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We present a novel multi‐layer method for extended position‐based dynamics that exploits a sequence of reduced models consisting of rigid and elastic parts to speed up convergence. Taking inspiration from concepts like adaptive rigidification and long‐range constraints, we automatically generate different rigid bodies at each layer based on the current strain rate. During the solve, the rigid bodies provide coupling between progressively less distant vertices during layer iterations, and therefore the fully elastic iterations at the final layer start from a lower residual error. Our layered approach likewise helps with the treatment of contact, where the mixed solves of both rigid and elastic in the layers permit fast propagation of impacts. We show several experiments that guide the selection of parameters of the solver, including the number of layers, the iterations per layers, as well as the choice of rigid patterns. Overall, our results show lower compute times for achieving a desired residual reduction across a variety of simulation models and scenarios. [ABSTRACT FROM AUTHOR]

We propose MiNNIE, a simple yet comprehensive framework for real-time simulation of nonlinear near-incompressible elastics. To avoid the common volumetric locking issues at high Poisson's ratios of linear finite element methods (FEM), we build MiNNIE upon a mixed FEM framework and further incorporate a pressure stabilization term to ensure excellent convergence of multigrid solvers. Our pressure stabilization strategy injects bounded influence on nodal displacement which can be eliminated using a quasiNewton method. MiNNIE has a specially tailored GPU multigrid solver including a modified skinning-space interpolation scheme, a novel vertex Vanka smoother, and an efficient dense solver using Schur complement. MiNNIE supports various elastic material models and simulates them in real-time, supporting a full range of Poisson's ratios up to 0.5 while handling large deformations, element inversions, and self-collisions at the same time. [ABSTRACT FROM AUTHOR]

In this work, a linear quadratic regulator controller is utilized to optimize the gains of the machine-side converter and grid-side converter controllers. The DC-link voltage and the rotational speed of the doubly-fed induction generator are optimally regulated by these controls. Additionally, the small-signal stability of the proposed system has been evaluated using the linearized model of the system. The small-signal stability analysis of the designed system has been accomplished through the application of the eigenvalues and participation factors technique. The suggested controller has been tested in a variety of operating conditions under small disturbances. MATLAB/SIMULINK simulation simulations have been used to evaluate the efficacy of the suggested optimal control system. Additionally, the generator-angular speed and DC-link voltage dynamic performances demonstrate the efficacy of the proposed controller. Finally, the robustness of the proposed controller has also been tested and validated using OPAL-RT technology under large perturbations at stator terminal voltage. [ABSTRACT FROM AUTHOR]

Green hydrogen production is a sustainable energy solution with great potential, offering advantages such as adaptability, storage capacity and ease of transport. However, there are challenges such as high energy consumption, production costs, demand and regulation, which hinder its large-scale adoption. This study explores the role of simulation models in optimizing the technical and economic aspects of green hydrogen production. The proposed system, which integrates photovoltaic and energy storage technologies, significantly reduces the grid dependency of the electrolyzer, achieving an energy self-consumption of 64 kWh per kilogram of hydrogen produced. By replacing the high-fidelity model of the electrolyzer with a reduced-order model, it is possible to minimize the computational effort and simulation times for different step configurations. These findings offer relevant information to improve the economic viability and energy efficiency in green hydrogen production. This facilitates decision-making at a local level by implementing strategies to achieve a sustainable energy transition. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), remains a critical study area to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that transcends traditional approaches based on estimating the available capacity using the integral of the battery current or estimating the increase in internal resistance. The SOH estimator employs a partial discharge method (PDM) and a linear state-of-charge (SOC) observer based on an equivalent electrical circuit model (ECM), utilizing readily available manufacturer data and designed for real-time applications. The proposed method was tested and validated using three different automotive battery technologies and a real-time simulation on the OPAL-RT platform. The simulations involved voltage and current measurements of pulsed-discharge current profiles under temperature-controlled conditions and an electric vehicle driving profile. The results showed a high accuracy in SOH estimation, with a maximum standard deviation of approximately 0.03497 V for lithium-ion batteries, representing about 7.124% of the mean value of the SOH estimator output. For other technologies, the standard deviations were even lower, all below 0.61% of their respective mean values. These outcomes demonstrate the reliability and accuracy of our method, making it suitable for real-time SOH estimation in EV-BMSs. [ABSTRACT FROM AUTHOR]

This paper addresses aliasing oscillations encountered in hardware-in-the-loop (HIL) simulation caused by inaccurate duty cycle detection in high-frequency power electronic applications. Oversampling has been commonly used as a solution to detect switching events more accurately. Traditional oversampling methods use the extra information obtained by oversampling the inputs for further computations to enhance the precision of the simulation. However, these techniques increase the complexity of the model since they take into account several switch states during each simulation step. To mitigate these complexities, the integration oversampling method (IOM) was introduced as a recent alternative with minimum impact on the model complexity. IOM provides a modified switching pattern that effectively prevents aliasing oscillations while maintaining a single switch-state for each simulation step. It can be implemented as an independent block between the controller and the HIL model, so it keeps the HIL model unchanged. This study highlights the limitations detected in applying IOM to the models with complementary switches, including possible undesired short circuits. To overcome these limitations, a novel oversampling method called 4PIOM is presented. 4PIOM further enhances the IOM algorithm by reducing the simulation step and sampling period. The validity of the new method is demonstrated by comparison with previous proposals and also with the same model without any oversampling. Both experimental and MATLAB simulation results prove its superior performance in attenuating the aliasing oscillations and improving the quality of the simulation. [ABSTRACT FROM AUTHOR]

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM's simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems. [ABSTRACT FROM AUTHOR]

Stochastic models can effectively describe the operating characteristics of power electronic converters with stochastic parameters. However, it is difficult to implement the models in field programmable gate array– (FPGA) based real-time simulation, because their high order leads to a large calculation. This article proposes an optimized real-time stochastic modeling method for power electronic converters based on generalized polynomial chaos. First, an orthogonal polynomials construction method is used based on Schmidt orthogonalization to describe stochastic variables with atypical probability distributions and provide conditions for simplifying the system model. Second, the method of probability space transformation is adopted to divide the system model into multiple sub-models to suppress the exponential growth of the model order while maintaining the statistical properties. This method has performed over the traditional stochastic modeling method. The proposed model is built on FPGA-based hardware-in-the-loop experiment platform with 1us simulation step. The optimized model uses approximately 37% fewer resources than the traditional stochastic model while maintaining the same level of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the design of a predictive controller for a boost converter and validation through real-time simulation. First, the boost converter was mathematically modeled, and then the electronic components were designed to meet the operation requirements. Subsequently, a model-based predictive controller (MBPC) and a digital PI (Proportional–Integral) controller were designed, and their performance was compared using MATLAB/SIMULINK®. The controls were further verified by implementing test benches based on an FPGA (Field-Programmable Gate Array) with an OPAL-RT real-time simulator, which included the RT-LAB and RT-eFPGAsim simulation packages. These tests were successfully carried out, and the methodology used for this design was validated. The results showed a better response obtained with MBPC, both in terms of stabilization time and lower overvoltage.

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes. [ABSTRACT FROM AUTHOR]

This paper presents the design of a predictive controller for a boost converter and validation through real-time simulation. First, the boost converter was mathematically modeled, and then the electronic components were designed to meet the operation requirements. Subsequently, a model-based predictive controller (MBPC) and a digital PI (Proportional–Integral) controller were designed, and their performance was compared using MATLAB/SIMULINK®. The controls were further verified by implementing test benches based on an FPGA (Field-Programmable Gate Array) with an OPAL-RT real-time simulator, which included the RT-LAB and RT-eFPGAsim simulation packages. These tests were successfully carried out, and the methodology used for this design was validated. The results showed a better response obtained with MBPC, both in terms of stabilization time and lower overvoltage. [ABSTRACT FROM AUTHOR]

The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model framework was proposed by integrating the finite-element method (FEM) and machine learning (ML), in which the basic methodology involved interrupting the computational workflow of the FEM and reassembling it with ML. Specifically, the displacement field, as the primary unknown quantity to be solved using the FEM, was mapped onto the displacement boundary conditions of the tube component with ML. To this end, the titanium tube material as well as the hydroforming process was investigated, and a fairly accurate FEM model was developed based on the CPB06 yield criterion coupled with a simplified Kim–Tuan hardening model. Numerous FEM simulations were performed by varying the loading conditions to generate the training database for ML. Then, a random forest algorithm was applied and trained to develop the surrogate model, in which the grid search method was employed to obtain the optimal combination of the hyperparameters. Sequentially, the principal strain, the effective strain/stress, as well as the wall thickness was derived according to continuum mechanics theories. Although further improvements were required in certain aspects, the developed FEM-ML surrogate model delivered extraordinary accuracy and instantaneity in reproducing multi-physical fields, especially the displacement field and wall-thickness distribution, manifesting its feasibility in the real-time, full-field simulation and monitoring of deformation states. [ABSTRACT FROM AUTHOR]

Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor‐made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. Practitioner Points: Various methods can be used for automated data transfer between the physical system and a digital twin.Delays in the data transfer differ depending on implementation method.The digital twin objective determines the required simulation frequency.Implementation method should be chosen based on the required simulation frequency. [ABSTRACT FROM AUTHOR]

We present a novel method for modeling adaptive movement behavior in force-based crowd simulations tailored to the requirements of interactive real-time applications. We aim for an efficient, controllable and situation-dependent synthesis of global path planning and local collision avoidance. To achieve this, we address two typical problems that can cause severe performance impacts and undesired motion: local neighbor search and the combination of goal directions with collision avoidance. We employ a direction-aware neighbor set to efficiently select agents for collision avoidance computation and crowd density estimation. We adaptively blend between goal and avoidance directions to utilize the entire spectrum of navigation strategies for goal attainment, ranging from completely goal-oriented to strongly avoidance-centric behaviors. Our method can be combined with established force-based simulation techniques and easily integrates into existing real-time simulation pipelines. Experiments show that our method can improve control over run-time performance as well as the movement behavior of agents compared to plain force-based methods. [ABSTRACT FROM AUTHOR]

Finite-set model predictive control (FS-MPC) is an easy and intuitive control technique. However, parametric uncertainties reduce the accuracy of the prediction. Classical MPC requires many calculations; therefore, the calculation time generates a considerable time delay in the actuation. This delay deteriorates the performance of the system and generates a significant current ripple. This paper proposes a finite-set integral sliding modes predictive control (FS-ISMPC) for a permanent magnet synchronous motor (PMSM). The conventional decision function is replaced by an integral sliding cost function, which has several advantages, such as robustness to parameter uncertainties, and convergence in finite time. The proposed decision function does not require the inductance and resistance parameters of the motor. In addition, the proposal includes compensation for the calculation delay of the control vector. The proposed control strategy was compared with traditional predictive control with delay compensation using a real-time hardware-in-the-loop (HIL) simulation. The results obtained from the comparison indicated that the proposed controller has a lower THD and computational burden. [ABSTRACT FROM AUTHOR]

As occurs with many other topics of pain curricula, lectures on pain neurophysiology are predominantly theoretical, and the practical experiences required to deliver that content to learners can be problematic regarding sustainability, logistics, and funding. Therefore, this study presents the development and testing of a microcontroller-based interactive tool to assist university-level pain neurophysiology teaching and facilitate a hands-on approach to exploring the functional connectivity of pain pathways. The proposed system consists of a sensing module that conveys electrical signals elicited by thermal and mechanical stimuli to an Arduino Nano board. The latter acts as a processing module and contains a codified version of a Neuroid-based network model of the pain circuitry. Our prototype was tested during a lecture on pain neurophysiology attended by forty-seven undergraduate health sciences students, who were instructed to observe the neural responses to sensory stimulation while altering the pain circuitry’s excitatory-inhibitory balance. We collected participants’ opinions via a 3-point Likert-type scale with seventeen questions regarding the tool’s composition, appropriateness, and theoretical foundation. Besides allowing students to explore the functional connectivity of pain pathways without the limitations posed by in-vivo or in-vitro models, the proposed tool achieved overall positive feedback among the surveyed.

This paper introduces a Fuzzy Logic Controller designed for an on-load tap changer within medium voltage distribution systems with bulk penetration of Distributed Energy Resources. As the on-load tap changer remains one of the most essential forms of voltage regulation in medium voltage distribution networks, improving its operation is a cost-effective response to the emerging voltage violations caused by intermittent generation during the early stages of the energy system transformation. Software-in-the-loop simulations were conducted to validate the effectiveness of the proposed algorithm compared to the conventional methods. A modified CIGRE Medium Voltage Distribution Network Benchmark in European Configuration was modelled while the controller code developed in Python 3.12 was running on a PC, both coupled in a real-time closed-loop environment. The analyses showed that the proposed algorithm managed to reduce overvoltage from 7.02% to 4.85% in the benchmark network, thus demonstrating that the algorithm is efficient and ready for on-field implementation. [ABSTRACT FROM AUTHOR]

The Modular Multilevel Converter (MMC) is a promising converter for medium-/high voltage applications due to its various features. The waveform quality could be enhanced further by expanding the number of generated voltage levels, which increases the number of submodules (SMs); however, this improvement enlarges the size and cost of the converter, posing a persistent challenge. Hence, there exists a trade-off between power quality and the size and complexity of the converter. To verify the performance of such a complex converter and to validate the effectiveness of the control system, especially in the absence of a physical system, Real-Time (RT) simulation becomes crucial. However, the large number of components of a MMC creates important numerical challenges and computational difficulties in RT simulation. This paper proposes a grid-connected MMC employing a Z Packed U-Cell converter as a SM to generate a higher number of voltage levels while minimizing the required number of SMs. The ZPUC-MMC is implemented on an FPGA-based RT simulation platform using Electric Hardware Solver to reduce computational burden and simulation time, while improving the accuracy of the obtained results. Conventional controllers of MMCs are applied to assess the effectiveness and robustness of the proposed system during steady-state and dynamic operations. [ABSTRACT FROM AUTHOR]

Thermal management is one of the key factors affecting the performance of hybrid vehicles. However, most traditional energy management strategies lack the consideration of thermal constraints under harsh operating conditions. In this paper, taking a specific type of diesel-electric hybrid system as the research object, a strategy for energy redistribution under thermal management constraints is proposed. The diesel-electric hybrid system model is built and verified. Based on the model, the cooling capacity indexes are selected, and the weight coefficient of each cooling capacity index is determined by neighborhood component analysis. Then a comprehensive thermal evaluation system is obtained. Combined with the thermal evaluation system, the energy redistribution strategy based on the momentum gradient descent method is proposed. Two typical working conditions of high altitude and battery cooling system deterioration are selected, and the energy redistribution strategy is simulated and analyzed in real-time under both working conditions. The results show that the energy redistribution strategy can significantly improve the thermal state of the system at the expense of less overall energy consumption, take into account the economy and thermal balance, and ensure the reliable operation of the vehicle. [ABSTRACT FROM AUTHOR]

The increasing integration of grid-connected photovoltaic (PV) inverters and inverter-based resource (IBR) systems into the power grid emphasizes the critical need for standardized procedures to ensure their reliability and effective grid support functions. This research is driven by the gap in standardized testing methodologies for smart inverters, which are pivotal for the stability and quality of power in distributed energy systems. We used a Power Hardware-in-the-Loop (PHIL) laboratory setup to conduct a comprehensive analysis of smart inverters within a simulated real-world grid environment. Our approach integrates a Distributed Energy Resource Management System (DERMS) with PHIL testing to evaluate the smart inverter's performance across various operational modes. The detailed test protocols mimic real-world grid conditions, enabling the examination of the inverter's dynamic response to grid disturbances, control strategies, and communication protocols. The primary aim of this study is to rigorously test and validate the primary functions of smart inverters, focusing on their impact on overall grid stability and power quality management. This includes advanced features like Volt–VAR, Volt–Watt, dynamic power factor control, and the seamless integration of smart inverters into DERMSs and Advanced Distribution Management Systems (ADMSs). Furthermore, we aim to bridge the current gap in standardized testing procedures, contributing to the establishment of robust standards and operating protocols for smart inverter integration into the grid. [ABSTRACT FROM AUTHOR]

Liquid crystal elastomers (LCEs) with photo‐responsive properties, typically driven by either photochemical or photothermal mechanisms, have found extensive applications as, for example, actuators in soft robots. However, intricate temperature‐dependent viscoelasticity of LCEs poses a challenge, leading to a notable gap in the domain of dynamic models for photothermal‐responsive LCE (PTR‐LCE) fibers. Here, a fundamental framework is proposed for accurate modeling and real‐time simulations of PTR‐LCE fiber dynamics. The PTR‐LCE fiber is described as a one‐dimensional (1D) string model that decomposes the fiber deformation into active and passive parts, which are characterized by an order parameter and a temperature‐dependent linear viscoelasticity model, respectively. Then, independent experimental measurements of model parameters are conducted, and a numerical algorithm is developed to solve the model, which is validated for convergence, time efficiency, and accuracy. Finally, the model is employed to simulate both open‐loop and Proportional‐Integral‐Derivative (PID) control of actuators made of PTR‐LCE fibers. The results confirm the advantages of this model over previous models. This work not only reveals the physical mechanisms underlying the PTR‐LCE fiber dynamic behaviors but also provides inspirations for more efficient and precise soft robotic applications. [ABSTRACT FROM AUTHOR]

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST's equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model's accuracy and the MHM's accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach. [ABSTRACT FROM AUTHOR]

This article introduces a new approach for validating directional overcurrent protection schemes in ring-topology electrical distribution systems with distributed energy resources (DERs). The proposed protection scheme incorporates overcurrent and directional functions and addresses DER-induced challenges such as variable short circuit levels. This study employs real-time and offline simulations to evaluate the performance of the protection coordination scheme using a digital twin under DER-supplied loads. The utilization of digital twins offers the possibility to simulate different scenarios, providing real-time responses to dynamic changes and allowing for informed decision-making in response to disturbances or faults. This study aims to present a new approach to validate the performance of the proposed protection scheme when the load is entirely supplied by DERs, highlighting issues such as false trips and protection system blindness resulting from changes in short circuit currents. The results show a breakdown in the coordination of the protection scheme during the fault conditions, demonstrating the effectiveness of digital twins in validating the protection scheme's performance. Performing an analysis in the electromagnetic transient (EMT) domain improves the validation and refines the results. [ABSTRACT FROM AUTHOR]

The impact of communication disturbances on microgrids (MGs) needs robust and scalable Information Communication Technology (ICT) infrastructure for efficient MG control. This work builds on advances in the Internet of Things (IoT) to provide a practical platform for testing the impact of various cyber-attacks on a distributed control scheme for a Multi-Agent System (MAS). This paper presents a Controller Hardware-in-the-Loop (CHIL) testbed to investigate the impact of various cyber-attacks and communication disruptions on MGs. A distributed consensus secondary control scheme for a MAS within an MG cyber-physical system (CPS) is proposed. The proposed cyber-physical testbed integrates a real-time islanded AC microgrid on RT-Lab, secondary controllers implemented on single-board computers, and an attacker agent on another single-board computer. Communication occurs via a UDP/IP network between OPAL-RT and controller agents, as well as between the agents. Through meticulous experimentation, the efficacy of the proposed control strategy using the developed platform is validated. Various attacks were modeled and launched including deception attacks on sensors, actuators, and their combinations, as well as disruption attacks. The ramifications of both deception and disruption cyber-attacks on system performance are analyzed. [ABSTRACT FROM AUTHOR]

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

Today’s power system is transforming into an increasingly complex entity, consisting of numerous components, such as transmission lines, controllable loads, and especially different types of distributed renewable energy sources (DERs). With the growing integration of DERs into the grid, multiple operational challenges arise, including overvoltage, undervoltage, or increased energy losses. Resolving these issues demands the implementation of both advanced and effective control strategies. As the dynamic power system evolves by incorporating new technologies, these control strategies need to consider other different technical aspects, such as communication protocols or real-time considerations. Additionally, the rise of smart metering devices has transformed conventional power systems into cyber-physical power systems (CPPS), which can integrate the advanced control strategies into the cyber layer. Given the operating challenges and the integration of diverse technologies, it is proposed that a CPPS testbed platform constitutes an ideal solution for developing and validating technologies in future smart grids. For this purpose, this paper introduces a co-simulation framework for implementing a CPPS testbed, utilising the real-time simulator, Typhoon HIL, within a laboratory environment. Additionally, it presents a proposed optimal power flow (OPF) control strategy that emphasises two key objectives, minimisation of operating costs and power network loss. The investigation is illustrated by a modified version of IEEE 39-bus test system with the high integration of DERs. The findings indicate that adopting a CPPS testbed can be advantageous for implementing real-time research on monitoring and control in a wide area network.

This paper presents an approach and a corresponding laboratory infrastructure for multi-hardware-in-the-loop (M-HiL) testing of power converters and intelligent electronic devices (IEDs) with wide area features for power system monitoring and control. The basis for the testing environment is a digital real-time simulation (RTS) platform which can be utilized for phasor-based or hybrid phasor and electromagnetic transient (EMT) power system simulation. A high-bandwidth power amplifier is used for interfacing the real-time simulated power system to the power hardware. The necessary elements and their interfaces required for realizing the M-HiL testing are highlighted in the paper. Finally, a complete example of an M-HiL test based on real-time simulation of the Nordic 44 (N44)-bus power system model is presented. The test setup includes two phasor measurement units (PMUs) with their corresponding communication layers and a converter configuration for scaled emulation of the HVDC transmission system of an offshore wind farm. The configuration also includes a grid forming converter unit operated in parallel to the grid-side HVDC terminal. The presented test demonstrates how a grid forming converter operated as a Virtual Synchronous Machine (VSM) can provide frequency support to the Nordic power system and how this support would be recorded by the PMUs.

This paper develops a real-time (RT) transmission and distribution (T&D) co-simulation platform for testing distributed energy resources management system (DERMS) algorithms. The platform consists of a transmission system modeled within a real-time transient-stability type environment interfaced to an active distribution network modeled within a fundamental frequency phasor-domain platform. The data exchange and time synchronization between the T&D models has been established via MQTT communication protocol, enabling the platform to communicate with a large number of DER models in real-time. The developed platform is generic in the sense that it can integrate an arbitrarily sized transient stability-type model of a transmission system with an arbitrarily sized fundamental frequency phasor-domain model of a distribution system. The platform does not impose an inherent limit on the size of the T&D models, and the size is limited by the computational capability of the simulation computer. This feature enables utilizing the platform for simulation studies of large-scale T&D systems. Furthermore, the developed platform enables both off-line and real-time simulations, extending its application from off-line planning-type studies to real-time operation-type and hardware-in-the-loop (HIL) studies. The paper demonstrates the application of the developed platform in a large-scale case study utilizing behind-the-meter (BTM) DERs to provide grid services when controlled by a DERMS. By including the propagation of dynamics between the simulation domains, realistic DER models, and typical communication protocols that are used in the field, the proposed platform enables the testing of DERMS algorithms in a close to real environment, replicating a wide range of power system phenomena that highlight the impact of a DERMS in an actual power system.

Design and selection of advanced protection schemes have become essential for reliable and secure operation of networked microgrids. Various protection schemes that allow correct operation of microgrids have been proposed for individual systems in different topologies and connections. Nevertheless, protection schemes for networked microgrids are still in development, and further research is required to design and operate advanced protection in interconnected systems. Interconnection of these microgrids in different nodes with various interconnection technologies increases fault occurrence and complicates protection operation. This paper aims to point out challenges in developing protection for networked microgrids, potential solutions, and research areas that need to be addressed for their development. First, this article presents a systematic analysis of different microgrid clusters proposed since 2016, including several architectures of networked microgrids, operation modes, components, and utilization of renewable sources, which have not been widely explored in previous review papers. Second, the paper presents a discussion on protection systems currently available for microgrid clusters, current challenges, and solutions that have been proposed for these systems. Finally, it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation.

To safely integrate large numbers of wind turbines into the electric power grid, turbines must demonstrate compliance with grid codes and technical guidelines through rigorous testing. Such tests are traditionally performed using prototype turbines on designated measurement sites. So-called field tests offer little flexibility and repeatability, as factors such as wind and grid conditions cannot be adjusted at will. Recently, specialized laboratory test facilities were established to augment or completely replace conventional field tests. These facilities need to constantly grow in size, power, and functionality to keep up with wind turbine trends, while new and increasingly complex grid requirements necessitate more advanced testing methodologies. This paper presents four testing concepts and how they can be further integrated into the development and certification process of wind turbines using the laboratories of Fraunhofer IWES as an example. In particular, turbine, nacelle, subsystem, and component testing are reviewed and their advantages and anticipated challenges for evaluating grid code compliance are highlighted. Experiences gained over the past decade from testing wind turbines and constructing experimental testing facilities are shared, including key data and capabilities of the developed test benches. The authors expect the presented approach to be used for validating simulation models that can be embedded into larger interaction and stability studies of wind farms at a later stage. Such an integrated testing approach may lead to quicker and more flexible design and optimization of new generations of wind turbines, effectively increasing the speed of grid integration and ensuring safe and reliable grid operation.

Power-Hardware-in-the-Loop (PHIL) is a kind of real-time simulation, capable of exchanging not just low-voltage, low current signals, but the power required by the power device under test (PDuT). PHIL requires a PDuT to be connected to a real-time digital power network simulator via a power interface (PI). There have been quite a few PIs proposed in the past. Among them, the ideal transformer model (ITM) is the most commonly used due to its ease of implementation. Other PIs such as partial circuit duplication and damping impedance can be considered as an extended version of the ITM. These PIs need to follow a strict impedance ratio between PDuT and the rest of the system prior to the PHIL implementation, which could be a tedious and difficult task. This paper proposed a new PI for PHIL based on multi-dimensional golden section search algorithm, which can eliminate such a constraint. The proposed method has been shown to have wider stability regions when PDuT is a passive device or active one such as an inverter based resource. Moreover, dynamic responses of the proposed method are similar to those of the ITM under stable conditions. The validity of the proposed method has been justified with offline simulation and experimental PHIL setups.

This paper presents the modeling, control and evaluation in a real-time simulation (RTS) of a three-phase multilevel inverter based on the modular multilevel converter (MMC) topology. The developed model for MMC includes four decoupled state variables per phase, which are instrumental for the control design, namely injected (output) current, circulating current, total energy, and energy balance between arms. Based on this model, a control scheme is proposed with the aim to regulate and balance the total energy on each converter’s phase, regulate the circulating currents, and inject a three-phase current synchronized with the grid voltage. As part of the control process, the proposed controller generates the reference for a modulation scheme to obtain the switching sequence for each converter’s cell, which, in this case, is the phase-shifted carrier-based pulse-width modulation (PSC-PWM). As it was already reported in the literature, this particular modulation guarantees self (natural) balance of all capacitor voltages, i.e., they converge to the same steady-state average value without the need of any external balancing controller.

Green hydrogen production is a sustainable energy solution with great potential, offering advantages such as adaptability, storage capacity and ease of transport. However, there are challenges such as high energy consumption, production costs, demand and regulation, which hinder its large-scale adoption. This study explores the role of simulation models in optimizing the technical and economic aspects of green hydrogen production. The proposed system, which integrates photovoltaic and energy storage technologies, significantly reduces the grid dependency of the electrolyzer, achieving an energy self-consumption of 64 kWh per kilogram of hydrogen produced. By replacing the high-fidelity model of the electrolyzer with a reduced-order model, it is possible to minimize the computational effort and simulation times for different step configurations. These findings offer relevant information to improve the economic viability and energy efficiency in green hydrogen production. This facilitates decision-making at a local level by implementing strategies to achieve a sustainable energy transition.

The importance of simulation at the machine level in industrial environments is steadily increasing especially in the design and commissioning phase. Using models during the operation phase together with the real machine or plant is referred to as online simulation. Online simulation is used for system monitoring, predictive analyses, decision support, or online optimization and therefore has various advantages and a wide field of applications. This paper aims to characterize online simulation at the machine level in industrial automation focusing on key technologies and common applications. Therefore, a set of 65 relevant publications, which are focusing on this subject, is found by database search, expert consultation, and snowballing. As key technological aspects, the used model types, interfaces, and platforms, and the aspects of initialization and synchronization are further investigated. The results are interpreted and limitations, knowledge gaps, and future prospects are discussed. The potential of online simulation at the machine level especially arises due to the increasing availability of component and machine models from the design and commissioning phase, which can be reused for online simulation. The remaining challenges are identified concerning implementation, simulation platforms, model maintenance, and especially in the field of synchronization. [ABSTRACT FROM AUTHOR]

The investigation of hippocampal traveling waves has gained significant importance in comprehending and treating neural disorders such as epilepsy, as well as unraveling the neural mechanisms underlying memory and cognition. Recently, it has been discovered through both in vivo and in vitro experiments that hippocampal traveling waves are typically characterized by the coexistence of fast and slow waves. However, electrophysiological experiments face limitations in terms of cost, reproducibility, and ethical considerations, which hinder the exploration of the mechanisms behind these traveling waves. Model-based real-time virtual simulations can serve as a reliable alternative to pre-experiments on hippocampal preparations. In this paper, we propose a real-time simulation method for traveling waves of electric field conduction on a 2D plane by implementing a hippocampal network model on a multi-core parallel embedded computing platform (MPEP). A numerical model, reproducing both NMDA-dependent fast waves and Ca-dependent slow waves, is optimized for deployment on this platform. A multi-core parallel scheduling policy is employed to address the conflict between model complexity and limited physical resources. With the support of a graphical user interface (GUI), users can rapidly construct large-scale models and monitor the progress of real simulations. Experimental results using MPEP with four computing boards and one routing board demonstrate that a hippocampal network with a 200 × 16 pyramidal neuron array can execute real-time generation of both fast and slow traveling waves with total power consumption below 500 mW. This study presents a real-time virtual simulation strategy as an efficient alternative to electrophysiological experiments for future research on hippocampal traveling waves. [ABSTRACT FROM AUTHOR]