Digital Twins for Smart Grids

Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

A digital twin (DT) system is a virtual system that can provide a comprehensive description of a real physical system. The DT system continuously receives data from physical sensors and user input information and provides information feedback to the physical system. It is an emerging technology that utilizes an advanced Internet of Things (IoT) to connect different objects, which is in high demand in various industries and its research literature is growing exponentially. Traditional physical systems provide data support for the monitoring of physical objects such as buildings through digital modeling techniques, data acquisition tools, human computer interfaces, and building information models (BIM). However, DT can offer much more than data presentation. DT uses the received data to perform operations such as analysis, prediction, and simulation, and finally transmits the analysis results to the physical system as feedback. Compared with other physical systems, DT has the characteristics of bidirectional data exchange and real-time autonomous management. The plant factory control system based on digital twin technology continuously measures the power consumption of electrical equipment through the sensors of the physical system and makes the corresponding virtual color-coded gradient map based on the obtained data. The darker the virtual device is, the more power it currently requires, and just based on the shade of color gives the user a very intuitive idea of the current power usage of the electronic device. There has been extensive research on digital twin technology, but there are few studies on implementing plant factories based on digital twin technology. This paper proposes the idea of combining digital twin technology with plant factories to provide research directions for future smart agriculture. It proves that smart agricultural production with sustainability can also benefit from this idea.

A digital twin is a virtual representation of a physical system, which can be used for prediction and control. This representation can include predictive models and simulations, which are continuously updated with physical system data. Autonomous control using a digital twin can be considered as a cognitive loop, involving interpretation of sensor data (perception), integration of the data into predictive models (which can involve reasoning) and the planning and execution of actions on the physical system. In particular, meta-reasoning can be used to monitor and control the algorithms and models of the digital twin. This is important for autonomous problem detection and recovery, as well as for explanation and transparency.Our approach to cognitive digital twin systems is first to specify a high-level architecture, grounded in cognitive systems research (as is often done in robotics). We then provide a background and survey of meta-reasoning and include it as an additional layer in the architecture. Finally, we outline a roadmap for gradually increasing the autonomy and resilience of a digital twin system by iterative development in a real-world environment.

Digital twin (DT), which integrates physical networks and digital space by using advanced technologies of sensing, communication and computation, has been envisioned as a promising paradigm for improving the quality of service in physical systems. In this paper, we propose a federated learning (FL)-enabled DT system consisting of the physical layer and DT layer. With FL, all wireless devices (WDs) can collaborate to update a universal DT model, after the DT server cluster (DSC) aggregates all the local models sent by the WDs with non-orthogonal multiple access (NOMA). Moreover, an action model based on the DT system is also updated to optimize the operations of WDs. To increase the energy efficiency, we formulate a problem to minimize the cost of the total energy consumption of the system by optimizing the time allocation of local training, uploading the local models, generating the action model as well as broadcasting the action model and DT model. The numerical results validate the effectiveness and efficiency of our proposed algorithm.

Complex electronic equipment final assembly workshops face challenges such as high coupling between information and physical systems, difficulty in integrating multi-source heterogeneous data, and frequent dynamic disturbances. These lead to severe information silos, lagging responses in dynamic scheduling, and low management efficiency. Based on Cyber-Physical System (CPS) theory, this paper proposes and constructs a digital workshop integrated application platform featuring a real-time data-driven collaborative scheduling framework integrating Digital Twin (DT) and Multi-Agent System (MAS). Firstly, a unified CPS framework integrating Manufacturing Operations Management (MOM), Supervisory Control and Data Acquisition (SCADA), Warehouse Management System (WMS), Visualization System (VDS), Advanced Planning and Scheduling (APS), and Digital Twin (DT) is established. Secondly, an ontology-based data encoding specification is designed to reduce interface coupling between systems. Furthermore, through the deep integration of DT and MAS, real-time dynamic response under high-frequency disturbances and closed-loop optimization of the assembly process are achieved. Application on a pulse assembly line for complex electronic equipment shows: assembly disturbance response time reduced by 50%, first-pass yield rate of final assembly increased to 99.32%, material distribution and plan matching degree exceeding 99%, average production waiting time reduced by over 30%, significantly enhancing workshop transparency and overall management and control efficiency.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

The rapid digitalization of manufacturing systems has led to the emergence of Cyber-Physical Systems (CPS) and Digital Twins (DTs) as key enablers of the Industry 4.0 revolution. While CPS provides real-time sensing, communication, and control across physical and cyber domains, DT offers a virtual mirror of assets and processes, enabling simulation, prediction, and optimization. The integration of these two paradigms creates a synergistic framework that enhances adaptability, resilience, and sustainability in modern manufacturing. This chapter explores the synergy between CPS and DT, emphasizing how their convergence enables real-time decision-making, predictive maintenance, process optimization, and lifecycle management. The conceptual architecture of DT–CPS integration is presented, highlighting data acquisition, interoperability, and feedback mechanisms. Representative applications in smart manufacturing, energy management, and automation are discussed to demonstrate their potential for sustainable industrial transformation. Finally, the chapter outlines implementation challenges, emerging research directions, and the evolving role of DT–CPS ecosystems in shaping intelligent, resource-efficient manufacturing systems aligned with the goals of Industry 5.0.

Cyber Physical Systems (CPS) are redefining modern engineering practices by bringing the computational systems together with physical processes, boosting efficiency, security, and sustainability in different industries. This research highlights the influence of CPS in different sectors: its power to facilitate realtime decision making, predictive maintenance, and cybersecurity issues. The context in which this study occurs is to the application of digital twin technology in CPS — which is physical systems’ simulation for performance optimization and downtime reduction. The research includes the analysis of the efficiency of cybersecurity measures applied in CPS environment, the identification of vulnerabilities, and the suggestions of solutions for the mitigation of a cyber threat. Digital twins and predictive maintenance appears to improve the system performance by 30%, reduces the downtime by 25% due to application. Moreover, the research addresses upcoming concerns of sustainable practices in CPS and emerging technologies such as Industry 4.0 that promote resource optimization and waste reduction. The results show that CPS can improve both the operational efficiency and the environmental sustainability by enabling a smarter resource management. This work advances our understanding of trends, challenges and future directions in CPS as a means to inform engineering management practices, which are enabled by CPS.

With the development of digital twin, the research on the fusion of intelligent manufacturing system and digital twin technology attracts more attention. In this paper, the realization of digital twin is studied for geometric feature online inspection system of body-in-white (BIW). A three-level virtual modeling approach of Element-Behavior-Rule is proposed for the physical environment modeling in a digital environment. An element model, behavior model and rule model are defined, respectively, and the relationship among the three-layer model is given for real-time communication. A three-layer communication architecture of the digital twin system is designed for the real-time mapping between physical and virtual space, and the process of the online inspection digital twin system is analyzed. Finally, on the basis of the online inspection physical experimental platform and JHIM (Jiangheng Intelligent Manufacturing) software platform, the digital twin system for geometric feature online inspection of BIW is developed, which shows that the virtual model can be driven in real time by real-time physical inspection data. Digital twin technology provides a feasible way for real-time monitoring of the online inspection system.

Introduction: To date, a variety of studies have been published on the topic of long term energy system transition. Most studies on future energy systems, however, have a shorter time frame or adopt a supranational focus (e.g. the Energy Roadmap, 2011 or the World Energy Outlook, 2015). It then constitutes a sincere challenge to perform a national energy system transition study with as time horizon 2050 and covering a far-reaching transformation of the energy system. VITO, together with the FPB (Federal Planning Bureau) and ICEDD, performed a study to scrutinise the transition of the Belgian national energy system towards a future mix entirely based on renewable energy sources. The focus on renewable energy sources and on building a national energy system completely running on renewable energy can be traced back to three main concerns: – Climate change: Renewable energy sources (RES) are a major instrument in the fight against climate change as RES do not release (net) greenhouse gas emissions. – Security...

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

The inevitable transition of the power system toward a sustainable and renewable-energy centered power system is accompanied by huge versatility and significant challenges. A corresponding shift in operation strategies, embracing more intelligence and digitization, e.g., a Cyber-Physical System (CPS), is needed to achieve an optimal, reliable and secure operation across all system levels (components, units, plants, grids) and by the use of big data. Digital twins (DTs) are a promising approach to realize CPS. In this paper, their applications in power systems are reviewed comprehensively. The review reveals that there exists a gap between available DT definitions and the requirements for DTs utilized in future power systems. Therefore, by adapting the current definitions to these requirements, a generic definition of a “Digital Twin System (DTS)” is introduced which finally allows proposing a multi-level and arbitrarily extendable “System of Digital Twin Systems (SDTSs)” idea. The SDTSs can be realized with an open-source framework that serves as a central data and communication interface between different DTSs which can interact by “Reporting Modules” and are regulated by “Control Modules” (CMs). Exemplary application scenarios involving multiple system levels are discussed to illustrate the capabilities of the proposed SDTS concept.

Aiming at the problems of single inspection mode and poor real time performance in current substations, a substation inspection visualization system based on digital twins is proposed. The development process of substation twin system is introduced. Based on the digital twin five-dimensional model, the connection between physical entities and twin entities is established. The modeling of physical entities, real-time data mapping and result analysis are described respectively. At the same time, the verification and analysis of the system results can prove that the model based on the digital twin system can effectively carry out virtual real synchronization and prove the effectiveness of the system.

Recently, more and more digital twin technologies are getting attention in managing and operating urban metros. The fact that integrating time-monitor data like a metro digital twin right now has chances to improve its operation and dependability. This essay is mostly about the applications of digital twins for the metro system: infrastructure monitoring and operation management. Digital twin, at the infrastructure level, can be utilized in observing the structure inside the metro tunnel to see if there is a dangerous situation. For example, a swelling on Earth. After applying our collected data to a predictive analysis, this will offer us an early warning and allow preventive maintenance. In terms of operation, a digital twin system would help people to monitor the operation of metro network, which means smoother passenger flow. Although the metro system's digital twin technology has great benefits, problems like data integration, reliability of the whole system and its security still need to be solved. More studies and practices need to take place to make sure that digital twin tech is employed safely and correctly on future metros.

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly.