Conference Paper

A Digital Twin for Cyber-Physical Energy Systems

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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.

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... 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. ...
... 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. ...
Article
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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. ...
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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. ...
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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. ...
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Full-text available
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.
... 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] . ...
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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). ...
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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. ...
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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. ...
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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 ...
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... 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] ...
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... 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. ...
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... (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. ...
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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 ...
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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。
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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.
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