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A Digital Twin for Cyber-Physical Energy Systems
Abstract
The pervasiveness of digitalization brings new opportunities, especially for monitoring and control in the energy domain. Access to sensor and context data allows a modern energy system to manifest as a Cyber-Physical Energy System (CPES). Specifically, in this paper we demonstrate the applicability of a Digital Twin for a CPES. We apply our vision of the Digital Twin paradigm to detect and analyze anomalies in a flexible energy deployment. Our results show that, with enough expert knowledge and insight, the Digital Twin can explore system behavior and is a candidate for system calibration and control parameter estimation.
... In the energy domain, sensor and context data from a Cyber-Physical Energy System (CPES) can be used to synchronize fit-for-purpose models with reasoning logic during the operational phase of the system, i.e., Digital Twins coordinated to support specific system goals. Previously, Pileggi et al. used an actual flexible energy system deployment to demonstrate the applicability of the Digital Twin [1]. ...
... These batteries provide flexibility to compensate for the energy imbalance in the ecosystem. They demonstrated an application of Digital Twin simulation to explore system battery behavior using an expert model [1]. The battery receives instructions from the controller and provides the controller with its flexibility information. ...
... Specifically, our method applies a Temporal Convolutional Neural Networks (TCN) [7], Fig. 1. Digital Twin using an expert model to investigate battery behavior as used in [1] extended with the Machine Learning model. Ideally, the battery provides current flexibility information, e.g., the amount of power it may consume or produce, which is used by the central energy controller to balance the energy ecosystem. ...
... A DT for a battery system is presented in [14]. A simulation of the battery system was created, and a rule-based system was used to detect anomalies in the battery state of charge (SoC). ...
... • Physics based • Real-time capable • Driven by physical twin sensor measurements The virtual twin's understanding of the physical model can be used to monitor and protect the physical twin. Using its understanding of the physical twin dynamics, a virtual twin can monitor the health of the physical twin [11], [12], [19], [20], [25], analyze its behavior and its interaction with the environment [14], [15], [19], [20], and prevent it from being placed in a dangerous state [18]. ...
... At present, the term "Digital Twin" means a representation of all components of an object over its life cycle relying on physical data, virtual data, and data on the interaction between them [4][5][6]. According to experts, the digital twin is one of the promising approaches to the implementation of the Fourth Industrial Revolution (Industry 4.0) [6][7][8][9][10]. Industry 4.0 paradigm suggests the creation of cyber-physical systems and employs the concept of a digital twin and the technology of the Internet of Things, which enhances the efficiency of the maintenance and operation of engineering systems, including energy systems. ...
... The study presented in Ref. [13] explores the potential of methods and approaches based on digital twins, which are intended to create an intelligent system for optimizing and automating the management of energy consumption in a residential area with the aid of a data model integrated with the Internet of Things, artificial intelligence, and machine learning. Pileggi et al. [8] use the concept of a "cyber-physical power system," in which the power system control relies on digital twins. The findings of the research indicate that the digital twin technology allows for optimizing the size of control actions on the power system and evaluating its operating parameters. ...
The design of integrated energy systems (IESs) is a challenging task by reason of the highly complex configurations of these systems, the wide range of equipment used, and a diverse set of mathematical models and dedicated software employed to model it. The use of digital twins allows modeling in virtual space for various IES configurations. As a result, an optimal option of IES is obtained, which is implemented in the construction or expansion of a real-world IES. The paper proposes the principles of building digital twins for solving the IES design problems. The paper presents a new methodological approach developed by the authors to design an IES with the help of its digital twin. This approach includes the following components: the architecture of the software platform to create digital twins, a set of technologies and tools to implement the platform, methods to automatically construct a digital twin based on the Model-Driven Engineering concept, an algorithm to design an IES based on its digital twin, and principles to organize a computational process using a multi-agent approach. The results of the computational experiment using the software implementation of the IES digital twin components are presented for a test energy supply scheme.
... Therefore, simulation models that are utilized for a virtual prototype might not be appropriate because the DT poses more challenges on a simulation model. Furthermore, interpretation and our understanding of system might not be complete and 100% reliable so and DT requires to contract with such reliabilities [21]. ...
... • Executing: is final step in which DT or its Physical counterpart can be modified through the actuation process. However, all these parts might not be completely digital processes and in some cases, human intervene will be necessary particularly in reasoning and executing parts [21]. ...
... The system can collect and transmit real-time performance data of electric vehicles to remote analysis servers and better decide for maintenance and operation personnel. 32 Pileggi et al 33 proposed a platform based on digital Twin the assess the state of charge (SOC) and state of health (SOH) of battery cells and the remain useful life (RUL) of the battery pack, and obtain a reliable measurement and prediction of battery system characteristics. ...
... For example, the charging post and maintenance service point is also the digital twin's application fields in the Battery. 33 Zhang et al have chosen digital Twin as the technical support to assist and evaluate the charging proposal design and the deployment plan of charging pile arrangement and distribution. 34 The following section consists of the challenges to implement the five-dimensional approach as a threedimensional approach not enough to create a sophisticated digital system of battery. ...
This paper proposes a Digital Twin (DT) framework for the whole life cycle of batteries. Specifically, in the stage of R&D, Digital twin can integrate the data of all technical fields into one model to optimize the battery's performance. During the manufacturing and production phase, DT can establish a digital production line and workshop to improve it. In the operation stage, aiming at various fault features in the battery operation process, with the assistance of large data samples, digital twin simulation is used to determine the fault status of the equipment accurately, to realize the self‐perception, judgment, error correction, early warning and other functions of the equipment. The paper also proposes a Post‐operation phase to recycle the battery to resolve the battery materials shortage problem. The critical research direction is a futuristic plan of battery communication with a charging Station, Battery Swapping System, Smart Grid, and Renewable resources. This sophisticated DT can comprehensively improve the energy system's entire life cycle management. Moreover, it can help perceive customer demand and offer an efficient operation mode to reduce running costs.
... The former can be at least used to create a large dataset containing data samples of normal operation conditions which can be utilized in machine learning approaches. The latter can play a key role in the anomaly detection problem [11]- [13], when it runs in parallel to the physical system with the same input values and environmental conditions. ...
... Several applications are presented in different areas of design, production, prognostic and health management. To the best of our knowledge, there are just few references in literature to the usage of Digital Twin in the context of anomaly detection [11]- [13] and all of those are more focused to present the Digital Twin system rather than a anomaly detection algorithm. ...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery, therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this paper, we present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called Cluster Centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly-supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyper-parameters related to feature extraction and network architecture is investigated. We find that the proposed SAE based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyper-parameter settings on all performance measures.
... The former can be at least used to create a large dataset containing data samples of normal operation conditions which can be utilized in machine learning approaches. The latter can play a key role in the anomaly detection problem [11]- [13], when it runs in parallel to the physical system with the same input values and environmental conditions. ...
... Several applications are presented in different areas of design, production, prognostic and health management. To the best of our knowledge, there are just few references in literature to the usage of Digital Twin in the context of anomaly detection [11]- [13] and all of those are more focused to present the Digital Twin system rather than a anomaly detection algorithm. ...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery, therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this paper, we present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called Cluster Centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly-supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyper-parameters related to feature extraction and network architecture is investigated. We find that the proposed SAE based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyper-parameter settings on all performance measures.
... In [15], a Matlab GUI toolbox is utilized to build a microgrid digital twin (MGDT) to address some specific customer needs using an energy management system algorithm. In [16], python is used to develop a DT model of a battery system to find and detect anomalies for CPS purposes. ...
Space missions would not be possible without an available, reliable, autonomous, and resilient power system. Space-based power systems differ from Earth’s grid in generation sources, needs, structure, and controllability. This research introduces a groundbreaking approach employing digital twin (DT) technology to emulate and enhance the performance of a physical system representing a space-based system. The system encompasses three DC converters, a DC source, and a modular battery storage unit feeding a variable load. Rigorous testing across diverse operating points establishes the real-time high-fidelity DT, with root mean square error (RMSE) values consistently below 5%. The principal innovation leverages this DT to fortify system resilience against unforeseen events, surpassing the capabilities of existing controllers and autonomy levels. The approach offers an invaluable tool for scenarios where the system may not be primed for or physical access to components is limited. This research introduces a modular battery storage solution that seamlessly compensates for power shortages due to dust effects on the Lunar surface or unexpected system faults. This holistic approach validates the DT’s fidelity and underscores its potential to revolutionize system operation, safeguard against uncertainties, and expedite response strategies during unexpected contingencies. The proposed approach also paves the way for future development.
... Many challenges in operating DTs, such as lack of interoperability and practical value, hinder their design and implementation [85]. Additional challenges of the existing DT for UMES are rooted in the complexity of transforming energy systems into cyber-physical systems [62,94]. As UMESs evolve to integrate more DERs and adopt advanced digital and automation technologies, there is a pressing need for robust data management and sophisticated tools to harness the potential synergies and manage the reliability and flexibility of these interconnected systems [62]. ...
Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems.
... A virtual reality substation is shown in [13]. Works [14][15] show the use of digital twins in the power system. A digital twin of the educational stand developed and implemented in the educational process is presented in [16]. ...
The development of active-adaptive electrical networks with an intelligent control system involves the creation of energy information complexes with the possibility of continuous monitoring and remote control of the operating modes of all its components in order to optimize network parameters. This necessitates the need to adapt educational programs and training technologies to train specialists with skills in related fields. The paper describes a developed research complex with elements of artificial intelligence for performing research and studying intelligent systems and means of controlling the modes of power supply systems based on a set of educational equipment using real and virtual objects of electric power systems with adjustable parameters. Pychram Community Edition was chosen as the integrated development environment. The software part of the complex has been developed, including a digital twin of the stand, an executive part and a neural network model. The neural network allows you to optimize the parameters of an active-adaptive electrical network losses during electricity transmission.
... The emergent cloud computing and the Internet of Things (IoT) are prerequisites for a cyber-physical energy system. Future energy systems will access various distributed sensors and contextual data of generation, conversion, and storage technologies as well as networks and appliances [33]. However, new requirements for the design of energy systems arise due to growing quantities of data and the need to extract actionable insights. ...
Defossilization of the current energy system is a major requirement to decelerate anthropogenic climate change. However, a defossilized energy system is vastly more complex than current fossil-based energy systems: The integration of distributed energy resources and sector-coupling increases connectivity, demands interdisciplinary workflows, and creates a need for more sophisticated design processes. Inspired by the semiconductor and automotive industries, digitalization of the design process using platform-based design (PBD), coupled with the energy hub concept, can improve cost-effective energy systems design and accelerate the industry’s contributions to achieving net-zero emissions. PBD is an efficient and effective methodology to manage and de-risk the complexity of integrated energy system design, leading to affordable and reliable solutions due to the inherent techno-economic analysis underlying the decision-making process. Combining the PBD framework with the energy hub concepts establishes a powerful design workflow for developing holistic energy systems from a single building up to the district and city scales. The fundamental tenets of this workflow, as discussed in this paper, are (1) the separation of functions from architectures, (2) the identification of abstraction levels at which systems can be analyzed and optimized, and (3) the ability to repurpose components at all levels of abstraction to aid design reuse and allow performance feedback at every stage of the process. We argue that PBD can become the next frontier in energy system design. PBD, as presented in this paper, is not limited to the energy sector, and it can also be a sub-process of an even more holistic infrastructure design. Spatial planning, architecture, and civil engineering can all be further integrated with the PBD concept, allowing societies to reach ambitious sustainability goals faster, at lower cost, and with greater resilience.
... A digital twin is a simulation process, that integrates multiple disciplines, physical quantities, scales, and probabilities [2] . In the field of the power system, Zhou et al. [3][4][5][6] designed the power system digital twin's framework, modules, communication architecture, and statutes. PALANGI et al. [7] built a digital twinbased operation model of the wind farm. ...
The methods for maintaining and monitoring the condition of the power system have changed significantly with the advancement of modern communication and information technology. A strong foundation for the use of digital twin technology and data science in the diagnosis of switchgear health status is provided by the efficient collection of enormous, high-dimensional equipment operation data. This research proposes a digital twin technology-based and random matrix model-based substation switchgear diagnosis approach. The health status of the switchgear can be estimated by mining the potential value of high-dimensional switchgear operation data and relying on data science techniques like high-dimensional statistical analysis and artificial intelligence. This will give maintenance personnel strong support as they develop response strategies.
... Most of the research in the field of digital twins in the power industry is focused on the following issues: assessment of the power equipment technical state [3][4][5][6][7][8], control of the power system operation modes [9,10], optimization of energy consumption [11] as well addressing the technical issues of renewable energy sources integration [12]. ...
Digital twin is one of the emerging technologies for the digital transformation of the power industry. Many existing studies claim that the widespread application of digital twins will shift the industry to a principally new level of development. This article provides an extensive overview of the industrial application experience of digital twin technologies for solving the problems of modern power systems with a particular focus on the task of high-voltage power equipment lifecycle management. The latter task contours one of the most promising areas for the application of the digital twins in the power industry since it requires deep analysis of the technological processes dynamics and the development of physical, mathematical and computer models that cover all the potential benefits of the digital twin technology. At the moment, there is a lack of reliable data on the problems of assessing and predicting the technical state of high-voltage power equipment. The use of digital twin technology in modern power systems will allow for aggregating data from a variety of real objects and will allow the automatization of collecting and processing of big data by implementing artificial intelligence methods, which will ultimately make it possible to manage the life cycle of the power equipment. The article puts to scrutiny the industrial experience of digital twins creation, considering the technical solutions suggested by the largest manufacturers of electrical equipment. A classification of digital twins, examples and main features of their application in the power industry, including the problem of managing the life cycle of high-voltage electrical equipment, are considered and discussed.
... For protection and maintenance (PM), Pileggi DT in the anomaly detection and analysis of energy deployment, viewing the DT paradigm as an application of cyber-physical energy systems [174] . Jain et al. designed a fault detection and diagnosis procedure under DT settings to construct distributed photovoltaic systems [175] . ...
Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems.
... DTs of transformers have been introduced to evaluate their state [9] and to monitor voltage waveforms of one side based on measurements form the other one [10]. A DT to detect potential anomalous behavior impacting the state of charge of a household battery system was proposed in [11]. In [12], a DT for fault diagnosis (detection and identification) in a distributed PV system was introduced and implemented on a Xilinx Artix-7 field programmable gate array (FPGA). ...
Power system digital twins are foreseen as an essential step towards future grids that will positively affect network monitoring, operation and planning across the whole power industry. Existing frameworks for developing a digital twin are bounded to particular power system components, applications and/or users. This article proposes a modular framework for the implementation of power system digital twins that is flexible, robust and cost-effective. The modularity in design enables the expansion of the twin beyond power system components and facilitates the integration of multiple services and users, without affecting the functionality of existing modules. Each module can be independently built, modified, replaced, or exchanged with other modules, unlocking multiple, more advanced, dedicated and multi-domain applications for the operation and planning of power systems. The first step towards a power system digital twin is illustrated based on a real-time compatible model of the Australian National Electricity Market The model can be seen as the core of one or more distinct modules in the digital twin, which can be adapted and scaled for particular applications. The EMT real-time compatible model is employed to demonstrate four potential services, including renewable energy integration and what-if scenario cases, which are expected applications in forward-looking power system digital twins.
... The key challenges facing the energy industry, the need for improving flexible power plant operation and implementing power plant DT were introduced by Fig. 1 of the introduction section. For rapid transformation of power systems and to reduce the impact of plant Sládek and Maryška (2018) Business potential of emerging technologies in decentralized energy industry Klein et al. (2020) Pressure-driven dynamic simulation to provide a detailed, transient simulation model, a digital twin, of an air separation unit Saad et al. (2020a) DT for energy cyber-physical systems based on IoT and cloud computing Scheibe et al. (2019) Analysis study in a power system simulation tool Pileggi et al. (2019) Detect and analyze anomalies in a flexible energy deployment Brosinsky et al. (2020) Digital Dynamic Mirror (DDM) for grid control Park et al. (2020a) Optimization model for microgrid energy storage operation/scheduling Saad et al. (2020b) DT for Networked Microgrids Resiliency against Cyber Attacks Kozhevnikov and Kaplin (2019) Fault diagnosis and maintenance of power grid equip. and transmission lines Barszcz and Zabaryłło (2019) A method for automated fault detection with analytical rotordynamic model Errandonea et al. (2020) Review of DT for maintenance Peng and Wang (2019) Condition monitoring for power converters cycling, the power plant flexible operations can be improved via digitalization and connected plant technologies using DTs. ...
The complex future power plants require digital twin (DT) architecture to achieve high reliability, availability and maintainability at lower cost. The available research on DT for power plants is limited and lacks details on DT comprehensiveness and robustness. The main focus of the present study is to propose a comprehensive and robust DT architecture for power plants that can also be used for other similar complex capital-intensive large engineering systems. First, overviews are conducted for DT key research and development for power plants and related energy savings applications to provide current status, guidelines and research gaps. Then, the requirements and rules for the power plant DT are established and the major DT components are determined. These components include the physics-based formulations; the statistical analysis of data from the sensor network; the real-time data; the pre-performed localized in-depth simulations to predict activities of the corresponding physical twin; and the system Genome with a digital thread that connects all these components together. Recommendations and future directions are made for the power plant DT development including the need for real data and physical description of the overall system focusing on each component individually and on the overall connections. Data-driven algorithms with capabilities to predict the system’s dynamic behavior still need to be developed. The data-driven approach alone is not sufficient and a low-order physics based model should operate in tandem with the updated latest system parameters to allow interpretation and enhancing the results from the data-driven process. Discrepancies between the dynamic system models (DSM) and anomaly detection and deep learning (ADL) require in-depth localized off-line simulations. Furthermore, this paper demonstrates the advantages of the developed ADL algorithm approach and DSM prediction of the DT using vector autoregressive model for anomaly detection in utility gas turbines with data from an operational power plant.
... The complexity of such multi-modal energy systems subsequently increases [1], which affects modeling and control in several fields, such as cost-efficiency, financial viability, technological push-effects, usability, and technology acceptance. Digitalization and digital transformation describe the process of continuously transforming energy systems to cyber-physical energy systems [2,3]. This digital transformation of energy systems includes elements of high resilience requirements, interdisciplinary research settings, user acceptance issues and creates the need for collaborative research and collaboration among several stakeholders in cyber-physical critical infrastructures. ...
Energy systems are changing rapidly and energy research is fundamental to enable and optimize this change involving academics, practitioners, and the public. Therefore, an open digital platform to share knowledge and experiences is crucial for the energy sector. We identify and discuss requirements from 36 semi-structured interviews with various stakeholders for a platform based on five essential elements. The competence element enables researchers and developers to find suitable partners for their research and practice projects, and the best practices element delivers ideas to structure cooperative energy research. The repository element helps to find available data and frameworks for energy systems' simulation and optimizations. Frameworks and models are coupled by using the simulation element. Last, results and contents from the energy community can be published within the transparency element to reach various interested stakeholders. We discuss implications and recommendations as well as further research directions. 1 Introduction Digital transformation is a crucial process in the conversion of the current energy systems for the challenges of post-fossil power generation and supply systems. With the ongoing development in renewable energy generation and flexible infrastructures in the field of energy supply and distribution, the whole system, comprising multiple energy sectors, is continuously changing. The complexity of such multi-modal energy systems subsequently increases [1], which affects modeling and control in several fields, such as cost-efficiency, financial viability, technological push-effects, usability, and technology acceptance. Digitalization and digital transformation describe the process of continuously transforming energy systems to cyber-physical energy systems [2, 3]. This digital transformation of energy systems includes elements of high resilience requirements, interdisciplinary research settings, user acceptance issues and creates the need for collaborative research and collaboration among several stakeholders in cyber-physical critical infrastructures.
... Furthermore, with the extensive integration 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. [110] District EMS [111] Monitoring and control of current source inverters [48] Wind farm monitoring and analysis [112] Power transformer monitoring [113] Renewable energy generator twinning [114] CPSs twinning [115] Microgrid design and EMS [116] Smart home management SoH monitoring and predictive maintenance [117] Power transformer SoH monitoring [118] Anomaly detection of battery [119] Prognostics and health management of a WT gearbox [80] Estimating RUL of the power converter of fixed and floating offshore WTs [81] SoH monitoring of a Lithium-ion battery pack in a spacecraft [83] Monitoring battery degradation [120] Lithium-ion and lead-acid battery management systems ...
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.
... DT can succor modern monitoring systems to deal with cutting edge technologies. DT can provide efficient solutions to deal with the complexity of systems [68,123,[131][132][133][134][135][136][137]137] ...
In the industry 4.0 era, the Smart Energy System (SES) should be able to address the emerging challenges of digitization and socioeconomic/ecologic transition along with other critical entities of the society. However, because of the complexity of this system, both researchers and practitioners are seeking an agile and smart solution. The main motive of this review is to investigate the applications and implementation of Digital Twin (DT) in the provision of energy services. Research Questions (RQ) of this study include: RQ1: What are the applications of DT in SES and how effective is DT in that use case of EIoT? RQ2: Which issues of an SES can be addressed efficiently by using DT? Through answering the mentioned questions, the current study is heading to following objectives (O), O1: Describe the state of the art of DT in SES. O2: Develop a direction for energy 4.0 management through listing the applications, challenges and important factors of implementing DTs. O3: Provide a list of various approaches in employing DT in the scope of SES. The current study is a systematic literature review (SLR), based on SCOPUS, WOS and IEEE digital libraries. Two keywords (namely “Digital Twin” and “Energy Systems”) have been first used. To achieve the final list of articles, 2 levels of screening have been conducted. The first Screening was based on the relevance of the results concerning research objectives. The second screening was an abstract study. The exclusion/inclusion criteria in the abstract study were based on the research questions. The papers that have the potential of answering one of the research questions have been included. Since the implementation of DT is a rather new topic, both backward snowballing and forward snowballing strategies are implemented to finalize the article selection phase. 60 articles identified by searching through scientific databases and 11 articles have been appended to the list during the snowballing process. The results of the current review provide a managerial guideline for practitioners that are heading to utilize DT, along with an anthology of DT within SES scope to feed possible future studies.
... 文献 [38] 介绍了中压电 缆建模在风电场数字孪生中的应用. 文献 [39] 研究如何使用数字孪生对信息物理能源系统进行异常 检测. 文献 [40] 讨论了将数字孪生在 EMS 中应用的适用性. ...
... The aspect of cybersecurity modeling in cyberphysical systems like SG is of vital importance since modeling attacks under test conditions is extremely difficult in operational environments [12]. For this reason, in [13][14][15], a review of the state of the art of cybersecurity of cyber-physical systems in Industry 4.0 systems is shown. ...
The proliferation of cyber-physical systems is bringing with it the growing need to link these systems with virtual environments. Particularly in the smart grid, the high costs of some devices and especially the imminent need to not be able to manipulate these devices in production environments make necessary mechanisms that allow the manipulation of these physical objects in virtual environments; this has been called a digital twin. On the other hand, cyberattacks are growing in all cyber-physical systems, and in the smart grid, cybersecurity is essential due to the smart grid is a critical infrastructure. This work shows a small implementation of a digital twin system for smart metering systems in a smart home environment for testing cybersecurity issues. The results show that the use of digital twins is feasible in various contexts of the smart grid in particularly in cybersecurity testing.
... (1) Power industry equipment and systems [53]. DT can digitize all stages of the life cycle for power generation equipment [54] used in the power industry [55], such as wind turbine manufacturing, assembly, operation and maintenance, power network data transmission, and user services. (2) Aero-engine DT has been used to solve aircraft-related problems in the aerospace industry [56] from the beginning of DT applications. ...
Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.
... This dimension is mutually exclusive. An example for a single entity provide Orive et al. (2019), while Pileggi et al. (2019) give an example for a system. van der Valk, Hunker, Rabe, and Otto ...
With the advances in information technology, the concept of Digital Twins has gained wide attention in both practice and research in recent years. A Digital Twin is a virtual representation of a physical object or system and is connected in a bi-directional way with the physical counterpart. The aim of a Digital Twin is to support all stakeholders during the whole lifecycle of such system or object. One of the core aspects of a Digital Twin is modeling and simulation, which is a well-established process, e.g., in the development
of systems. Simulation models can be distinguished on the basis of different dimensions, e.g., on the basis of their time perspective. The existing literature reviews have paid little to no attention to this simulation aspect of a Digital Twin. In order to address this, the authors have developed a taxonomy based on an extended literature review to bridge the aforementioned gap.
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https://informs-sim.org/wsc20papers/278.pdf
As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy Systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems.
Research aimed at the design and development of artificial intelligence systems is currently in great demand. These systems have gained particular popularity when integrated into complex multi-level technical complexes, which include energy industry facilities. The introduction of modern information processes for the rapid processing of technical information makes it possible to develop and implement expert systems for developing generative management decisions using an artificial intelligence system. Such modern approaches in the field of digitalization and information processing, such as the creation of digital twins of physical objects, allow for research and qualitative assessment of management decisions when implementing expert systems.
Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.
The article deals with the problem of building Digital Twins and Smart Digital Twins for control and management in power systems. The energy system is understood as a set of energy resources of all types, methods for their production (extraction), transformation, distribution and use, as well as technical means and organizational complexes that ensure the supply of consumers with all types of energy. Integrated intelligent energy systems are analyzed as one of the important trends in the Russian energy sector, and the main directions of digitization of the energy sector are considered. The concept of "digital twins" in technical fields is considered as one of the main digitalization trends, an ontological approach to building digital twins and semantic models for building smart digital twins are proposed. It is proposed to use a fractal approach when performing ontological engineering, which makes it possible to formalize the concepts of the subject area and allows you to build different-scale ontologies using metalevels of ontologies. Formalized models of digital twins and smart digital twins are presented. The developed approaches are illustrated by the example of construction of digital twins of a solar power plant and smart digital twins of a fuel and energy complex. The approach described in the article makes it possible to integrate different levels of digital and smart digital twins into a single digital solution when modeling energy facilities and power systems.
Assessing power systems’ reliability and condition is a difficult task. This is partly due to the complexity of the many interrelated components that compose these systems. As a result, traditional reliability assessment methods are inadequate. This raises the question of whether digital twins can be used to assess the reliability of power systems. The objective of this paper is to consolidate information on the use of digital twins in the electrical industry and demonstrate how they can be used to assess the reliability of such complex systems. To accomplish this, a literature review is conducted. Then a method for evaluating the reliability of a power system with DTs is proposed.
Digital Twins are currently investigated as the technological backbone for providing an enhanced understanding and management of existing systems as well as for designing new systems in various domains, e.g., ranging from single manufacturing components such as sensors to large-scale systems such as smart cities. Given the diverse application domains of Digital Twins, it is not surprising that the characterization of the term Digital Twin, as well as the needs for developing and operating Digital Twins are multi-faceted. Providing a better understanding what the commonalities and differences of Digital Twins in different contexts are, may allow to build reusable support for developing, running, and managing Digital Twins by providing dedicated concepts, techniques, and tool support. In this paper, we aim to uncover the nature of Digital Twins based on a systematic mapping study which is not limited to a particular application domain or technological space. We systematically retrieved a set of 1471 unique publications of which 356 were selected for further investigation. In particular, we analyzed the types of research and contributions made for Digital Twins, the expected properties Digital Twins have to fulfill, how Digital Twins are realized and operated, as well as how Digital Twins are finally evaluated. Based on this analysis, we also contribute a novel feature model for Digital Twins from a software engineering perspective as well as several observations to further guide future software engineering research in this area.
Currently, a mega-trend on digitization and servitization using digital technologies and digital twins to support digital (business) transformation can be observed. In literature, emerging digital technologies are considered as an enabler for the creation of additional value, the strengthening of the customer relationship, and as an accelerator of the servitization process in manufacturing. The introduction of such technologies may result in adaptations of the product & service life cycle as well as the business model. A generic product & service life cycle consisting of four phases – design, simulation, manufacturing, and usage of products and services – serves as a foundation for the integration of digital technologies and related data. It can be observed that there is a gap with respect to data integration between the manufacturing and the usage of products and services. A model-based approach including digital twinning is applied to bridge this gap and to show how data can be integrated along the product & service life cycle. The conceptual approach of such a model-based digital twin environment is presented in form of a meta model. To depict how the integration could be eased and guided in manufacturing, findings of the European project Change2Twin, where a digital twin is established for a paint production pilot, are introduced. This industrial manufacturing pilot was supported by production process models. Additionally, a physical experiment was created to raise awareness for the challenges of digitization and servitization.
特高压交直流的建设与大规模新能源接入使大电网的调控越来越复杂，而数字孪生能够为电网向数字化转型、提高电网调度运行决策的准确性与实时性提供关键技术支撑。本文首先建立了复杂大电网数字孪生虚拟模型，提出了实现电网物理系统与数字孪生系统的实时信息交互和协同控制方法，并构建了电网数字孪生软件平台。其次介绍了基于数字孪生的秒级在线分析系统的架构及实施方案，将在线分析系统的响应速度由分钟级提升到秒级。最后对于数字孪生在复杂大电网调度运行的四个方面应用场景进行了探讨。
This paper addresses how a power system Digital Twin (DT) can be structured, what it should be able to do, and how it can possibly be implemented with already known methods. To this end, a structural framework for the design of power system DTs is presented. The framework consists of functional blocks such as model execution and model validation, with which the core of the DT, the virtual entity, is be built. Subsequently, a power system DT has been studied. Here, established approaches of state and parameter estimation like the weighted least squares or extended Kalman filter have been used as parts of the functional blocks. Combined they form an adaptive model of the physical system, which can be used in the framework. In particular, it turns out that under certain circumstances the accuracy of potential sensor measurement data may not be sufficient to realize the described methods under field conditions.
Digital twins run concurrently with the assets and
systems they mirror. They offer real-time execution of advanced
control schemes, e.g., for smart buildings, but also various
analysis tasks, e.g., for predictive maintenance that ensures
resilience. In this, they need to cope with change: The physical
twins they mirror operate in a highly dynamic environment with
unknown or unexpected variables and impact factors to which
they adapt or are adapted to via upgrades, maintenance, or
behavioral changes. Keeping digital twins fit for their purpose
over their lifecycle is thus a demanding governance challenge that
needs to be addressed in tandem with the lifecycle management of
the mirrored system. We argue that the best practices of Systems
Engineering (SE) and Information Technology (IT), especially
software development, need to be brought together to succeed.
In this paper, we present a joint SE and IT lifecycle model called
the Double Helix model. It highlights the concurrent governance
processes of both twins, capturing the temporal dynamics of
the environment and the system itself. It accents the important
concept of the leading twin. The Double Helix model captures
both design and operational phases of SE and management, going
from cradle to grave, by empowering a shift from using data in
offline simulations during design, to the online operational phase.
The Digital Twin (DT) is one of the main concepts associated to the Industry 4.0 wave. This term is more and more used in industry and research initiatives; however, the scientific literature does not provide a unique definition of this concept. The paper aims at analyzing the definitions of the DT concept in scientific literature, retracing it from the initial conceptualization in the aerospace field, to the most recent interpretations in the manufacturing domain and more specifically in Industry 4.0 and smart manufacturing research. DT provides virtual representations of systems along their lifecycle. Optimizations and decisions making would then rely on the same data that are updated in real-time with the physical system, through synchronization enabled by sensors. The paper also proposes the definition of DT for Industry 4.0 manufacturing, elaborated by the European H2020 project MAYA, as a contribution to the research discussion about DT concept.
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capable of dealing with a continuously changing environment and emerging requirements that may be unknown at design-time. However, building such systems cost-effectively and in a predictable manner is a major engineering challenge. In this paper, we explore the state-of-the-art in engineering self-adaptive systems and identify potential improvements in the design process.
Our most important finding is that in designing self-adaptive systems, the feedback loops that control self-adaptation must become first-class entities. We explore feedback loops from the perspective of control engineering and within existing self-adaptive systems in nature and biology. Finally, we identify the critical challenges our community must address to enable systematic and well-organized engineering of self-adaptive and self-managing software systems.
Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.
To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have
turned to self-adaptivity. Self-adaptive systems are capable of dealing with a continuously changing environment and emerging
requirements that may be unknown at design-time. However, building such systems cost-effectively and in a predictable manner
is a major engineering challenge. In this paper, we explore the state-of-the-art in engineering self-adaptive systems and
identify potential improvements in the design process.
Our most important finding is that in designing self-adaptive systems, the feedback loops that control self-adaptation must
become first-class entities. We explore feedback loops from the perspective of control engineering and within existing self-adaptive
systems in nature and biology. Finally, we identify the critical challenges our community must address to enable systematic
and well-organized engineering of self-adaptive and self-managing software systems.
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.
to difierentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the efiectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the difierent existing techniques in that category are variants of the basic tech- nique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the difierent directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.
IoT applications and other distributed control applications are characterized by the interaction of many hardware and software components. The inherent complexity of the distributed functionality introduces challenges on the detection and correction of issues related to functionality or performance, which are only possible to do after system prototypes or pilot installations have been built. Correcting these issues is typically very expensive, which could have been avoided by earlier detection. This paper makes four main contributions. (1) It presents a virtual prototyping approach to specify and analyze distributed control applications. The approach is based on a domain model, which can be configured for a specific application. It consists of eight domain-specific languages (DSLs), each describing one system aspect. (2) The DSLs provide each stakeholder in the application’s lifecycle a natural and comprehensible way to describe his/her concerns in an unambiguous manner. (3) The paper shows how the DSLs are used to automatically detect common configuration errors and erroneous behavior. (4) The virtual prototyping approach is demonstrated using a lighting domain case study, in which the control system of an office floor is specified and analyzed.
The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
Purpose
– This article aims to provide an insight into recent deliberations on the possibility of a global sensor market reaching one trillion units per annum within the next decade.
Design/methodology/approach
– Following an introduction, which includes details of the TSensors Summit, this article discusses existing high volume sensor applications with multi-billion unit growth prospects. It then considers certain new and emerging applications, including the Internet of Things. This is followed by technological considerations and a brief discussion.
Findings
– The possibility of a global sensor market reaching one trillion units per annum within the next decade is the topic of serious debate. Several applications representing multi-billion levels have been identified and the ongoing TSensors Summit activities seek to identify further high volume, high growth uses and the factors that will stimulate them. While MEMS will play a central role, other, often new sensor technologies will be vital to achieving the trillion unit level.
Originality/value
– This article provides a timely review of recent deliberations surrounding the feasibility of achieving a global, trillion sensor market.
Modern Accident Investigation and Analysis