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Distributed energy resources and the number of relays are expected to rise in modern electrical grids; consequently, relay misoperations are also expected to grow. Relays can detect electrical fault types using an internal algorithm and can display the result using light indicators on the front of the relay. However, some relays' internal algorithms for predicting types of electrical faults could be improved. This study assesses a relay's external and internal algorithms with an Advanced Synchronized Time Digital Grid Twin (ASTDGT) testbed with paired relays. A misoperation relay analysis focused on measuring the accuracy of using the boundary admittance (the external algorithm) versus the set-default (the internal algorithm) relay method to determine the electrical fault types was performed. In this study, the internal and external relay algorithms were assessed with a synchronized time digital grid twin testbed using a real-time simulator. This testbed evaluated two sets of logic at the same time with the digital grid twin and paired relays in the loop. Different types of electrical faults were simulated, and the relays' recorded events and electrical fault light indicator states were collected from the human–machine interfaces. This ASTDGT testbed with paired relays successfully evaluated the relay algorithm misoperations. The boundary admittance method had an accuracy of 100% for line-to-line, line-to-ground, and line-to-line ground faults. [ABSTRACT FROM AUTHOR]

Previous research employing a conceptual approach with a digital twin has demonstrated that (noise-based) dynamic watermarking is incapable of providing unconditional security in smart electrical grid systems. However, the implementation of digital twins can be prohibitively costly or infeasible due to limited available data on critical infrastructure. In this study, we first analyze the spectral properties of dynamic watermarking and its associated protocol. Subsequently, we present a straightforward attack inspired by the digital twin method, which extracts and utilizes the grid noises and completely breaches the security of dynamic watermarking without requiring knowledge of the private watermarking signal. The attacker can fully expose the grid while evading detection by the controller. Our findings indicate that in the absence of secure and authenticated communications, dynamic watermarking offers neither conditional nor unconditional security. Conversely, when communication lines, sensors, and communicators are equipped with tamper-resistant and secure/authenticated links, dynamic watermarking becomes redundant for grid security. [ABSTRACT FROM AUTHOR]

Reconstructing three-dimensional (3D) models of power equipment plays an increasingly important role in advancing digital twin power grids. To reconstruct a high-precision model, it is crucial to accurately obtain the pylon type and its necessary parameter information before modeling. This study proposes an improved method for identifying pylon types based on similarity measurement and a linearly transformed dataset. It begins by simplifying the identification of point clouds using the pylon shape curve. Subsequently, the resemblance between the curve and those curves within the dataset is evaluated using a similarity measurement to determine the pylon type. A novel method is proposed for calculating the characteristic parameters of the pylon point clouds. The horizontal and vertical distribution characteristics of the pylon point clouds are analyzed to identify key segmentation positions based on their types. Feature points are derived from key segmentation positions to calculate the characteristic parameters. Finally, the pylon 3D models are reconstructed on the basis of the calculated values. The experimental results showed that, compared with other similarity measurements, the Hausdorff distance had the best effect as a similarity measurement using the linearly transformed dataset, with an overall evaluation F-score of 86.4%. The maximum relative error of the calculated pylon parameters did not exceed 5%, affirming the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on Deep Reinforcement Learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including Deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates. [ABSTRACT FROM AUTHOR]

Electronic control strategies are pivotal in the evolution of power systems, which have higher requirements for power leveling and optimization, frequency safety, and frequency stability. In contrast, the core objectives of existing energy storage services are mostly limited to one function, which cannot fully meet the operational requirements of power systems. This paper presents research on a frequency security controller based on digital twin technology and aimed to enhance the safety of the system. The proposed controller can simultaneously smooth out active wind power fluctuations and optimize reactive power, participate in system frequency regulation, and improve system damping to damp low-frequency oscillations based on simulating the actual operating environment. Simulations also verify the effectiveness of the proposed controller in DIgSILENT/PowerFactory based on a two-area system. This active/reactive power-based system support service will bring new economic benefits to wind energy storage systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The operation of the electricity network has grown more complex due to the increased adoption of renewable energy resources, such as wind and solar power. Using energy storage technology can improve the stability and quality of the power grid. One such technology is flywheel energy storage systems (FESSs). Compared with other energy storage systems, FESSs offer numerous advantages, including a long lifespan, exceptional efficiency, high power density, and minimal environmental impact. This article comprehensively reviews the key components of FESSs, including flywheel rotors, motor types, bearing support technologies, and power electronic converter technologies. It also presents the diverse applications of FESSs in different scenarios. The progress of state-of-the-art research is discussed, emphasizing the use of artificial intelligence methods such as machine learning, digital twins, and data-driven techniques for system simulation, fault prediction, and life-assessment research. The article also addresses the challenges related to current research and the application of FESSs. It concludes by summarizing future directions and trends in FESS research, offering valuable information for further advancement and improvement in this field. [ABSTRACT FROM AUTHOR]

This paper focuses on the importance of Geographical Digital Twins to increase renewable energy production in urban areas and cities for decarbonization and reduction of climate change impacts. The study demonstrates the role of interoperable geographical digital twins based on Free and Open-Source software and geospatial software technologies in the simulation, monitoring, and management of renewable-based energy systems. We simulated the potential for building-integrated and building-attached solar photovoltaic (PV) electricity generation for the use case in cities to allow at a later stage for scale up to a nationwide level. The platform provides a tool for estimating PV power generation at high resolution across entire neighbourhoods and districts. Tools for the identification of cost-efficient PV placement or integration in buildings on roof-tops and facades were implemented to allow for interactive selection of optimal PV placement. The developed platform serves multiple beneficiaries, including municipalities, urban planners, developers, citizens, investors, and energy communities. Overall, the study highlights the advantage of an interoperable geographical urban digital twin, which provides the flexibility necessary to simulate and test scenarios for rapid, integrated urban planning for decarbonization under climate change. The platform is based on open-source, open standards and APIs, using open 3D city models, and allowing seamless integration of simulation and assessment methods and tools to provide a 3D real-world environment to assess and develop energy transition approaches. [ABSTRACT FROM AUTHOR]

This article addresses a digital twin-based real-time analysis (DTRA) to meditate the power system vulnerability whenever cascading failures and blackouts occur for any reason, and thus, to improve the resiliency. In addition to this, a water-power package is proposed to enhance the vulnerable percentage of the system by promptly syringing energy to the grid under line/generator outage contingencies. To this end, in the first place, we will develop a digital twin model along with a cloud platform derived from the Amazon Cloud Service (ACS) into the Amazon Web in order to scrutinize the online vulnerability data arising from the equivalent physical twin in real-time. Indeed, such a DTRA model can help us check the real grid's behavior and determine how to meet the needs of the energy hub system to prevent blackouts. Additionally, a modified bat-based optimization algorithm is matched to settle the energy between the hub system and the electrical grid in furtherance of real-time analysis. To raise awareness, we will first compile how the hub system interactions can be effective in declining the vulnerability indices, and afterward, we will map out the ACS-based digital twin model on the studied case. [ABSTRACT FROM AUTHOR]

With the widespread applications of electric vehicles, power grid stabilization, and high‐pulsed power loads, high‐power lithium‐ion batteries (LIBs) are in urgent demand. However, the existing experimental‐based design of high‐power batteries is usually costly and inefficient, and provides limited information on the complex physicochemical processes inside the batteries. Digital twin concept is promising for capturing the batteries' electrochemical performance, and optimizing the power capability of LIBs. Here, an electrochemical‐thermal coupled model is developed as a digital twin model for rational design of ultrahigh‐power LiFePO4/graphite LIBs. The model can accurately predict the batteries' performance and help to predetermine the optimal parameters to achieve an ultrahigh power capability. After model‐guided optimization, the battery shows a high energy density of 92.38 Wh kg−1 at an ultrafast discharging current of 50 C and can withstand 150 C pulse discharging tests. Notably, the digital twin model can reveal experimentally inaccessible time‐ and space‐resolved information and identify the rate‐determining steps inside the battery. Hence, model‐driven optimization of ultrahigh‐power LiFePO4/graphite batteries is successfully realized aiming at the critical factors in the rate‐determining steps. The work provides an instructive design of ultrahigh‐power LiFePO4/graphite batteries, which might guide the future direction to boost the power capability of LIBs. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the integration and intelligence of ubiquitous power Internet of things system equipment, its cost in life cycle management such as data service, operation monitoring, and safety protection is becoming higher and higher. At the same time, the probability of power grid failure, reduced operation efficiency, and function failure will gradually increase with the development of its scale. Accurately evaluating the metering performance of distribution network digital metering system in the operation of power system and improving its accuracy, stability, and reliability are the key issues for its future use in power trade settlement. In this paper, a digital reconstruction method of power metering production data based on digital twin is proposed. The operation data of power system is collected in real time through the data acquisition module, the operation data is analysed, identified, and predicted, and then the analysis, identification, and prediction results are used to realize the visualization and digitization of operation data. Further, the operation data of the digital metering system are monitored and analysed to verify the effectiveness of the method. The advantages and disadvantages of the design scheme of the digital metering system are obtained by analysing the active power error, which provides the basis of practice and selection for the application of the digital metering system. [ABSTRACT FROM AUTHOR]

The high-level goal of this research work is to enhance solar integration into power distribution grids with a lower cost and less complexity. High penetration levels of solar can lead to power quality issues at the point of interconnection such as flicker and voltage violation. This paper presents a novel Delta-Q approach that determines the most appropriate amount of reactive power support independent of the time step. The proposed scheme is embodied in the solar smart inverters' Volt-VAR control. It concurrently mitigates voltage violation and smoothens the voltage profile in areas where the solar irradiance is highly intermittent. The performance of the proposed control scheme is evaluated on the Typhoon HIL digital twining testbed along with the dSPACE MicroLabBox rapid control prototype unit. The real-time analysis shows the effectiveness of the proposed technique in lowering voltage violations and also mitigating voltage fluctuations. • A novel Volt/VAR control is proposed for smart solar inverter. • The proposed control strategy contributes to power quality enhancement in smart grids. • A digital twining testbed is utilized for validation/verification. • The proposed control scheme brings the cost and complexity down. [ABSTRACT FROM AUTHOR]

Apart from conventional centralized control and administration, power systems entered the smart grid era, whilst the burgeoning Industry 4.0 demanded production and distribution in terms of digital data exchange and IoE (Internet of Everything)-based platforms. The usage of a digital twin in power system operation is an advanced version of IIoT (Industrial Internet of Things). In terms of a more secure and dependable grid network, interconnected cloud architecture and online remote administration provide effective vulnerability management. Yet, to fully exploit the promise of digital twin technology in the electric grid, a conceptual framework is required to define the critical parts and procedures involved in constructing and deploying a digital twin system. A conceptual framework based on systems engineering approaches is proposed in this paper which takes the electric grid's particular characteristics, such as system complexity, the necessity for real-time surveillance and control, and the significance of security and dependability into consideration. Accelerating the adoption of digital twin technologies in the electric grid can be made viable through the establishment of a well-defined and comprehensive development framework. This approach will contribute to the realization of a power system that is characterized by enhanced stability, efficiency, and sustainability. Considering the issue to be a multivariable optimization problem, a systems engineering approach is chosen based on component-based modeling, overall system configuration, and functional analysis. • Twin grid mirrors elements, integrates for predictive maintenance and data flow. • Systems engineering constructs twin grid, accurately mimicking components. • Dynamic math model crucial for predictive analysis and lifecycle impact. • Component-based modeling replicates grid for twin operations, considering lifespan. • Digital twin electric grid leverages tech support and Industry 4.0 advancements. [ABSTRACT FROM AUTHOR]

The integration of a large number of photovoltaic systems into the electricity grid presents significant technical challenges. To address the issue of excess power injection into the grid, various standards recommend curtailment of PV systems, which results in penalizing renewable energy. This paper proposes a solution to the problem without resorting to curtailment. The key innovation is to optimize the energy injected into the grid, achieving a delicate balance between benefits for prosumers and the grid operator, while also avoiding PV curtailment. The results obtained underscore substantial advantages for prosumers when contrasted with commercial solutions that resort to curtailing PV power, while ensuring the alignment of interests with network providers. The proposed energy management system is based on a multi-objective linear programming algorithm and aims to increase energy injection into the grid during peak regional demand while simultaneously considering the electricity price profile. The study utilizes digital twin technology to establish a dependable framework for validating proposed algorithms and comparing simulated results with experimental ones. By doing so, this process guarantees the accuracy of simulation results and allows for informed decision-making regarding the system under investigation. [ABSTRACT FROM AUTHOR]

The performance of microgrid operation requires hierarchical control and estimation schemes that coordinate and monitor the system dynamics within the expected manipulated and control variables. Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation technologies are reviewed for developing dynamic models of smart microgrids. The hierarchical system of a microgrid control consists of three architectural layers, primary, secondary and tertiary, which need to be supported by real-time monitoring and measurement environment of the system variables and parameters. Various control and estimation schemes have been devised to handle the dynamic performance of microgrids in the function of control layers requirement. Firstly, control schemes in the innovative grid environment are evaluated to understand the dynamics of the developed technologies. Six control technologies, linear, non-linear, robust, predictive, intelligent and adaptive, are mainly used to model the control design within the layer(s) regardless of the types of microgrids. Secondly, the estimation technologies are evaluated based on the state of variables, locations and modelling of microgrids that can efficiently support the performance of the controllers and operating microgrids. Finally, a future vision for designing hierarchical and architectural control techniques for the optimal operation of intelligent microgrids is also provided. Therefore, this study will serve as a fundamental conceptual framework to select a perfect optimal design modelling strategy and policy-making decisions to control, monitor and protect the innovative electrical network. [Display omitted] • Frameworks for optimal control and monitoring of smart power grids are presented. • Propose novel control structures as a golden opportunity for autonomous power grid development. • Assess the opportunity of modern energy systems for sustainable development goals. • Develop a digital thread framework for digital twin designs of a modern power grid. • Provide challenges and future perspectives for autonomous power grid technologies. [ABSTRACT FROM AUTHOR]

Large-scale grid-connected lithium-ion batteries are increasingly being deployed to support renewable energy roll-out on the power grid. These battery systems consist of thousands of individual cells and various ancillary systems for monitoring and control. Although many studies have focused on the behaviour of single lithium-ion cells, the impact of system design choices and ancillary system controls on long-term degradation and efficiency of these systems, containing thousands of cells, has rarely been considered in detail. Here, we simulate a 1 MWh grid battery system consisting of 18,900 individual cells, each represented by a separate electrochemical model, as well as the thermal management system and power electronic converters. Simulations of the impact of cell-to-cell variability, thermal effects, and degradation effects were run for up to 10,000 cycles and 10 years. It is shown that electrical contact resistances and cell-to-cell variations in initial capacity and resistance have a smaller effect on performance than previously thought. Instead, the variation in degradation rate of individual cells dominates the system behaviour over the lifetime. The importance of careful thermal management system control is demonstrated, with proportional control improving overall efficiency by 5%-pts over on–off methods, also increasing the total usable energy of the battery by 5%-pts after 10 years. • Simulation study of 1 MWh grid battery system with 18900 cells cycled for 10 years. • All cells modelled with individual electrochemical models including degradation. • Cells thermally and electrical coupled and supported by ancillary systems. • Exploration of cell variability, interconnection resistance, and thermal management. • Results show key influence of thermal management design and control on lifetime. [ABSTRACT FROM AUTHOR]