Search

This paper investigates the problem of designing an observer-based event-triggered H ∞ controller for a Hamiltonian system with delays incorporated in the underlying network. As our contributions, we first propose an event-triggered scheme which uses the Hamiltonian to decide whether to trigger the event generator at the sampling time. Additionally, when states are not exactly known globally asymptotically stable, we proceed to design an observer-based controller with which the resulting closed-loop system can be transformed into a time-delay Hamiltonian system. Based on the structural characteristic of the Hamiltonian systems, sufficient conditions are given to guarantee the closed-loop system to achieve the H ∞ performance index with external disturbances in available and unavailable states, respectively. Finally, multi-machine power systems as simulation examples are illustrated to validate our proposed results. • Event-triggered H ∞ control is discussed via a Hamiltonian approach. • A discrete event-triggered scheme is proposed for saving communication resources. • An observer is designed for networked Hamiltonian systems with unknown states. • The influence of network-induced delay is considered. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cells have become one of the most viable power sources for electric aircraft. Models representing the electrical behavior of the fuel cell stack over the full dynamic operation region are essential for the development of power electronic energy systems powered by fuel cell stacks. This work presents an electrical equivalent circuit model for PEM fuel cell stacks representing the static and dynamic electrical behavior of the fuel cell stack under pulsed loads up to frequencies of 10 kHz. Dynamic phenomena on time scales slower than the considered timescale of power-electronic switching, such as reactant flow, membrane hydration, and temperature effects, are considered stationary. The parameterization method proposed is developed on measured data from a 110 W PEM fuel cell stack and validated with a set of measured data from a 2 kW stack. The time-domain simulated behavior of the parameterized model shows an accurate representation of the measured behavior: the parameterized model reproduces both the static polarization behavior and the behavior under high-frequency pulsed loading with errors of less than 1% with respect to the nominal stack voltage. The model is suitable for dynamic simulation of power electronic systems directly connected to fuel cell stacks and can be parameterized without special electrochemical impedance spectroscopy measurements. • A novel equivalent circuit model for PEM fuel cells is proposed. • The equivalent circuit model is valid in time domain for large-signal perturbations. • The model represents the dynamic electrical behavior under pulsed loads up to 10 kHz. • The proposed model is parameterized using a novel pulsed load measurement method. • The model is aimed for highly dynamic simulations of fuel cells in pHiL environments. [ABSTRACT FROM AUTHOR]

This study proposes a simulation model with practical constraints on the use of diesel engine and investigates the impact of adding the constraint of the minimum length of diesel operation time on total cost of hybrid power system, which is designed to supply power for a small village located in the east part of Iran. The results indicated that in the presence of the aforementioned constraint, total cost of hybrid power system increased by shortening diesel generator lifespan and growing diesel fuel consumption. The research did not find any specific uptrend or downtrend in battery lifetime by adding the mentioned constraint into the model. The study outcomes also specified that the surplus power of the hybrid power system inclined in the existence of the minimum length of diesel operation time constraint. The results demonstrated that one hour increment in the minimum length of diesel operation time left a remarkable part of generated renewable power as a surplus power. [ABSTRACT FROM AUTHOR]

Simulation of sizeable hydro-thermal power systems, such as Northern Europe or larger, requires several extensive simplifications and model reductions to decrease simulation time. Such reductions for hydrosystems are often called Equivalent models. Their purpose is to mimic a more detailed hydropower model while decreasing computation time. Both aspects are vital for accurate and useable simulation results. Here, different Equivalent models for hydropower have been developed together with a new function for adaptive Equivalent inflow based on local inflows to the detailed system. The models were computed via a bilevel optimization problem factoring in the novel adaptive inflow. Based on this, the new function for adaptive inflow was calculated using regression. The Equivalents have then been evaluated in a case study of hydropower systems in Northern Sweden regarding accuracy in hourly and total power generation, revenue estimation, and relative computation time. For all Equivalents, the computation time is decreased by ¿96%. Further, the Equivalents demonstrate improved performances in hourly and total power production and revenue estimations. The best hourly power difference was 9.2%, and the best revenue estimation was 5.9%. Especially notable is the low total power production difference of ¡0.5% compared to the more detailed model. [ABSTRACT FROM AUTHOR]

This paper provides a standardised methodology to assess the suitability of using stand-alone solar photovoltaic (PV) systems for different productive uses of energy (PUE) at a country-wide level. The focus has been on the country of Tanzania, but the methodology is suitable for any location. This analysis has reviewed fourteen different PUE categories, with several sub-categories. The PUE were assessed for a wide range of factors, including cost of the system, potential income from the PUE, and potential market size. This report also highlights the PUE load profiles and energy requirements for comparison. Assessing a potential productive use is a complex process with multiple stakeholders and decision factors. The analysis here is not meant as a definitive answer, but to highlight the technical characteristics and economics of the potential productive uses for comparison. This methodology is a first pass method to assess the suitability of stand-alone PV systems for PUE applications at a country-wide level. For more detailed analysis of the PUE potential, the enabling environment, socio-cultural context and supporting services must be investigated in much greater detail. The quantitative methodology given here used computer-based simulation tools, including data processing (Excel & Python), geographical information systems (QGIS) and power system simulation (HOMER Pro). Input data from multiple sources, including in-country fieldwork, was used. This analysis has highlighted PUE with smaller energy requirements but low sensitivity to equipment or service cost to be the most suitable for more detailed analysis. Applying this methodology to Tanzania has shown stand-alone solar PV systems for barbershops, tailors, mobile carpenters, drip-feed irrigation systems and fishing lights as the lowest risk PUE for implementation. • Covers a standardised methodology for assessing and comparing productive uses of energy for remote rural applications. • Applies to remote stand-alone solar PV systems in Tanzania. • The methodology is a first pass assessment highlighting the most suitable productive uses of energy for future development. • Energy requirements, cost of system, potential market size and potential range of profit are compared for 25 use cases. • Drip-fed irrigations systems have the largest market potential and profitability in Tanzania. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data-based adaptive predictive frequency control method for multi-area power systems with unknown and time-varying inertia. Firstly, a data-based representation is built and updated at each instant based on behavioural system theory by using historical input–output data, where a moving horizon estimation method is used to deal with the unknown time-varying inertia issue adaptively. Then, the optimal frequency control signal is computed by solving an optimization problem under the framework of data-based predictive control. Simulation results on a power system with three control areas demonstrate the effectiveness of the proposed method. • A novel data-driven control framework that does not require offline training is proposed. • The data-based system representation is updated adaptively based on moving horizon estimation to allow for the time-varying inertia. [ABSTRACT FROM AUTHOR]

The fast growth of renewable energies increases the power congestion risk. To address this issue, the French Transmission System Operator (RTE) has developed closed-loop controllers to handle congestion. RTE wishes to estimate the probability that the controllers ensure the equipment's safety to guarantee their proper functioning. The naive approach to estimating this probability relies on simulating many randomly drawn scenarios and then using all the outcomes to build a confidence interval around the probability. Although theory ensures convergence, the computational cost of power system simulations makes such a process intractable. The present paper aims to propose a faster process using machine-learning-based proxies. The amount of required simulations is significantly reduced thanks to an accuracy-aware proxy built with Multivariate Gaussian Processes. However, using a proxy instead of the simulator adds uncertainty to the outcomes. An adaptation of the Central Limit Theorem is thus proposed to include the uncertainty of the outcomes predicted with the proxy into the confidence interval. As a case study, we designed a simple simulator that was tested on a small network. Results show that the proxy learns to approximate the simulator's answer accurately, allowing a significant time gain for the machine-learning-based process. • Energetic transition enhances renewable energies deployment. • Fast growth of renewable energies increases the power congestion risk. • Zonal controllers are deployed in the network to handle congestions. • Need to certify the security properties of the zonal controller. • Use of machine-learning-based proxy to speed up the certification. [ABSTRACT FROM AUTHOR]

To analyze the voltage sag characteristics of distribution networks, automotive companies need an accurate and efficient non-stationary signal analysis tool to extract voltage sag feature information from power quality monitors and fault recorders, which is crucial for automotive companies to analyze grid characteristics and improve localized charging strategies. We innovatively propose a hybrid decomposition method to decompose voltage sag waveforms from composite disturbance data. Subsequently, we design a context-aware mechanism to adapt the proposed decomposition method to the complex power quality data in different regions. In addition, tests were conducted on an integrated power quality simulation system to verify that this method can accurately decompose the voltage sag waveform from the composite disturbance signals. Finally, the experimental simulation results also show the better performance of the method in decomposing voltage sag waveforms with different causes in different composite disturbance signals compared with other mainstream methods. • This technique can decompose sag waveform from various composite disturbance signals. • Design a context-aware mechanism to improve adaptability of the proposed method. • Tested the impact of different parameters of voltage sag on electric vehicles. • The proposed method is effective even in different noisy environments. • Designed a hardware platform to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

• Providing a comprehensive and systematic review of existing modelling approaches of ESS. • Analysing the application cases of ESSs based on their characteristics. • Evaluating the methods of participation of ESSs in multi-timescale simulation from the perspectives of multirate simulation and co-simulation. Due to the intermittent nature of renewable energy sources, modern power systems face great challenges across generation, network and demand side. Energy storage systems are recognised as indispensable technologies due to their energy time shift ability and diverse range of technologies, enabling them to effectively cope with these changes. However, the multi-timescale dynamics of the energy storage system that differs from the traditional synchronous generators results in the challenges for the accurate and efficient simulation for the power system with multiple energy storage systems. The purpose of this study is to investigate potential solutions for the modelling and simulation of the energy storage system as a part of power system by comprehensively reviewing the state-of-the-art technology in energy storage system modelling methods and power system simulation methods. The selection principles for diverse timescales models of the various energy storage system models to solve different analysis of the power system with energy storage systems are discussed. The implementation methods for existing solutions to multi-timescale simulation enabling effective analysis of behaviours resulting for the coupling of multiple timescales of the power system are also introduced and their potential applications on analysing the dynamic features of the power system with multiple energy storage systems are also discussed. [ABSTRACT FROM AUTHOR]

In this paper we present a scalable approach based on the latency-based linear multistep compound method to use graphic processing unit (GPU) to accelerate the power system simulations with electromagnetic transients. To balance between the computational load and accuracy, we modeled the power system using shifted frequency analysis. Then, to efficiently exploit the hardware, our computational approach is designed to utilize both data-parallel and task-parallel execution. In addition, a graph-based thread safety design is introduced to achieve high scalability in the paralleled component computations. Furthermore, our implementation introduces a translation layer for different heterogeneous computing frameworks during compile time, so that a wide range of GPU devices can be supported without losing performance. Finally, benchmark results show that our approach achieves faster-than-real-time capability for systems with hundreds to thousands of nodes. [Display omitted] • Exploits data- and task-parallelism together in an automated way. • A graph-based approach to ensure thread safety to increase scalability. • A compile-time translation layer to allow execution on various platforms. • Faster-than-real-time for system with more than 10,000 dynamic components. • 82 × faster compared to an optimized sequential simulation. [ABSTRACT FROM AUTHOR]

The paper investigates secure filtering of nonlinear large-scale systems suffering from randomly occurring DoS attacks. By introducing an adjustable parameter, an adaptive event-triggering mechanism is proposed for the sake of decreasing the transmission burden of signals, where the memory is utilized to reflect the influence of past triggered information. The main objective is to design an event-based secure filter to ensure that the dynamics of filtering errors is input-to-state stable in the mean square. Using the constructed Lyapunov function, a sufficient condition is derived where some element matrix inequalities are utilized to handle the inherent coupling of subsystems. Furthermore, the desired filter gains are parameterized by resorting to the feasibility of matrix inequalities. Finally, a numerical simulation about a power system is provided to verify the effectiveness of the developed secure filtering algorithm. • An adaptive event-triggered scheme with memory mechanisms is proposed. • Sufficient conditions are obtained to achieve the input-to-state stability. • The filter parameters are acquired by virtue of the matrix decoupling technique. [ABSTRACT FROM AUTHOR]

In this article, the solar hydrogen storage is modeled and hourly investigated with TRNSYS software. The Photovoltaic (PV) panel is employed for green power generation that is consumed in the electrolyzer subsystem and produced hydrogen. Additionally, the required electricity at the lack of enough solar irradiation is supplied from the grid. The performance of the system is comparatively analyzed for three main cities. Results show that the maximum power generation by PV panel is about 1670 kW in June which approximately is the same for two cities. The energy and Faraday efficiency of electrolyzer changes between 0.85-0.89 and 0.89–0.92 respectively. The amount of hydrogen production reaches 1235 m3/h for one of them in May. The total amount of hydrogen production is 13,181 m3/year in Yazd, 13,143 m3/year in hot city, and 13,141 m3/year in most populated city. • Transient simulation of power to hydrogen system using TRNSYS. • Influence of various places on hydrogen production rate. • Designing a hydrogen storage system with a solar energy source. • Analyzing the performance of the electrolyzer from an energy viewpoint. [ABSTRACT FROM AUTHOR]

Virtual power plants need to build awareness, transmission and application of information flow model, require multiple levels between virtual power plant, the virtual power plant each participation main body, regulation and control of virtual power plant and quantification of communication information transmission between the main constraints, this paper studies the distributed resources polymerization technology, the modeling and simulation of mechanical and electrical characteristics of virtual power plant communication network transmission characteristics of digital simulation technology, This paper proposes a simulation method of diversified resource aggregation regulation based on virtual group structure. By collecting the electrical characteristic change curve of each distributed unit, and stacking the transmission characteristics of communication network, digital simulation aggregation is carried out to obtain the digital model of cooperative operation and control characteristics of virtual power plant "information flow + energy flow". Through this model will be connected to Matlab/Simulink power system simulation environment for power regulation and operation simulation verification. [ABSTRACT FROM AUTHOR]

The escalating integration of renewable energy sources necessitates enhanced power system flexibility. Gas units, with their rapid start-stop capabilities, emerge as crucial assets for system operators grappling with supply-demand fluctuations. This paper proposes a novel multi-stage fully adaptive distributionally robust unit commitment (MFA-DRUC) model to optimize the operation of these flexible units under the uncertainties inherent in real-time dispatch. Leveraging the Wasserstein metric, our approach significantly expands the feasible solution space compared to traditional multi-stage adaptive unit commitment (MA-DRUC) models, bolstering resilience against extreme scenarios. To overcome the computational challenges posed by the model's multi-stage structure, we introduce a mixed approximation rule (MAR) that effectively handles high-dimensional variables and strong coupling characteristics. By employing duality theory, we transform the unit commitment (UC) problem into a computationally tractable mixed-integer linear programming problem. Comprehensive simulations across power systems of varying scales, encompassing scenarios such as coal-fired unit decommissioning and gas unit integration, validate the efficacy of our proposed MFA-DRUC model. These results underscore its potential to enhance the reliability and efficiency of power systems navigating the complexities of a renewables-driven future. • A multi-stage unit commitment model considering the sequential decision-making characteristics of practical power systems is developed. • The start-up and shutdown of gas-fired units during intra-day rescheduling are incorporated to provide a larger feasible region. • A mixed approximation rule based on nonlinear lifting operators is proposed to enhance solution accuracy for the high-dimensional variables and strong coupling arising from the multi-stage structure. • A distributionally robust unit commitment model is constructed using the Wasserstein metric to address the uncertainty of renewable energy. • Leveraging optimization theory, the unit commitment model is reformulated as a mixed-integer linear programming model. [ABSTRACT FROM AUTHOR]

This paper studies the frequency fluctuation problems of a multi-area power system with renewable energy sources (RESs) under denial-of-service (DoS) attacks. (i) Due to the decoupling of the RESs from power system using electronic devices, the RESs have no natural frequency response capability, which result in decreasing of entire power system equivalent inertia with large penetration of RESs. (ii) DoS attacks cause the insecurity of network communication, which can vanish data transmission and yield data packet dropout. To address the above issues, a virtual synchronous generator (VSG) incorporated with load frequency control (LFC) method (VSG-LFC) is proposed by introducing a hybrid-triggered mechanism (HTM) to (i) reduce the system sensitivity to the penetration level of RESs; (ii) compromise between frequency regulation performance and the reasonable usage of network resource under unsecured communication environments. Then, a switched system model is constructed considering non-identical DoS attack situations. Furthermore, theoretical analyses using Lyapunov functional related methods are carry out for asymptotical mean-square stability with H ∞ performance on frequency regulation. Simulation evaluations of power system verify the effectiveness of this development. It shows that in a two-area power system frequency derivation performance indicators (IAE, ITAT, ISE) in A r e a 1 are reduced by (82.996%, 86.273%, 85.823%) and in A r e a 2 are reduces by (77.996%, 83.613%, 82.315%). • VSG-LFC is proposed to reduce the sensitivity to the penetration level of RESs. • An HTM is proposed to balance control performance and network utilization. • A VSG-LFC power system under DoS attack is reconstructed as a switched system. [ABSTRACT FROM AUTHOR]

This work proposes a convex methodology that uses power system measurements (power generation and total demand) to accurately estimate transmission loss coefficients (B -coefficients). The main contribution of this research is the possibility of incorporating the stochastic behavior of demand consumption in the final calculation of said coefficients. Power system simulations are considered, employing random variations in the active power injection of voltage-controlled nodes, in addition to power demands, using uniform or Gaussian distributions (for each variation, the power flow problem is solved using the classical Newton–Raphson approach). Based on the properties of the B -coefficients, a semi-definite programming model is proposed to obtain these parameters, considering that, for a power system with N g generators, 0. 5 N g N g + 3 + 1 measurements are required. The robustness of the proposed measurement-based optimization model is contrasted with a recent literature report using a linear solution method. Numerical results in the IEEE 14-, 39-, 57-, and 118- test feeders demonstrate the effectiveness of the proposed convex-based approach when using a different set of measurements (power system simulations) for cross variations, whose average estimation errors are between − 0. 1109 % and 3.3967% for all the tested cases. All the numerical simulations were carried out using the MATLAB software and the MATPOWER 7.1 tool. • A measurement-based approach using SDP allows for determining transmission losses coefficients. • A power flow solution generates data regarding power generation and losses for the proposed SDP model. • Cross-validation with 1000 simulation cases found low estimation errors in the tested transmission networks. [ABSTRACT FROM AUTHOR]

The modernization of power systems faces uncertainties due to fluctuating renewable energy sources, electric vehicle expansion, and demand response initiatives. These uncertainties require consideration in power flow analyses by calculating power flow across a spectrum of generation and load values. Traditional power flow methods, which rely on iterative solutions of non-linear equation sets, become computationally burdensome under these uncertain conditions. Surrogate models have been used to reduces the computational cost of power flow analysis under uncertainty. Recently, graph neural networks (GNNs) have gained increasing attention as surrogates for power system simulations. However, GNNs, similar to other machine learning models, require significant amounts of training data. This paper proposes an innovative approach combining a multi-fidelity methodology with a GNN-based surrogate model for efficient power flow calculations. This model is trained using both high-fidelity power flow simulations and low-fidelity power flow simulations. Our multi-fidelity GNN model not only reduces the cost of generating training data, but also outperforms both single-fidelity GNN models trained solely on high-fidelity data and conventional neural network models. Additionally, the proposed model exhibits robustness to minor topology changes and achieves reasonable performance with unseen topologies. The model's effectiveness is validated through simulations on standard IEEE systems. • The paper proposes a multi-fidelity GNN model for power flow analysis under uncertainty. • The proposed model combines high-fidelity and low-fidelity simulations to enhance training efficiency. • The proposed multi-fidelity model outperforms traditional and single-fidelity models in power flow calculation accuracy. • The proposed multi-fidelity model demonstrates robustness to minor grid topology changes, enhancing model adaptability. [ABSTRACT FROM AUTHOR]

A perfect combination of weather and a power system backed by battery-based energy storage enabled Ireland to break a renewable penetration record in February 2022, with 96 % of electricity demand covered by wind generation at its peak. In high renewable energy penetration scenarios seen worldwide, ensuring the resilience of power systems against major events has received increased attention. Supplementing traditional generation units with renewables and distributed energy resources (DER) in the energy mix to meet the load demand and net-zero emission goals has affected power system inertia, black-start readiness, service restoration, and operations. Maintaining adequate resources to retain black-start readiness in anticipation of widespread outages is becoming challenging. This study reviews the technical aspect of a 100 % inverter-based resources (IBR) based distributed restart zone (DRZ) to assist black-start. It investigates the feasibility of using a set of IBR control schemes to help restart a non-black start combined cycle unit by aggregated energy storage system and PV via transmission lines. Through assistance from power system operators, common operation practices are incorporated and simulated in a black start cranking path case study. Simulation results are reported and compared against the traditional practices based on hydropower. The results show that the IBR could supplement the traditional black-start resources. The functional requirements identified in this study would be useful for developing GFL and GFM inverter controls in the industry to enable black-start ancillary service by IBR. [Display omitted] • Shows how distributed renewables can reboot a power system post-blackout. • Proposed restart zone complements traditional methods, boosting grid resilience. • Simulations on a real power system prove distributed restart zones aid black-start. [ABSTRACT FROM AUTHOR]

• A zero-carbon hybrid powertrain based on an ammonia-hydrogen ICE is proposed. • Fuel supply strategy based on ammonia suppression hydrogen combustion knock is established. • A simulation model of the ammonia-hydrogen hybrid system is developed. • Ammonia-hydrogen hybrid systems are superior in both performance and economy. In the context of carbon neutrality, high efficiency, low carbon, and zero environmental impact have become an essential development direction of internal combustion engines (ICEs). Hydrogen energy has the advantage of having a high calorific value. It also has zero carbon emissions and is considered one of the significant alternative fuels for ICEs. Moreover, ammonia has a high octane number and has an anti-knock effect, which facilitates the mitigation of knock in hydrogen ICEs and potentially mitigates the negative impact of knock on engine volume power. Scholars have carried out some explorations on ammonia-hydrogen ICEs (AHICE), which have been applied in marine power systems. It is anticipated that AHICE will become one of the most significant development directions in the field of vehicle power systems in the future. However, there are still some problems with using AHICEs in passenger cars. The proposed work presented a zero-carbon hybrid power system based on an AHICE. Specifically, the external characteristics curve and power output boundary were obtained by bench experiments of the ICE under wide open throttle (WOT) conditions and diverse engine speeds and λ. Indeed, a hybrid system model consisting of an AHICE and a power battery was built where the AHICE was used to respond to the demanded power and the power battery was used to provide additional power and store the electrical energy converted from braking. Incidentally, an engine control strategy was developed to expand the knock limit and power boundary by dynamically adjusting the ammonia-hydrogen volume ratio. Finally, a hybrid power system simulation model was established based on MATLAB/Simulink. The CLTC-P and WLTC simulation results demonstrated that the zero-carbon hybrid system which comprised AHICE and power battery can work in the high efficiency area. The AHICE demonstrates lower fuel consumption while satisfying power requirements. This work provides a promising route to zero-carbon hybrid technology for the passenger car industry facing the challenge of carbon neutrality. [ABSTRACT FROM AUTHOR]

• The inherent physical information of the system is extracted from single-ended field data measurements. • The "target dataset" is generated via simulation, to minimize the gap between simulation and practical power system. • The proposed fault location method is compatible with digital fault recorders in practical substations, and works with a short data window of half a cycle before and during the fault. • The proposed data driven fault location method is validated using field data experiments. Data driven transmission line fault location methods have the potential to more accurately locate faults by extracting fault information from available data. However, most of the data driven fault location methods in the literature are not validated by field data for the following reasons. On one hand, the available field data during faults are very limited for one specific transmission line, and using field data for training is close to impossible. On the other hand, if simulation data are utilized for training, the mismatch between the simulation system and the practical system will cause fault location errors. To this end, this paper proposes a physics-informed data-driven fault location method. The data from a practical fault event are first analyzed to extract the ranges of system parameters such as equivalent source impedances, loading conditions, fault inception angles (FIA) and fault resistances. Afterwards, the simulation system is constructed with the ranges of parameters, to generate data for training. This procedure merges the gap between simulation and practical power systems, and at the same time considers the uncertainty of system parameters in practice. The proposed data-driven method does not require system parameters, only requires instantaneous voltage and current measurements at the local terminal, with a low sampling rate of several kHz and a short fault time window of half a cycle before and after the fault occurs. Numerical experiments and field data experiments clearly validate the advantages of the proposed method over existing data driven methods. [ABSTRACT FROM AUTHOR]

This paper presents an accurate and efficient initialization strategy for modular multilevel converters (MMCs) based on the shooting method, a numerical technique aimed at deriving the periodic steady-state operating condition of any circuit. This technique is compatible with MMC models of different levels of detail and whose control scheme may include modulation strategies and capacitor voltage balancing algorithms. Electromagnetic transient simulations of the NORDIC32 power system modified by adding a high-voltage direct current link with 128-level MMCs prove that the proposed initialization strategy allows starting simulations close to steady-state, thereby significantly limiting initialization transients and their corresponding extra CPU time. • We present a technique to initialize grids with modular multilevel converters (MMCs). • The proposed technique is based on the shooting method (SHM) and is purely numerical. • Initialization transients (and, thus, CPU time) are minimized by the SHM. • The approach is compatible with different MMC models and control schemes. • A benchmark grid is simulated to validate the accuracy and efficiency of the method. [ABSTRACT FROM AUTHOR]

Digital controllers are involved in every aspect of today's power systems, from local device-level to wide-area supervisory applications. However, the existing dynamic simulation tools are still struggling to capture an accurate response of such systems with adequate computational performance. The reason is that digital controllers introduce numerous discontinuities to the simulation and handling them is computationally expensive, thus forcing the users to compromise between accuracy and simulation performance. This paper investigates different traditional approaches for incorporating digital controllers in power system dynamic simulations and compares them with new interpolation-based approaches in terms of simplicity, accuracy, and performance. • Reviewing methods for numerical simulation of power systems with digital controllers. • Comparing the methods using case studies in terms of accuracy, and performance. • Providing insights on the Jacobian formulation, and their impact on performance. [ABSTRACT FROM AUTHOR]

This work provides an approach that automatically optimizes the component computations on graphics processing unit (GPU) devices from different vendors. The approach consists of a two-level optimization, where the first level considers the linear part of the computation for vectorization and applies mixed matrix formats to increase computational throughput further. Then, the second optimization level treats the combination of linear and non-linear parts as a black box and searches for the optimal configuration of parameters such as the degree of vectorization, the combination of matrix formats, and the group (of threads) sizes during parallel execution on GPU. Moreover, we also introduce constraints that reduce the optimization procedure's execution time. Finally, we select three different types of components that could be representative to computational tasks in power system and perform our optimization approach on these kernels. The computational performance is compared with unoptimized baseline and sparse linear algebra library based implementations, result shows that our optimization leads to better performance and more efficient memory utilization. • Automatic vectorization on linear algebraic operations. • Optimized memory access by combined matrix formats and storage strategies. • Automated benchmarking and code generation to allow automatic performance tuning. • Exploration space reduction algorithm to reduce benchmark time. • Performance outperforms the implementation based on linear algebra libraries. [ABSTRACT FROM AUTHOR]

• Introducing a modeling framework designed for simulating the power response dynamics of air-source heat pumps, tailored explicitly for high-speed demand-side response applications such as fast frequency response services. • Validating the real-time performance of the proposed modeling framework by demonstrating its deployment on conventional computational hardware, achieving a simulation rate of 10 kHz. • Presenting a Power Hardware-in-the-Loop experiment that employs the proposed model framework to implement a virtual heat pump, providing a valuable simulation tool for power system laboratories to design controllers for high-speed demand-side response applications. This paper presents a standard power-hardware-in-the-loop testing platform to simulate detailed air-source heat pump dynamics based on well-established modeling knowledge. Distributed air-source heat pumps can be potentially used for fast frequency response. However, using air-source heat pumps for rapid modulation cannot be based on the current assumptions of linear speed-power transient characteristics developed for low-speed temperature control applications. Customized setups with experimental validation options are needed to design the new fast frequency response compatible heat pump controllers. Most power system laboratories struggle to build a customized air-source heat pump, which hinders research progress. The proposed platform is relatively universal, can fit different heat pumps with minor modifications, and can be implemented on standard PHIL emulators in power system laboratories. Emulation results, real-time implementation details, and model complexity metrics are presented to assist in the transference of the setup to other laboratories. [ABSTRACT FROM AUTHOR]

The electrification of the transportation sector poses an opportunity for reducing greenhouse gas (GHG) emissions from passenger vehicles. Electric vehicle (EV) charging through dynamic wireless power transfer (DWPT), known as roadway electrification, could shift EV demand profiles to better coincide with renewable electricity generation. However, this would be a very large new load and few studies evaluate the regional impacts of DWPT charging in a power transmission system. This paper defines methods that address dataset generation for passenger vehicle trips and models to evaluate regional impacts for this emerging technology. Household vehicle miles traveled (VMT) data form localized EV demand profiles through discrete-event simulation. This data serves as exogenous inputs for a Production Cost Model (PCM) of a synthetic transmission system based on the Electric Reliability Council of Texas's (ERCOT) network. EV charging methods are compared for both a 2018 baseline generation mixture and a high-renewable generation case incorporating 20 GW of installed solar photovoltaic (PV) capacity. The PCM employs unit commitment and economic dispatch (UC&ED) models to compare financial, environmental, and grid reliability impacts from EV charging across passenger EV adoption levels. In-transit charging could reduce grid operational costs by as much as 1.49%, with up to $13.7B saved in annual vehicle operational costs for consumers compared to gas-powered vehicles. Health impacts analysis from power plant and vehicle tailpipe emissions from this study show net health benefits increase by 40% for in-transit charging coupled with high renewable generation. Renewable resources provide an avenue for cost-effective in-transit charging with reduced emissions. The combination of dataset generation and open-source power system modeling establish a foundation for the holistic evaluation of regional DWPT impacts. • PEV dynamic charging with increased renewable generation has positive system impacts. • Public data informs vehicle-miles traveled datasets for energy consumption analysis. • In-transit wireless charging from PEVs decreases power system peak demand hours. • In-transit PEV charging aligns with solar power to reduce curtailment and total cost. • Net health benefits from PEVs improve with in-transit charging and renewable sources. [ABSTRACT FROM AUTHOR]

• Battery modeling allows accurate prediction of the parameters in long-term processes improving decision-making. • The generic modeling algorithm combines three methods in one model: the experimental database, the equivalent circuit, and the analytical equations. • The model input signals are power demand and environment temperature corresponding to power system simulations such as sizing procedures. • The generic model could easily be utilized for any ESS (with similar technology) by a few experimental procedures. The lengthy process of sizing and optimizing hybrid energy sources requires an accurate battery model. This paper presents a generic new energy storage system model premeditated to solve the optimization problem of the sizing procedure. The model comprises several methods, a lookup table, an equivalent battery circuit, and analytical equations. The database is created offline based on experimental results achieved under various conditions. In the first step, the model receives an external vector of signals comprising load power demand, instantaneous generated energy, and ambient temperature. Then, the algorithm predicts the impact of the load on the battery parameters by either interpolation or extrapolation. The results are utilized at an equivalent circuit that supplies the basic parameters and the battery constraints. Next, analytical methods reveal the more advanced parameters such as charge, supplied and remaining energy, etc. The results show that the proposed dynamic battery model can predict the battery states through all operating zones and under different battery conditions. The benchmark results present higher accuracy than other available models. The proposed model was employed in a sizing procedure to verify the model's accuracy. It was shown that the new model estimates the required source rating more precisely than standard models. Since the suggested algorithm is based on actual battery curves, it can be utilized for all types of batteries by reentering the data of any other battery. [ABSTRACT FROM AUTHOR]

The increasing incorporation of solar and wind energy sources into the power system results in a diminishing power system inertia, which compromises the security of power system operation. This paper introduces a new scheduling method for hybrid power systems (HyPS), focusing on frequency security. It considers the time-varying characteristics related to frequency security requirements and the interdependence of components, such as generation units and the delivery network. Firstly, the optimal objective is to minimize overall operational costs and the pressure on energy delivery, ensuring the reserve capacities for fast frequency regulation remained within the predefined limits. Secondly, the frequency constrained unit commitment (FCUC) problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) framework is integrated to correct the advanced scheduling scheme in real-time. Thirdly, the coordinated scheduling scheme dynamically adjusts to operational events like fluctuations and line failures in the HyPS by employing the reward function of a comprehensive assessment. Finally, numerical results demonstrate the practicality of the novel coordinated scheduling approach for hybrid power systems through simulations in the Grid2Op environment. As a result, the frequency security margin of the HyPS is capable of increasing the daily average level of the frequency security margin by 3% across diverse scenarios, including fluctuations in power demand, renewable energy variations, and power line failures. PE-interfaced renewable energy sources are thereby able to integrate with conventional synchronous units as a hybrid power system to support frequency regulation services. • A coordinated scheduling method improves frequency security by utilizing variable renewable energy sources. • The optimal scheduling method manages physical network stress while redistributing the controlled units. • A dynamic regulation module is designed to improve the real-time frequency security margin. • The dynamic regulation module is integrated with SAC for energy and reserve scheduling. [ABSTRACT FROM AUTHOR]

To ensure the stability of nonlinear power systems with actuator faults and time delays, this study aims to design an adaptive fuzzy finite-time control scheme. To deal with this control issue, firstly, we propose an adaptive fault-tolerant finite-time controller using backstepping technique. To aid the controller design, fuzzy logic systems are applied to approximate the uncertain nonlinear terms without the prior information. Then, it is proved that the designed controller can ensure the stability in a finite time for the nonlinear power systems with actuator faults and time delays using Lyapunov–Krasovskii method. Finally, numerical simulations of a 4-machine power system with Thyristor Controlled Series Compensation (TCSC) illustrate the applicability of the proposed control methodology. • A novel fuzzy backstepping-based control scheme is proposed for the nonlinear power systems. • The realization of finite-time stability greatly saves the stability time of power systems, and improves operational efficiency. • The robustness of the nonlinear power systems with time delays and actuator faults is improved with the fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

Hydrogen can play a key role in facilitating the transition to a future deeply decarbonized energy system and can help accommodate higher penetrations of renewables in the power system. Arguments to justify this conclusion are supported by an analysis based on real-world data from China's Western Inner Mongolia (WIM). The economic feasibility and decarbonization potential of renewable-based hydrogen production are discussed through an integrated power-hydrogen-emission analytical framework. The framework combines a high-resolution wind resource analysis with hourly simulation for the operation of power systems and hydrogen production considering technical and economic specifications on selection of three different types of electrolyzers and two operating modes. The results indicate that using wind power to produce hydrogen could provide a cost-competitive alternative (<2 $kg−1) to WIM's current coal-dominated hydrogen manufacturing system, contributing at the same time to important reductions in wind curtailment and CO 2 emissions. The levelized cost for hydrogen production is projected to decrease in the coming decade consistent with increases in wind power capacity and decreases in capital costs for electrolyzers. Lessons learned from the study can be applied to other regions and countries to explore possibilities for larger scale economically justified and carbon saving hydrogen production with renewables. • Propose an integrated power-hydrogen-emission analytical framework for H2 production. • Conduct hourly system simulations with real data for Western Inner Mongolia China. • Alkaline and solid oxide electrolyzers are the most cost-effective options for 2020 and 2030. • Carbon-free H2 produced from wind power can be cost-competitive (0.5-2 $/kg). • An evidence of economic feasibility for renewable-based H2 production to other regions/countries. [ABSTRACT FROM AUTHOR]

Wind variability and hydro-wind complementarity should be both considered in the planning of electrical power systems. In such cases, the spatio-temporal dependence between streamflow and wind speed regimes needs to be asses to balance the electricity generation. Medium-term hydrothermal scheduling problems (MTHS) are used to define operation policies for electrical power systems under 5–10 years horizon. MTHS uses stochastic optimization techniques fed by synthetic streamflow scenarios. To set better operation policies, such scenarios should well represent statistical features of historical data. With the rapid growth in the installed capacity of wind power, operators are encouraged to consider novel approaches to represent the dependence of hydrometeorological variables. This study integrates wind variability and hydro-wind complementarity in the medium-term planning of electrical power systems employing joint simulation of periodic streamflow and wind speed time series. The generated scenarios are used as input to derive monthly operational policies via Implicit Stochastic Optimization. A hydropower plant and a wind farm, both located in the Northeast region of Brazil were selected as a case study. Results show that considering wind variability and hydro-wind complementarity can significantly reduce energy deficits in power systems and increase the flexibility in the operation of water reservoirs. • Hydro-wind complementarity is evaluated in the planning of electrical power systems. • Hydro-wind complementarity is represented by joint simulation of time series. • Copulas are used to represent the temporal dependence of streamflow and wind regimes. • Hydro-wind complementarity can support a more flexible water reservoir operation. [ABSTRACT FROM AUTHOR]

The paper presents a machine learning based Smart Intrusion and Fault Identification (SIFI) method to identify the cyber-physical abnormalities in an inverter-based cyber-physical microgrid (CPM). The SIFI method utilizes an ensemble classifier (EC) to assimilate the decisions from three distinct classifiers (C4.5 decision tree, random forest (RF), and forest by penalizing attributes (FPA)). The proposed method employs a voting mechanism to classify and localize abnormal events for improved accuracy. The paper investigates the effects of cyberattacks (Denial-of-Service (DoS) and Malicious Data Injection (MDI) attacks) and physical abnormalities caused by line faults in microgrids. To train the classifiers, SIFI employs dataset with statistical attributes extracted from measurements. The renewable alternative power systems simulation (RAPSim) software tool is utilized to model the proposed system. The effectiveness of the presented model is assessed with respect to mean value error (MVE). The efficiency of the classifier is demonstrated by comparing its performance with other classifiers in terms of the MVE. For physical abnormalities identification and localization, the approach provides enhanced outputs with the MVE of 0.157 % and 0.162 % correspondingly. For identifying MDI anomalies, the model provides better results with a lower error rate. For DoS anomaly detection, the model provides better classification performance with 0.136 % error. The extensive empirical analysis proves that the anticipated ensemble classifier-based SIFI model yields the minimum MVE for identifying physical faults and cyberattacks. Furthermore, the effectiveness of the proposed method is identified by considering a case study in which the SIFI method outperforms well for the MDI and DoS cyber-attacks in the CPM. [ABSTRACT FROM AUTHOR]

To manage power shortages, system operators must rely on involuntarily demand curtailment to keep the grid operational. Curtailment causes damages to consumers, the cost of which should be balanced against investments in a more reliable power system. To understand the behavior of the system and the expected character of shortages, we rely on resource adequacy studies. These studies provide a probabilistic quantitative framework by emulating system operation over many future years. However, the methods to emulate the power system must balance computational efficiency with accuracy and rely on different simplifications and approximations of the power system and its behavior. Here we show that the transmission model choice particularly impacts the results of these studies, especially for large systems where scarcity risk is shared through markets with significant inter-regional transmission capacities. Our results demonstrate that the most commonly used simple transmission grid models highly distort key adequacy metrics and are likely to cause the misinterpretation of system needs. • We evaluate the impact of transmission modeling details on resource adequacy. • The accuracy, scalability, and computational complexity of models are quantified. • Commonly used transmission models can skew adequacy indicators. • Locational information about potential scarcity is often misrepresented. • We provide insights into the role of the transmission grid in reducing scarcity risk. [ABSTRACT FROM AUTHOR]

This paper proposes a simplified yet accurate enough thermal model of Voltage Source Converters (VSC), aimed at circumventing the high computational cost of existing models, which prevents their use in electromechanical simulations. The proposed model reduces to a simple first-order system for thermal dynamics plus two quadratic equations separately modeling the IGBT and diode power losses. In addition, a methodology is provided to derive the proposed VSC thermal model parameters from manufacturer data. The proposed model is tested for two types of devices, both in steady and transient states. The results show that the reduced-order thermal model produces accurate results at a low computational cost, making it especially suitable for the co-simulation of thermal and electrical dynamic phenomena. • New methodology for embedding VSC thermal behaviour in electromechanical simulations. • Fast and slow thermal dynamics modelled by respective algebraic and differential equations. • VSC thermal model parameters computed from manufacturer datasheet. • Methodology validated for three VSC applications in steady-state and dynamic conditions. [ABSTRACT FROM AUTHOR]

Frequency estimated throughout the wide-spread grid is used for monitoring and controlling various local as well as global power system phenomena. Such applications require precise frequency estimation, especially during challenging power system conditions when signals are non-stationary or contain harmonics. Therefore, in this paper, a signal-reconstruction-based approach has been described to estimate the frequency and rate-of-change of frequency (ROCOF) for a single-phase system. The approach is based on the idea that the frequency information in case of a reconstructed signal is preserved even during off-nominal frequency conditions. Single-phase reconstructed time-domain signals are proposed as an alternative to phase-angle signals for frequency estimation. From the reconstructed time-domain signals, the frequency is estimated using the convolution average filter (CAF) based method and a single-phase demodulation technique employing Hilbert filter (HFD). The effectiveness of this approach especially during off-nominal and inter-harmonic conditions is demonstrated using the synchrophasor standard based test signals. The proposed method is compared against state-of the art single-phase phasor measurement unit based estimates. Accuracy of the reconstruction-based approach is also verified through signals obtained from the ISO-New England power system and simulation based studies. The output frequency and ROCOF signals are also used for mode identification using the Prony method. • Frequency and ROCOF computation using a reconstruction based approach is presented. • M-class frequency and ROCOF estimates are obtained using single-phase P- class PMUs. • Computed frequency and ROCOF values also determine the power system modal behavior. [ABSTRACT FROM AUTHOR]

• Proposing an new integration method that considers the relationship between simulation time-step and time constants of elementary transfer function blocks (ETFBs), including three integration modes: single-step integration, multiple-step integration, and static approximation. • Giving the implementation of static approximation for ETFBs with small time constants. • Discussing the determination of the threshold values of internal time-step for ETFBs. • Applying the proposed method to an open-source Simulation Toolkit for Electrical Power Systems (STEPS). With the development of renewable energy sources, the scale and complexity of power systems are continuously increasing, resulting in a high requirement for the efficiency of dynamic simulation. Increasing the simulation time-step is a possible measure for the fixed time-step (FTS) method to accelerate the simulation, yet also a potential cause for deteriorating the simulation accuracy. To improve the overall power system dynamic simulation speed without jeopardizing the expected accuracy, this paper proposes a novel Uneven Internal Time-Step Adjustment method based on Trapezoidal integration of elementary transfer function blocks (ETFBs). For ETFBs with different time constants, three different integration modes are defined, including single-step integration, multiple-step integration, and static approximation. Each ETFB can select an appropriate integration mode and adjust its internal time-step, automatically expanding the size of the simulation time-step. Implementation of static approximation for ETFBs with small time constants is given. Discussion on the determination of threshold values of internal time-step is provided, which mainly affect the accuracy of the system. The proposed method is applied to the open-source Simulation Toolkit for Electrical Power Systems (STEPS). Different cases are tested to obtain the maximum simulation time-step that can maintain numerical stability and validate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

• Solar photovoltaics use inverters to interface with the AC power system. • Inverters do not possess the rotational characteristics of synchronous generators. • High instantaneous inverter penetrations complicate traditional stability approaches. • Control techniques seen as the primary barrier to high inverter penetrations. • Research indicates no fundamental challenges to high inverter penetrations. As power system renewable energy penetrations increase, the ways in which key renewable technologies such as wind and solar photovoltaics (PV) differ from thermal generators become more apparent. Many studies have examined the variability and uncertainty of such generators and described how generation and load can be balanced for a wide variety of annual energy penetrations, at timescales from seconds to years. Another important characteristic of these resources is asynchronicity, the result of using inverters to interface the prime energy source with the power system as opposed to synchronous generators. Unlike synchronous generators, whose frequency of alternating current (AC) injection is physically coupled to the rotation of the machine itself, inverter based asynchronous generators do not share the same physical coupling with the generated frequency. These subtle differences impact the operations of power systems developed around the characteristics of synchronous generators. In this paper we review current knowledge and open research questions concerning the interplay between asynchronous inverter-based resources (IBRs) and cycle- to second-scale power system dynamics, with a focus on how stability and control may be impacted or need to be achieved differently when there are high instantaneous penetrations of IBRs across an interconnection. This work does not seek to provide a comprehensive review of the latest developments, but is instead intended to be accessible to any reader with an engineering background and an interest in power systems and renewable energy. As such, the paper includes basic material on power electronics, control schemes for IBRs, and power system stability; and uses this background material to describe potential impacts of IBRs on power system stability, operational challenges associated with large amounts of distributed IBR generation, and modern power system simulation trends driven by IBR characteristics. [ABSTRACT FROM AUTHOR]

As electricity demand can vary considerably and unpredictably, it is necessary to integrate energy storage with power generation systems. This study investigates a solid oxide and molten carbonate fuel cell system integrated with a gas turbine (GT) for power generation. The advanced adiabatic compressed air energy storage (AA-CAES) system is designed to enhance the system flexibility. Simulations of the proposed power system are performed to demonstrate the amount of power that can supply to the loads during normal and peak modes of operation under steady-state conditions. The pressure ratios of the GT and AA-CAES and the additional air feed are used to design the system and analyze the system performance. The results show that a small additional air feed to the GT is certainly required for the hybrid system. The GT pressure ratio of 2 provides a maximum benefit. The AA-CAES pressure ratio of 5 is recommended to spare some air in the storage and minimize storage volume. Moreover, implementation of the GT and AA-CAES into the integrated fuel cell system allows the system to cope with the variations in power demand. • The hybrid SOFC system with AA-CAES energy storage is proposed. • The pressure ratios of the GT and AA-CAES and added air feed are key design parameters. • Each power unit in the SOFC system is analyzed under the based load and peak load. • The SOFC system can cope with up to 60% increase in power demand of the based load. • The GT and AA-CAES provide 22% and 12% of total power generation at the peak load. [ABSTRACT FROM AUTHOR]

In this paper, a generalized neural network with a novel auxiliary function is proposed to solve a distributed non-differentiable optimization over a multi-agent network. The constructed auxiliary function can ensure that the state solution of proposed neural network is bounded, and enters the inequality constraint set in finite time. Furthermore, the proposed neural network is demonstrated to reach consensus and ultimately converges to the optimal solution under several mild assumptions. Compared with the existing methods, the neural network proposed in this paper has a simple structure with a low amount of state variables, and does not depend on projection operator method for constrained distributed optimization. Finally, two numerical simulations and an application in power system are delineated to show the characteristics and practicability of the presented neural network. [ABSTRACT FROM AUTHOR]

This paper describes the detailed modeling and simulation of an aerogenerators system based on DFIG using RT-LAB simulator. A stator flux oriented vector is applied to decouple the control of active and reactive powers generated by the DFIG, and other controllers are needed to keep the DC-link voltage and grid currents at desired values. In order to adjust appropriately the pitch angle and keep the wind power at rated value, a pitch control and MPPT are involved. The model of the studied was developed first in the MATLAB ® , then, it was rebuilt on RT-LAB to test its performance. Simulation results validate the capability of the platform to achieve rapid simulation of complex power systems. At the end, the details of the simulation results are presented, discussed, and concluded. • Detailed understanding of both modeling and control of DFIG based aerogenerators integrated in an HVAC is presented. • Comprehensive of real-time simulation steps of a complex power system are presented. • RT-LAB based real-time simulations of a wind farm connected to a large power system in faulty conditions are illustrated and discussed. [ABSTRACT FROM AUTHOR]

• Parallelizing the simulation of a wind farm with individualized turbines, resulting in computational cost savings. • A unified CUDA kernel is introduced, overlapping the grid and wind turbine solver, reducing computational costs. • Use of high-performant synchronization primitives, such as Cooperative Groups in CUDA. Dynamic simulations are crucial for proper power system operation. However, these can be computationally expensive. This article introduces an efficient CUDA kernel for GPU acceleration, that achieves parallelization without the need to solve many different power systems at once. The proposal unifies generator and grid execution within a single CUDA kernel, ensuring accurate dynamic simulations of wind farms. A custom solution from scratch was developed, that integrates the power system network solver with connected devices. Wind turbines are modeled as state-space linear systems, while the grid is parallelized through subunit partitioning and small submatrix inverses to reduce computational costs. The resulting kernel extracts parallelism in grids with limited inherent parallelism, outperforming previous methods such as column-level parallelism. Simulations on an NVIDIA GeForce RTX 3090 show a 26.39x speedup with respect to the C++ version, and the cooperative groups' synchronization primitives provide optimal performance while simulating up to 2464 wind turbines. [ABSTRACT FROM AUTHOR]

In order to make full use of the power quality monitor (PQM) for voltage sag location, an optimal configuration method of the PQM considering the voltage sag location is proposed. The method consists of several steps. Firstly, a co-connectivity observation-based integer linear programming is utilized to obtain a variety of optimal schemes while ensuring the observability of all state variables. Secondly, a decision tree with good solvability is employed to further filter out the PQM deployment scheme with better voltage sag location accuracy from the preferred schemes, using a large dataset of fault data generated by simulation software. Thirdly, three indicators of PQM monitoring are defined to cater to individual user needs, and the performance of each PQM configuration scheme is comprehensively evaluated using a weighting method. Finally, the optimal deployment scheme of PQMs is tested on the IEEE 39-node network, taking into account different installation costs, by generating sample data on a power system simulation platform. • Locating accuracy of the voltage sag source is taken into account when placing PQM. • The PQM deployment scheme can be easily adjusted based on the application objectives. • The multi-objective optimal configuration of PQMs is realized. [ABSTRACT FROM AUTHOR]

• User experience in porting offline simulations to real-time. • EMT best practices to accelerate offline simulations. • Detailed representation of Hydro-Québec power system: 1666 3-phase buses. • "Real-time modeling" used offline: speedups 6.78 (1 core) & 22.12 (4 cores). • Real-time operation with 7 Intel Xeon Scalable Gold 6144 (56 cores). At Hydro-Québec, power system studies are mainly done with offline electromagnetic transient simulation tools and real-time simulation is typically reserved for hardware-in-the-loop studies related to HVDC and compensation system commissioning. However, there is a growing internal need to pursue large-scale power studies incorporating physical systems to explore wide-area monitoring, protection, and control strategies: large power systems must then be ported from offline software to the real-time environment. The current paper presents lessons learned over the years about the offline to real-time porting process. As Hydro-Québec is involved in simulation tool development, software-related issues and challenges are presented and discussed. Details are then provided regarding the porting process of a large-scale power system required for wide-area control explorations. Prior to waveform comparison and performance assessment, modeling details and required modifications on the original simulation schematic are presented. In closing, electromagnetic transient modeling best practices and tricks to facilitate porting offline simulations to real-time are reported here to help users increase the efficiency and performance of their offline simulations and prepare them for real-time operation. [ABSTRACT FROM AUTHOR]

Detailed Equivalent Models (DEMs) of Modular Multilevel Converters (MMCs) are generally developed based on Thevenin equivalent circuits with a time-varying resistor. This approach may become computationally inefficient, specifically for the simulation of large power systems with many nodes, where the network admittance matrix needs to be frequently re-inverted every time a switching event occurs. This paper proposes a novel strategy to eliminate admittance matrix re-inversions during the converter's normal operation and restrict it only to when the converter undergoes blocking. The proposed DEM thus yields marked reductions in the simulation time of MMC circuits, and is particularly useful in studies wherein repetitive simulations are necessary. Models are implemented for MMCs with half-bridge (HBSM) and full-bridge (FBSM) sub-modules in the PSCAD/EMTDC simulator, and their accuracy is thoroughly validated for normal and blocked operating conditions. It is shown that the developed models are 30% and 60% more computationally efficient, respectively, for HBSM and FBSM MMCs in comparison to existing DEMs. • New detailed equivalent models for MMCs with HB and FB submodules are developed. • Simulation cases are shown to verify the accuracy and stability of the models. • A section on stability and accuracy is included to assess these issues for mixed integration methods. • The developed models show marked reductions ins simulation time compared with existing models. [ABSTRACT FROM AUTHOR]

Time-synchronized estimates acquired using the Wide Area Measurement System (WAMS) have substantially helped enhance the health of the modern power grid. WAMS data when used in conjunction with appropriate tools can aid in the timely detection of power system events. With the continuous expansion of the WAMS network, identification of events and narrowing down on their location is emerging as a serious challenge for power system operators. Therefore, in this paper an event detection and localization tool is developed which analyzes data obtained from multiple Phasor Measurement Units (PMUs) installed throughout the WAMS network in order to identify the occurrence of an event. The effectiveness of this tool is demonstrated using practical signals from the ISO-NE power system and simulation based signals obtained from a 4-machine 10-bus system. Occurrence of an event is flagged by comparing the wavelet energy and standard deviation values of a PMU signal against a threshold. An event localization algorithm based on the number of PMUs involved in the event detection stage is proposed. Based on the presented algorithm, disturbances are classified as local or wide-spread events. Finally, a method to identify a loss-of-synchronism condition using phase angle difference (PAD) signals obtained across transmission lines is also proposed. • An event detection, localization and classification method is proposed. • Multiple-PMU data is processed in order to identify the occurrence of an event. • Events are classified employing the "considerable excursion" criteria. • Method to predict loss-of-synchronism condition using PAD signals is also proposed. [ABSTRACT FROM AUTHOR]

Highlights • An improved average value model (AVM) of modular multilevel converter (MMC) for large-scale system simulation. • Accurate performance in all types of faults and blocked state of MMC. • Easy modelling in commonly used software packages. Abstract Detailed model (DM) of modular multilevel converter (MMC) brings about a high computational burden for electromagnetic transient (EMT) simulation. Therefore averaged value model (AVM) of MMC is proposed for fast simulation of large-scale multi-terminal HVDC system. No direct electrical contact exists between AC and DC system in conventional AVM and therefore the coupling relationship among electric quantities in fault analysis could not be accurately modelled. In order to solve the aforementioned problem, an improved AVM (IAVM) of half-bridge (SB) MMC topology is proposed. All capacitor voltages of AVM are constant and an arm is directly equivalent to a controlled voltage source. The proposed AVM can be applied for the DC faults and blocking conditions by introducing a group of switches connecting AC and DC sides. Two extra controlled voltage sources are introduced for correct DC voltage offset in AC voltages under DC line-to-ground (LTG) faults. Simulation results verify the accuracy and efficiency of the IAVM. IAVM could be implemented in commonly used software packages and is suitable for power system fault analysis. [ABSTRACT FROM AUTHOR]

Resistive type superconducting fault current limiter (R-SFCL) has always been a research hotspot. Due to its simple principle and structure, good current limiting effect and easy module, it has a promising prospect applied in power system. However, R-SFCL quenching is a complex process of multiple physical fields coupling, including current, magnetic field and temperature. In general, finite element software is used to accurately simulate the process of R-SFCL. In the simulation of complex power system, the coupling calculation method can increase the complexity, calculated amount and operation time of transient simulation, availability of which is low. The calculation method of fitting formula can't accurately reflect the change of resistance in the quenching process, which affects the accuracy of system simulation. According to experiments and simulations, it is found that once superconducting tape tends to completely quenching, the quenching resistance R of YBCO superconducting tape has a fixed relation with joule heat Q produced by quenching resistance when the refrigerating mode remains unchanged. Therefore a novel simplified modeling method based on R–Q curve of YBCO resistive type SFCL is proposed, which can be easily implemented in system simulation software such as MATLAB, PSCAD/EMTDC. This method is simple structure and based on experimental data which has the advantage of fast operation speed. The good simulation effect of the method is verified by quenching experiments. [ABSTRACT FROM AUTHOR]

Highlights • A general framework for model validation from frequency perspective is proposed. • New parameters, Frequency Ramp Rate (FRR) and Activity Range (AR), are introduced. • A quantitative assessment method is proposed for the model validation process. • Proposed framework is implemented successfully for a large and real power grid. • The steps of implementation are clarified through detailed results and discussion. Abstract This paper proposes a quantitative model validation framework from the frequency perspective in which some of the technical issues of the process are solved. Two influential parameters of the process, namely the frequency ramp rate and activity range are introduced. A new quantitative assessment method including indices development and threshold calculation method is proposed. In order to reduce the complexities and efforts, all the capabilities of the existing models are initially tried (as approach 1) and upon its failure, more complex models are developed and employed (as approach 2). After discussing the theoretical aspects, the implementation steps and effectiveness of the process are demonstrated by applying the framework to a large and real power system. Some solutions for the practical issues are proposed and implemented. Since the simulation results of the first approach exceed some specified thresholds, the second approach is followed. Quantitative assessment of the second approach proves that the simulation results satisfy the specified thresholds for all incidents. The prominent and vital effect of different influential parameters and improving actions of the process are also demonstrated on the validated model. [ABSTRACT FROM AUTHOR]

Abstract The analysis of power systems with a significant share of renewable generation using probabilistic tools is essential to appropriately consider the impact that the variability and intermittency of the renewable generation has on the grid. This paper provides a critical assessment and classification of the available probabilistic computational methods that have been applied to power system stability assessment (including small and large disturbance angular stability, voltage and frequency stability). A probabilistic analysis framework with a state-of-the-art review of the existing literature in the area is presented comprising of a review of (i) input variable modelling, (ii) computational methods and (iii) presentation techniques for the output/results. The most widely used probabilistic methods in power system studies are presented with their specific application areas, advantages, and disadvantages. The purpose of this overview, classification, and assessment of the existing methods is to identify the most appropriate probabilistic methods to be used in their current forms, or suitably modified, for different types of stability studies of power systems containing renewable generation. Highlights • Uncertainty modelling techniques for independent variables. • Uncertainty modelling techniques for correlated variables. • Probabilistic techniques applied to power system stability. • Identification of efficient computational techniques. • Future research needs in this area. [ABSTRACT FROM AUTHOR]