twin could expand beyond the local facilities integrating and providing the cloud-based services

Page 5 of 14


is determined by the dimensionality of the model and this in turn is dependent on the type of application for which it is used For example from a power system stability perspec-tive the load model and quasi-steady-state model for fault anal-ysis fall under the low dimensionality category whereas the dynamic RMS and EMT models fall under the high dimension-ality category42 In 16 the authors describe the use of dynamic models as faster than real-time digital twins of power sys-tems for predictive mitigation of short term voltage instability problems The faster than real-time digital twin is realized by ultra-fast simulations using the Python Application Pro-gramming Interface (API) for Powerfactory software43 and the high levels of details achieved by models developed in Powerfactory accurately describe the Fault Induced Dynamic Voltage Recovery (FIDVR) event propagation and mitigation strategy16 A model-based digital twin for fault diagnosis in distributed photovoltaic systems is proposed in 17 where the digital twin is implemented in the field using low cost Field Programmable Gate Array (FPGA) units

The power system digital twin framework with the data-driven approach is based on the black box modeling meth-ods which heavily rely on statisticalmachine learning-based algorithms as the core engine of the digital twin44 The grow-ing popularity of machine learning techniques such as deep learning adds more fuel to the process of development of many applications based on this framework In 41 the authors propose a digital twin for real-time power flow moni-toring taking the advantage of random matrix theory and deep learning The situational awareness is obtained by employing data extraction and information mining of the temporal-spatial dataset A combination of both frameworks can be seen in Zhou et al 201939 utilizing the grey box modeling approach The digital twin framework consists of a constantly updated grid topological model that is implemented using the interPSS software45 and a machine learning-based fast dynamic security assessment as the decision support system

Integrated energy systems digital twinsIncreasing adoption of power-to-x (P2X) energy converters in residential4647 commercial4849 and industrial50ndash52 spaces is enabling a more interlinked and integrated energy system A digital twin can play a transformational role in enhancing the

operation and reliability of such an integrated system Consider a local energy system operator with electricity distribu-tion network and district heating network The energy system operator is noticing changes in its network topology such as an increasing presence of rooftop solar adoption of smart appliances (such as refrigerators washing machines etc) the transition to electric heating (using boilers and heat pumps) etc making its control area a much more complex entity To test the reliability of the network the energy system operator can use the digital twin along with sophisticated physical models of its network to monitor and predict sys-tem health Since the digital twin is the virtual replica of the exact system it enables testing and experimentation such as analyzing the impact of adoption of newer technology capacity expansions needed etc

Another use of the digital twin for the energy system opera-tor is planned or predictive maintenance53 Using the most up-to-date representation of the network enabled by the con-stant stream of data coupled with historical measurements the energy system operator can predict network health and take appropriate actions In case of events such as line faults in its electric network or water leaks in district heat net-work etc the physical models of the network can be used to evaluate immediate-future scenarios (by looking hours to days ahead in the future) to avoid any unwanted condi-tions A digital twin can also assist asset owners of P2X devices in such an integrated system to know more about how their devices are operating and monitor their health condition with real time data Not only does it make operation more efficient it can extend the operational lifetime by minimizing the down-time of network and devices therein In the above-mentioned use cases modeling and simulation plays a key role

In domain-specific applications of digital twins (such as in building energy management)5455 the models to support the dig-ital twin can be created with state-of-art modeling languages and tools (eg EnergyPlus) In an integrated energy system this is harder to accomplish since complex interactions within and between energy carriers cannot be adequately cap-tured by any single modeling environment56 Despite the availability of general purpose modeling languages such as Modelica developing models for integrated energy systems

Table I Digital twins transform the way we operate power systems

Functions Traditional power system Twin-enabled power system

Model validation Offline with system models updated in span of few years

Online models updated on the fly

Dispatch optimization Offline Online

Outage planning Offline Online

Phasor Measurement Unit (PMU) based wide area system

Monitoring islanding Predicitive control

Page 6 of 14


faces several challenges such as 1) the presence of mature and advanced domain-specific model libraries 2) expert domain knowledge to model the individual subsystem 3) the abil-ity to fulfill domain-specific simulation characteristics among others Without appropriate models the full power of digital twin cannot be harnessed

An interesting option to overcome limitations of modeling the entire integrated energy system in a single modeling envi-ronment is to use domain-specific tools to model individual subsystems and use co-simulation as a model coupling method to combine these subsystems56 Co-simulation or coupled simulation is a simulation technique that allows different types of models (physical equation-based data-driven etc) to be combined and simulated in a unified fashion57 For an energy system operator this offers a significant benefit The models developed in different divisions (eg district heat net-work model developed by the heat division electricity net-work model developed by electricity division) can be effectively combined without having to convert one or more subsystem models into a common modeling language These models

can be placed either in the same machine58 or distributed on various machines59 to power the digital twin

The co-simulation approach employs a master algorithm which controls the time progression in the simulation and manages the exchange of information between the individual model subsystems60 In case of distributed subsystem models the time progression can be coordinated through standardized pro-tocols such as Distributed Co-simulation Protocol (DCP)61 Already a variety of co-simulation masters are available under open-source licenses such as energysim58 MESCOS62 Mosaik63 etc which can be used to execute an integrated energy system co-simulation By bringing together multi-domain models (physical or data-driven) using the described co-simulation approach and utilizing faster-than-real-time simulation capabilities of modern computing infrastructure digital twins can enable the integrated system operators a new way to design operate and maintain highly-interconnected energy systems An illustration of the digital twin of an inte-grated energy system coupled with the physical world is shown in Figure 5

Figure 5 Digital twin in an integrated energy system CHP combined heat and power EV electric vehicle P2H power to heat P2G power to gas

Page 7 of 14


Although there are challenges in adopting a model coupling approach via co-simulation such as algebraic loops conver-gence issues etc59 it offers three important advantages over traditional simulation techniques Firstly a larger system can be broken down into smaller subsystems and efficiently be processed in parallel This decreases the computational effort to simulate the system62 Secondly the individual domain models can be developed in state-of-art tools by expert modelers Using the advanced libraries and features provided by these tools modelers can create high-fidelity subsystem models Thirdly it allows individual subsystem models to be embedded as black-box models in the overall system setup This permits the subsystem models to retain sensitive information such as model definitions and model topology (thus protecting the model IP) while exposing only requested variables with other subsystems during the co-simulation64

Open standards such as the Functional Mock-up Interface (FMI)65 allow packaging physical models as encrypted functional mock-up units (FMUs) containing model descriptions model equations and optionally the solvers to simulate the model This allows energy system operators to bring together highly detailed and complex subsystem models which enables a model-enhanced digital twin The backing of its digital twin with high-fidelity subsystem models allows it to accurately analyze the impact of its operational strategies network upgrades etc

Digital twin application in future power systemsDigital twin plays a significant role in various applications related to future power systems It carries the promise of an

accurate reliable and timely support tool for decision-making of stakeholders on all levels (from maintenance crews via plan-ning engineers down to controllers in the substation bay) see Figure 6 In the following we list some high-impact applications of digital twin

bull Control room advisory - Ensuring reliability of the electricity supply requires making quick decisions relying on expert judgement of grid operators In the past an experienced operator has been able to make high quality decisions in a timely manner However as we increase sector coupling penetration of distrib-uted energy resources and demand response the grid operators find themselves in many new challenging situations66 To overcome these a timely digital twin advice could be crucial One such example is real-time voltage stability control reported in 67

bull Education and training - Another essential usage of the digital twin is in education and training of infra-structure operators The digital twin systems can be used to set up red team - blue team exercises emulat-ing cyber-attacks on power systems natural catas-trophes cascading failures etc If coached with the aid of digital technologies the grid operators are quicker on their feet in the case of a real event68

bull Post-mortem analysis - In the post-mortem analy-sis power engineers seek to establish a sequence of events that led to a grid (or component) breakdown in order to increase the resilience of the system to future events69 Since it simulates forking of different

Figure 6 Many uses of digital twin including analytics maintenance real-time operations and planning

Page 8 of 14


pathways digital twin eases the tractability of critical events and helps establish causality70

bull Long-term decision support - To make long-term deci-sions stakeholders require accurate and compre-hensive models The digital twin allows to evaluate feasibility of alternatives and investigate adoption pathways71 The stage-based and comparative analy-sis of grid reinforcements and expansion projects can be supported

bull Asset management - The high-fidelity models can successfully be used for predictive maintenance of power carriers circuit breakers and other substation equipment The digital twin already encompasses such models72

bull Field operation support using augmented reality - The maintenance crews can be supported by digital twins enhanced with augmented reality access73 Evaluating decisions in real-time before they are implemented on the electricity grid increases safety and security of technicians and the infrastructure

bull Collaborative decision-making among stakeholders - The infrastructure expansion decisions often require alignment and support of all involved stakeholders74 The digital twin provides a back-end andor front-end platform for a collaboration tool

bull Model-predictive operations - Controllers operat-ing on-load tap changing transformers and intelligent rooftop photovoltaic inverters can use a digital twin in order to optimally regulate distribution grid voltages and congestion levels75 Since relatively complex

challenges of control and coordination in the distribu-tion grid do not fit into an embedded conventional linear controller anymore a digital twin hosted in a data center could provide an adequate solution In this case the rdquoedgerdquo (the controller in the field) consults the rdquocloudrdquo (the twin in a data center)

OutlookDigital twins are now where computers were in the 1950s big bulky things that need lots of attention and manual expert support In the future we expect them to be an established principle accepted in all sectors The expert input such as model generation or validation will be replaced by data-driven processes where parameters are learned and changes are automatically identified and endorsed

Twins might be everywhere For the power system this means that not only all controllers in a substation might have their own private twin to help them with their daily tasks but every participant in the grid electric vehicles photo-voltaic invert-ers smart heat-pumps and wind power stations will use the digital twin principle in order to get their environment and peers digitally represented Having that they can optimize their operations since environmental situations and even peer behavior can be anticipated

A self-organizing resilient flat and peer-to-peer power system will not work unless intelligence is embedded in the components of such system It requires individuals that are aware of themselves and others

Data availabilityNo data are associated with this article

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60 Steinbrink C van der Meer AA Cvetkovic M et al Smart grid co-simulation with mosaik and hla a comparison study Computer Science - Research and Development 2018 33(1) 135ndash143 Publisher Full Text

61 Krammer M Ferner P Watzenig D Clock Synchronization in Context of the

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Distributed Co-Simulation Protocol 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) 2019 1ndash6 Publisher Full Text

62 Molitor C Groszlig S Zeitz J et al MESCOSmdashA Multienergy System Cosimulator for City District Energy Systems IEEE Transactions on Industrial Informatics conference Name IEEE Transactions on Industrial Informatics 2014 10(4) 2247ndash2256 Publisher Full Text

63 Steinbrink C Blank-Babazadeh M El-Ama A et al CPES Testing with mosaik Co-Simulation Planning Execution and Analysis Appl Sci number 5 Publisher Multidisciplinary Digital Publishing Institute 2019 9(5) 923 Publisher Full Text

64 Durling E Palmkvist E Henningsson M Fmi and ip protection of models A survey of use cases and support in the standard Proceedings of the 12th International Modelica Conference Prague Czech Republic May 15-17 2017 Linkoumlping University Electronic Press 2017 (132) 329ndash335 Publisher Full Text

65 Blochwitz T Otter M Akesson J et al Functional mockup interface 20 The standard for tool independent exchange of simulation models Proceedings of the 9th International MODELICA Conference September 3-5 2012 Munich Germany Linkoumlping University Electronic Press 2012 (076) 173ndash184 Publisher Full Text

66 Wu FF Moslehi K Bose A Power system control centers Past present and future Proceedings of the IEEE 2005 93(11) 1890ndash1908 Publisher Full Text

67 Joseph A Cvetković M Palensky P Prediction of short-term voltage instability using a digital faster than real-time replica Proceedings of IEEE IECON 2018 2018 Publisher Full Text

68 Assante D Caforio A Flamini M et al Smart Education in the context of Industry 40 2019 IEEE Global Engineering Education Conference (EDUCON) 2019 1140ndash1145 Publisher Full Text

69 Dagle JE Postmortem analysis of power grid blackouts - the role of measurement systems IEEE Power and Energy Magazine 2006 4(5) 30ndash35 Publisher Full Text

70 Liu Z Meyendorf N Mrad N The role of data fusion in predictive maintenance using digital twin AIP Conference Proceedings 2018 1949(1) 020023 Publisher Full Text

71 Fathy Y Jaber M Nadeem Z Digital twin-driven decision making and planning for energy consumption J Sens Actuator Netw 2021 10(2) 37 Publisher Full Text

72 Jiang Z Lv H Li Y et al A novel application architecture of digital twin in smart grid J Ambient Intell Human Comput 2021 Publisher Full Text

73 Schroeder G Steinmetz C Pereira CE et al Visualising the digital twin using web services and augmented reality 2016 IEEE 14th International Conference on Industrial Informatics (INDIN) 2016 522ndash527 Publisher Full Text

74 Bishop I Stock C Using collaborative virtual environments to plan wind energy installations Renewable Energy 2010 35(10) 2348ndash2355 Publisher Full Text

75 Jain A Nong D Nghiem TX et al Digital twins for efficient modeling and control of buildings An integrated solution with scada systems 2018 Building Performance Analysis Conference and SimBuild 2018 Reference Source

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Open Peer ReviewCurrent Peer Review Status

Version 1

Reviewer Report 14 December 2021

httpsdoiorg1021956digitaltwin18710r26846

copy 2021 Mehlmann G et al This is an open access peer review report distributed under the terms of the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Gert Mehlmann Institute of Electrical Energy Systems Friedrich-Alexander Universitaet Erlangen-Nuernberg Erlangen Germany Timo Wagner nstitute of Electrical Energy Systems Friedrich-Alexander Universitaet Erlangen-Nuernberg Erlangen Germany

The article provides a good overview of the topic digital twins for power systems The structure of the paper makes sense and is roughly divided into history ndash technical forms of implementation ndash sectors for implementation ndash outlook The handling of literature is quite good and the history of technical developments up to the digital twin is described interesting Different technical variants of digital twins are described based on possible applications and differentiated from each other in a meaningful way Overall the associated chapter is very successful although no hard technical definition is given for a digital twin for power systems In my opinion this is because a clear definition does not yet exist in the field of power systems Therefore it is not to be regarded as task of the authors to give this definition On basis of the described technical variants of digital twins the authors describe two sectors of applications of digital twins in more detail Power system digital twins and integrated energy system digital twins According to the title and focus of the paper the power system sector should be weighted more heavily than the integrated energy sector which is not the case In the chapter integrated energy system digital twins there is a strong focus on co-simulation as tool for sector coupling Of course co-simulation can be seen as an important tool for digital twins but the weighting of the topic is quite high with respect to other tools In this context the chapter power system digital twins should have been focused more strongly on tools Examples are data communication protocols or a discussion about the possible role and suitability of commercial real-time simulators for digital twins (OPAL-RT RTDS) Despite this point of criticism of the weighting of the power system and integrated energy sector I think the content of the paper is well done Finally the paper gives a good outlook on the further development possibilities of

Digital Twin

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digital twins Linguistically the paper is written in a way that is easy to understand but at some points it is a bit colloquial Is the topic of the review discussed comprehensively in the context of the current literatureYes

Are all factual statements correct and adequately supported by citationsYes

Is the review written in accessible languageYes

Are the conclusions drawn appropriate in the context of the current research literatureYes

Competing Interests No competing interests were disclosed

Reviewer Expertise Power System Stability

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard

Reviewer Report 26 October 2021


copy 2021 Hardy T This is an open access peer review report distributed under the terms of the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Trevor Hardy Pacific Northwest National Laboratory Richland WA USA

This article provides a general overview of the digital twin concept its historical development and various forms It focusses its current application in the power system and discusses potential applications and implementations techniques (co-simulation specifically) to move the state-of-the-art forward I am only somewhat familiar with the digital twin concept and the explanation the authors provide is helpful for those new to the topic After reading this article several times I have concluded it is useful as a review article There are two key areas that I think should be addressed though Simulation is at the heart of digital twins but a digital twin is not well described as a simulation

Digital Twin

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For people like me who are new to the topic it would be helpful to elucidate the differences As I was reading this I kept asking myself Are they just talking about better-implemented simulations Though I havent checked I would be surprised if some of the cited references do not describe this well and maybe can be leveraged to provide the bulk of the explanation I think for a review article which is intended to have a more general audience spending more page-space on fundamental topics such as this will increase the comprehension of the article as a whole (You may disagree we can discuss) Suggested (not required) questions that could be helpful in creating this content

What are the essential components of a digital twin This is addressed in the last paragraph of Origins and history of digital twins but the reader is left to read another reference I think spelling it out in this document would be more helpful

In what ways is a digital more than just a simulation

Must a digital twin reflect a specific physical thing

Must a digital twin include not only a model for the object in question but also supporting models of the environment in which it is operating

Though digital twins are relatively new concept and are still going through an initial adoption phase by industry and researchers I suspect there are some particular challenges for adoption of digital twins in the power system and these deserve devoted page-space Similarly I also suspect there are nascent or incomplete versions of digital twins in operation today in power systems as part of describing the challenges these can be used as examples to show if particular capabilities or components could be added significant progress would be made in achieving a digital twin With a more well-articulated definition of a digital twin it would also be possible to point out in which ways the existing technologies do not meet the definition of a digital twin and what important or useful capabilities they lack as a consequence Is the topic of the review discussed comprehensively in the context of the current literaturePartly





Reviewer Expertise Power system analysis and co-simulation

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard however I have significant reservations as outlined above

Digital Twin

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Origin and history of digital twinsDigital transformation features a number of powerful tech-nologies such as machine learning and the Internet of Things1 One such technology that recently gained visibility is the concept of the digital twin This article will introduce you to this concept and review the benefits of applying this concept in future power systems

Travelling through the brief history of the digital twin concept its theoretical foundations emerged over the past few years from various diverse disciplines such as production engineer-ing aerospace engineering automotive engineering electrical engineering computer technology and information science2 The concept of twins dates back to NASArsquos Apollo program As a part of this program identical space vehicles were built3 During the mission the earth-based one acted as twin and was used to mirror the flight conditions using the available flight data thus assisting the astronauts and engineers in critical situations The Iron Bird is another famous example of a rdquohardwarerdquo twin which was a ground-based engineering tool used by the aircraft industries to incorporate optimize and validate vital aircraft systems4 With the advances in the simulation technology the physical components in the Iron Bird were replaced by its virtual counterparts and this allowed the system designers to test various product life cycle scenarios even when the actual physical components were not available Extending the idea further to all the phases in the product life cycle management leads to the development of a complete digital model of the physical system the digital twin (Figure 1)

The term digital twin was first introduced by Grieves in 20025 as a new concept in product life cycle management Even though it was initially termed as Mirrored Space Models in 20036 the concept later evolved into information mirroring models in 20057 and eventually into digital twins in 20118 In

2012 the concept of digital twin was revisited by the National Aeronautics and Space Administration (NASA) They defined the digital twin as a multiphysics multiscale probabilis-tic ultra-fidelity simulation that reflects in a timely manner the state of a corresponding twin based on the historical data real-time sensor data and physical model9 In 2016 Grieves10 defined the digital twin as a set of virtual information constructs that fully describes a potential or actual physi-cal manufactured product from the micro atomic level to the macro geometrical level and at its optimum any information that could be obtained from inspecting a physical manufactured product can be obtained from its digital twin

In the preliminary stage of the concept development the dig-ital twin was defined to include only three parts a) the physical product in real space b) the virtual product in virtual space and c) their connections in the form of data and information11 The most recent concept of the digital twin in 201912 proposes that digital twin should comprise a five-dimensional model with a physical part virtual part connection data and services The digital twin concept evolved leading to special notions such as airframe digital twin (ADT)13 and experimental digital twin (EDT)14 The ADT consolidates different models such as structural models flight dynamics models materials state evolu-tion models etc required for the design of an aircraft and uses high-performance computational simulations as virtual health sensors and forecasts maintenance needed for individual aircraft An EDT proposed in 14 combines the approaches of the digital twin and virtual testbeds With the new advancement of technology the digital twin concept has undergone expo-nential growth in various multidisciplinary applications such as Industry 40 or power systems13ndash19

Architecture and variants of digital twinsThe term digital twin gained popularity in the recent months also fueled by tech trend reports such as 20 The Internet of Things and the digital transformation in general make this

Figure 1 In contrast to classical maintenance the digital twin provides insight into the behavior of the entire system

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Digital twins and their use in future power systems[version 1

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