Power System Dynamic Simulation Research Articles

Uneven internal time-step adjustment for fast power system dynamic simulations based on Trapezoidal integration of elementary transfer function blocks

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.

Dynamic modelling, simulation, and control of hybrid power systems for escort tugs and shuttle tankers

The maritime industry targeted shipping decarbonization over the past few years for achieving stakeholders’ expectations and also to abide by the environmental global agenda. For that, increasing energy efficiency and reducing pollutant gas emissions are key objectives. Thus, alternatives, such as hybrid power systems and the use of alternative fuels, emerge as promising solutions to meet stakeholders’ and society’s expectations. This paper proposes hybrid power system architectures for an escort tug and for a shuttle tanker, which are vessels largely used in the oil and gas sector. We analyze different control strategies of these systems, namely Rule-Based method, Equivalent Consumption Minimization Strategy (ECMS), Adaptive-ECMS and Model Predictive Control (MPC), using numerical modeling and simulations, comparing their performance with those of traditional power systems. The most efficient designs showed reductions in fuel consumption of up to 18.9% for tugs, and 2% for shuttle tankers.

Impact of Inner Control Loops on Small-Signal Stability and Model-Order Reduction of Grid-Forming Converters

The analysis and efficient dynamic simulation of power systems with a large number of distributed generators using grid-forming converter control requires reduced order models. The natural separation of time scales allows for the simplification of the inner control loops, i.e. voltage and current controller. This work provides a structured overview of model reduction approaches of the inner loops and introduces new reduced models based on transfer function approximation. The models are thoroughly tested using frequency response analysis, eigenvalues sensitivity analysis, time-domain simulation and stability regions for various scenarios, varying controller and electrical parameters. Furthermore, the influence of simplifications of the network lines is investigated. It becomes apparent that there is no single reduced model that is suitable for all scenarios. Models ought to be selected depending on the specific scenario. Guidelines for the model selection as well as the choice of controller parameters are provided.

A Fast Method for Uncertainty Analysis of Power System Dynamic Simulation

Uncertain variables, such as electric power system parameters, have significant impacts on dynamic simulations of power systems. As traditional uncertainty analysis methods for power system dynamic simulations, both the simulation method and the approximation methods are difficult to balance the model complexity, computational efficiency, and simulation accuracy. In order to balance the model complexity, computational efficiency, and simulation accuracy, this paper proposes a method for uncertainty analysis for power system dynamic simulation based on the Nataf transformation and Gaussian-Hermite quadrature. Firstly, the samples on the normal distribution space are determined according to the Gaussian-Hermite quadrature points and the Nataf transformation. Secondly, obtain the simulation samples by inverse Nataf transformation, and perform power system dynamic simulation. Thirdly, the random output is approximated as a linear combination of a single random input, and the mean and standard deviation of the random output under the impact of a single random input are calculated by Gaussian-Hermite quadrature. Then, calculate the mean and standard deviation of the random output under the impact of all random input. Finally, the effectiveness of the proposed method is validated on the IEEE 9-bus system and IEEE 39-bus system. Compared with Monte Carlo simulation and Latin Hypercube sampling, the proposed method can greatly reduce the simulation time for uncertain dynamic simulations while maintaining high accuracy.

The energy storage mathematical models for simulation and comprehensive analysis of power system dynamics: A review. Part i

Energy storage systems are increasingly used as part of electric power systems to solve various problems of power supply reliability. With increasing power of the energy storage systems and the share of their use in electric power systems, their influence on operation modes and transient processes becomes significant. In this case, there is a need to take into account their properties in mathematical models of real dimension power systems in the study of various operation modes, design, etc. In this article the main types of energy storage devices, as well as the fields and applications of their use in electric power systems are considered. The principles of realization of detailed mathematical models, principles of their control systems are described for the presented types of energy storage systems. The article is an overview and can help in choosing a mathematical model of energy storage system to solve the necessary tasks in the mathematical modeling of storage systems in electric power systems.Information is presented on large hydrogen energy storage units for use in the power system.

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design of an AI-oriented power system dynamic simulator is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AI-assisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out under four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation of the AI-oriented power system dynamic simulator.

PowerSAS.m—An Open-Source Power System Simulation Toolbox Based on Semi-Analytical Solution Technologies

In handling complex power system simulation tasks, semi-analytical solution (SAS) methods have proven to be numerically robust and computationally efficient. They provide a competitive alternative to traditional numerical approaches. Still, there is inadequate power system simulation software, especially the open-source tools, that implements this technology. This paper introduces PowerSAS.m, an open-source toolbox that closes this gap by providing SAS baseline simulation options for power system steady-state and dynamic simulations. At its core, it implements a novel SAS method and encloses various heuristics and simulation techniques to ensure enhanced computational performance. In case studies, we verify PowerSAS.m in benchmarking comparisons and demonstrate its functionalities in grid analysis scenarios.

The energy storage mathematical models for simulation and comprehensive analysis of power system dynamics: A review. Part II

With the development of electric power systems, especially with the predominance of renewable energy sources, the use of energy storage systems becomes relevant. As the capacity of the applied storage systems and the share of their use in electric power systems increase, they begin to have a significant impact on their dynamic properties. Accordingly, when solving the issues of design and operation of power systems with energy storage systems, it becomes necessary to take into account their properties. For this purpose, depending on the type of energy storage, different mathematical models have been developed, with varying degrees of detail. At that, for a number of researches it is necessary to apply detailed mathematical models, but simplified models are also widespread. In article approaches in simplification of detailed models of energy storage systems with their mathematical description are described, the area of their application is considered. The authors also give some limitations and disadvantages associated with the use of simplified models. The article is a review and can help in choosing a mathematical model of the energy storage system to solve the necessary problems in the mathematical modeling of storages in electric power systems.

Stochastic Power System Dynamic Simulation and Stability Assessment Considering Dynamics From Correlated Loads and PVs

The integration of uncertain photovoltaics (PVs) and flexible loads leads to uncertainties in the power system dynamic simulation results. Furthermore, geographically close PV farms are correlated and may exhibit nonlinear correlations. This article proposes a copula-based sparse polynomial chaos expansion (PCE) framework for quantifying the impacts of uncertain dynamic PVs and loads on power system dynamic simulations and stability. The dynamics include both PV and load stochasticity and those governed by differential and algebraic equations. The copula statistics are utilized to accurately characterize the dependence structure of PVs and further used to develop the copula-PCE for quantifying the impacts of uncertain PVs and loads. A probabilistic TSI is also developed to assess the uncertainties from PVs and loads on the system stability. To address the cases, where both stable and unstable conditions coexist, a preprocessing step via sample classification is proposed. The effects of different dependence structures of PVs and different numbers of uncertain sources are investigated. Comparison results with other methods on the modified IEEE 39- and 118-bus systems, including the Monte Carlo method, Latin hypercube sampling, and traditional PCE without consideration of uncertain input correlations show that the proposed method is able to accurately quantify the uncertain dynamic simulations and transient system stability while being computationally efficient.

Modeling of Direct-Drive Permanent Magnet Synchronous Wind Power Generation System Considering the Power System Analysis in Multi-Timescales

The dynamics of wind power generation cannot be neglected in the modern power system and could have a great impact on the system dynamics, even raising the risk of a blackout. Because of this, power system simulation has to include the model of wind power generation. However, due to the high order of the full model of the wind power generator, it is impossible to model them in detail in the use of the power system dynamic simulation considering the thousands of wind generators in the grid. In this context, a simplified model is normally used with the trade-off in lower accuracy. As a direct-drive permanent magnet synchronous wind power generation system (D-PMSG) would take up a certain occupation in the modern power system, a proper D-PMSG simplified model is needed in the power system simulation. For a different research purpose in a different timescale, a different complexity of the model can be used to maximize the accuracy, in the meantime speeding up the simulation. This paper proposes a set of simplified models of the direct-drive permanent magnet synchronous wind power generation system (D-PMSG) and classifies them according to the timescale of the dynamics and the use cases, i.e., faults (transient stability analysis), system contingencies (voltage and frequency stability analysis) and wind speed variations (energy transformation). The accuracy of the proposed simplified models is verified by comparing them with the detailed D-PMSG electromagnetic transient mode in Matlab/Simulink, and their use case of the power system simulation is validated based on the case study of the IEEE 39-bus system considering the above scenarios.

Trust-Region Approximation of Extreme Trajectories in Power System Dynamics

In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given a bounded set that represents parametric uncertainty. We show how this method, while remaining computationally efficient compared with sampling-based techniques, overcomes the limitations of previous sensitivity-based techniques to approximate the bounds of the trajectories, when the local approximation loses validity because of the nonlinearity. In addition, we show how this method can be adapted to account for those cases in which the initial conditions depend on the uncertain parameter. To conclude, we present several numerical experiments that showcase the accuracy and scalability of the technique, including a demonstration on the IEEE New England test system.

Diagnosis of inter‐turn short circuit in excitation winding of pumped storage unit based on frequency‐resolved circulation spectrum

The online monitoring of inter-turn short-circuit (ITSC) faults in excitation windings of pumped storage units is easily affected by static air-gap eccentricity (SAGE) and armature response, and the early fault characteristics cannot be easily identified effectively. To address these problems, this study proposes a frequency-resolved circulation spectrum (FRCS) fault diagnosis method for ITSC faults in excitation windings. Firstly, the equation for the same phase multi-branch circulation under SAGE compound ITSC conditions is derived based on the structural characteristics of pumped storage units and electromagnetic induction relations, and obtains the characteristic harmonics of the loop current caused by ITSC faults. Secondly, this study introduces the implementation method of FRCS, considering a pumped storage unit with a generation capacity of 334 MVA as the research object. This study develops a two-dimensional finite element simulation model, calculates the magnitude of the in-phase branch circulation at different fault levels, extracts the corresponding time and frequency domain spectrum and FRCS, and verifies the effectiveness of the proposed method based on comparative experiments. Finally, based on the MJF-30-6 Synchronous Generator Experimental Platform in the Power System Dynamic Simulation Laboratory, ITSC operation at different fault levels is simulated, and the corresponding in-phase branch current data are collected for FRCS analysis. The experimental results demonstrate that the method can effectively eliminate SAGE and armature reaction interference, identify early weak ITSC faults and visualise the severity of faults in the form of pixel dots.

Dynamic Simulation of Power Systems Considering Transmission Lines Icing and Insulators Flashover in Extreme Weather

Global climate deterioration caused by excessive carbon emissions poses a severe threat to the stable operation of power systems. In extreme snow and ice weather, complex faults, such as ice-covered line breakage and insulator flashover, may be triggered. Hybrid simulations to reveal the influence mechanism and dynamic interaction process between extreme weather and power systems have become a research focus. Relationship models between meteorological conditions and grid failure were constructed in ice accretion and insulator flashover scenarios. Based on the above two models, a multi-variable and multi-time scale hybrid simulation of the meteorological process and power system, considering line icing and insulator flashover, was realized. An example was provided to verify the feasibility of the proposed simulation scheme.

A Hybrid Model for Fast and Efficient Simulation of Fluid Power Circuits With Small Volumes Utilizing a Recurrent Neural Network

The modeling and simulation of fluid power systems are essential parts of the real-time simulation of virtual prototypes of mobile working machines. In several cases in the dynamic simulation of such fluid power systems, a longer simulation time is required. This makes the traditional mathematical models inefficient for real-time simulations, particularly when simulating fluid power systems because of the small volumes in stiff differential equations of pressure. The main reason for such an issue in the simulation of fluid power systems is the existence of small oil volumes in stiff differential equations of pressure. To overcome this issue, a novel hybrid model is proposed for stiff fluid power systems simulation. The main feature of the model is the utilization of a recurrent neural network instead of stiff differential equations of pressure with small volume. At the same time, the dynamics of the rest system are traditionally presented with algebraic and differential equations. The testing results of the introduced hybrid model showed that the novel method can reduce the simulation time, which makes the model suitable for real-time applications. Moreover, the accuracy of the model remains at a fairly high level compared to traditional mathematical models.

Resilient Adaptive Parallel sImulator for griD (RAPID): An Open Source Power System Simulation Toolbox

This paper describes an open source power system simulation toolbox: Resilient Adaptive Parallel sImulator for griD (RAPID), a package of Python codes that implements an advanced power system dynamic simulation framework. The main function of RAPID is time-domain simulation, and it contains details of the solution process, including the quasi-steady-state power flow analysis, to solve nonlinear differential algebraic equations describing the detailed representation of network and transient stability models of dynamic devices in a computationally efficient manner. In particular, RAPID utilizes and incorporates emerging solution techniques; a novel “parallel-in-time” (Parareal) algorithm, adaptive model reduction, and semi-analytical solution methods as well as the standard numerical solution methods. Moreover, the whole simulation process for the transmission network has been coupled with OpenDSS, a widely used open-source distribution system simulator, to enable the co-simulation of integrated transmission and distribution systems. Basic structure and features of RAPID are presented to provide an easy-to-understand guideline for the usage of the tool and illustrate its unique capabilities. This paper also presents simulation results for a number of test scenarios and demonstrates RAPID’s values for advancing power systems research in dynamic modeling and simulation techniques toward researchers as well as educators and students.

Reliability and Dynamic Performance Simulation Based on Relay Protection Summary

With the development of science and technology, national security and social life can not be separated from electricity every moment, and the safety of power grid is becoming more and more important. At present, the research on relay protection model in dynamic simulation of power system in China is still in the primary stage. Because the research is not deep enough, the control law of relay protection elements can not be well grasped. The failure can not be handled well. Therefore, in order to ensure the safe operation of the power system, we must first strengthen the grid structure of the power system, improve the elasticity coefficient, distribute the reserve capacity reasonably, strengthen the adjustment ability of the tie line between the major power grids, and improve the stable reserve of the power grid. In order to improve the authenticity and reliability of dynamic simulation, it is necessary to establish a set of relay protection models, which should be consistent with the actual relay protection. In this way, the stability problem in power system can be analyzed accurately to improve the reliability of power system. The relay protection model established in this paper reduces the complexity of modeling and can reflect the dynamic characteristics of power system after interference. It is of great significance to the analysis and research of power system.

Impact of Governor Non-Linearities Representation on Power System Ultra-Low Frequency Oscillations

Power system dynamic models are essential for the stability analysis of a perturbed electrical system. In recent years, hydro-dominant power systems have experienced the occurrence of ultra-low frequency oscillations (ULFO). While power system dynamic simulation stands as an essential tool for the research community to analyze and understand this phenomenon, such systems involve non-linearities in their dynamic model affecting the ULFO analysis’s validity. This present work investigates the impact of governor non-linearities representation on the validity of the ULFO analysis. Sinusoidal-input describing function (SIDF) is borrowed from control theory to appreciate existing governor non-linearities models’ capacity to respond accurately to ULFOs. Through rigorous test-cases, results prove the imperativeness of prior accurate measurement of those governor non-linearities before their numerical implementation to ensure the power system dynamic simulation model’s validity for ULFO analysis.

Dynamic simulation of a space gas-cooled reactor power system with a closed Brayton cycle

Dynamic simulation of a space gas-cooled reactor power system with a closed Brayton cycle

Dynamic Modeling and Simulation of a Stand-alone DC Hybrid Microgrid for a Base Transceiver Station in Nigeria

This work considers the dynamic modeling and simulation of a DC hybrid power system for a rural base transceiver station in Nigeria currently being powered by an AC diesel generator (DG). The transient behaviour of the system is studied under varying solar irradiation to ascertain the stability of the power supplied to the sensitive telecommunication equipment. Each component of the system is designed and simulated in a MATLAB/Simulink environment and connected to form the whole system. A permanent Magnet DC diesel generator is used as back-up power for the system. A detailed presentation of the solar array, buck converter, battery storage system, battery controller, diesel generator and the load are presented in this paper. The result shows a stable power output to the load at rated voltage of 48 V.

Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. The selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.