Power System Simulation Research Articles

A Network Partitioning Method for Real‐Time Simulation of Power System Based on State‐Space and Branch Cutting

ABSTRACTWith the sustainable development of the power system, the dynamic characteristics of the system are becoming increasingly complex. The electric network partitioning method is an effective approach for implementing real‐time simulation of complex power systems. This paper proposes a novel electric network partitioning method based on state space and branch cutting, aiming to enhance efficiency and precision of real‐time simulation. In the proposed method, the entire system is partitioned into multiple subsystems through branch cutting. Each cut branch is modeled based on Kirchhoff's voltage law and the characteristics of components. Each subsystem is solved using the state‐space method, and all subsystems can be simulated in parallel. Compared with traditional network partitioning methods, the proposed method effectively reduces the computational cost resulting from integrating state‐space and branch cutting methods. Additionally, the proposed method eliminates the need for time delay, which can ensure high precision. Finally, the advantages of the proposed method are verified through several real‐time simulations of an RLC circuit, a grid‐connected inverter system with an LCL filter, and a boost‐inverter system.

Voltage support strength analysis and stability control strategy for grid‐forming wind power transmission system

AbstractIncreasing the short‐circuit ratio (SCR) of the power transmission system is crucial to ensuring voltage stability when the system has a high‐penetration of wind energy resources. This paper first analyses the weak nodes in the power system and quantifies the SCR requirements of these nodes under voltage safety constraints. Based on this analysis, a third‐order dynamic model of a virtual synchronous generator is established, and a design method for virtual transient reactance is proposed to meet the system's SCR requirements. Finally, a power system simulation with high‐penetration of wind energy is constructed, validating that under the proposed voltage stability support control strategy, grid‐forming wind turbines can meet the system's SCR requirements by optimizing the design of transient reactance, thereby improving the voltage stability of the system.

A Heterogeneous Multiscale Method for Efficient Simulation of Power Systems with Inverter-Based Resources

A Heterogeneous Multiscale Method for Efficient Simulation of Power Systems with Inverter-Based Resources

Reserve Technique in Integrating Large Sustainable Energy Sources: A Case Study of the Tunisian Grid

The increasing integration of sustainable energy sources, such as wind and solar power, into the national electricity grid presents significant challenges in terms of frequency control and grid stability. Additionally, the imbalance between electricity supply and demand introduces dynamic frequency variations. However, according to the literature, the impact of high penetration of renewable energy sources on the Tunisian grid has not been extensively analyzed using power system simulator for engineering (PSS/E). This research paper explores how the primary reserve technique participates to maintain frequency within acceptable ranges in the Tunisian electrical grid. Individual generators contribute to the total power output, thereby influencing frequency deviation. The primary frequency control action by each generator is proportional to its frequency deviation and inversely proportional to its governing drop, which measures the generator’s sensitivity to frequency changes. This paper analyzes frequency stability in the Tunisian grid under scenarios with and without different rates of sustainable energy source penetration, which barely reached 3.5% in 2023. In Tunisia, the use of sustainable energy is essential not only for ensuring grid stability but for combating climate change and reducing carbon emissions, aligning with the country’s environmental goals. The transition to sustainable energy significantly reduces the carbon footprint of the power sector, offering a sustainable solution for mitigating the adverse effects of climate change. Dynamic simulations were conducted for the isolated Tunisian system, separate from the interconnected grid, focusing on the critical scenario of the loss of a large electricity production unit. This study also examined the absence of sustainable energy integration and the effects of integration of different rates of renewable energy to evaluate the impact of reserves on the continuity of the Tunisian electrical service. Simulation results, which considered a 2023 grid model, show that with an integration trial of 20% renewable energy and, in the worst-case scenario, which represents the loss of the largest production group in the grid, the primary reserve of a given group—defined by the quantity of active energy—can be rapidly deployed to restore the balance between electricity supply and demand. Thus, reserves are a crucial solution for maintaining frequency within reasonable limits and ensuring the continuity of electrical services in Tunisia with varying rates from 10% to 20% integration of different sustainable energy sources.

EVALUATION OF THE PERFORMANCE OF αβPLL ALGORITHM UNDER ADVERSE GRID CONDITION USING PROCESSOR-IN-THE-LOOP

Solar photovoltaic (PV) is rapidly gaining traction as a significant contributor to the electrical power sector. The growing interest in integrating solar PV into the grid has sparked discussions about synchronization techniques. Ongoing research has effectively replaced smaller standalone systems with grid-connected PV systems. The appeal of grid-connected PV systems lies in their ability to harness ample solar light and utilize advanced power electronics techniques. This work evaluates the αβPLL algorithm's performance under phase jumps, frequency shifts, and presence of harmonics in grid-connected PV system. To ensure the seamless integration of PV systems with AC grids, the use of synchronization techniques like the αβPLL algorithm is indispensable. The performances of the αβPLL algorithm synchronization technique under those three conditions were evaluated experimentally and in the MATLAB/Simscape power system simulation toolbox. Both the simulation and experimental results showed that the algorithm exhibited robustness and reliability, affirming its potential for effective grid synchronization and harmonic mitigation in grid-connected PV systems.

An energy storage system with SOA-based FONPID controller for AGC study in restructured power systems

An energy storage system with SOA-based FONPID controller for AGC study in restructured power systems

An Open-Source Julia Package for RMS Time-Domain Simulations of Power Systems

This paper presents RMSPowerSims.jl, an open-source Julia package for the time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, frequency stability, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances, while also providing a range of pre-constructed models for common power system components. RMSPowerSims.jl simplifies the process of performing RMS simulations on power system models developed using the PowerModels.jl ecosystem, and provides an easy-to-use modeling that reduces the barrier to entry for new users wishing to perform RMS simulations. The accuracy of the package is verified against DIgSILENT PowerFactory for short-circuit and load-increase disturbances, using the New England 39-bus system. The active power generation delivered by several generators in the network, and the voltage magnitudes of selected busbars are analyzed and noted to be in close agreement with those obtained using PowerFactory. The computational performance of the package is compared to that of PowerFactory and is found to be comparable for load-step simulations; however, PowerFactory is found to be considerably faster for short-circuit simulations. As computational performance is not a priority at this stage of development, this is expected, and speed optimization is planned for future work. RMSPowerSims.jl is available under an open-source license and can be downloaded from GitHub.

Simulation of Faults in the Yeywa-Thaphaywa High Voltage Transmission Line using Fuzzy Logic

Faults in transmission networks frequently undermine the reliability and stability of electrical power supply, causing load shedding and posing risks to power system equipment. Effective fault classification is crucial to minimize power outages and ensure timely restoration. This study presents a fault classification method for transmission lines using fuzzy logic. The classification utilizes three-phase feeder currents, neutral current, and phase voltages as inputs to the Fuzzy Inference System (FIS). The proposed method accurately classifies the various types of faults in a high voltage transmission network, including line-to-ground fault, double line-to-ground fault, line-to-line fault, and three-phase faults. Numerous test cases demonstrate the technique's high accuracy. The simulations are carried out in MATLAB/SIMULINK using Sim Power Systems and the Fuzzy Logic Toolbox (MATLAB 2016a).

Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

Chaotic Sea-Horse Optimizer for Profit-Based Unit Commitment to Maximize the Profit of GENCOs in Restructured Power System Considering Reserve Allocation

Objectives: The primary objective of this research work is to solve the Profit Based Unit Commitment (PBUC) problem in Power Generation Companies (GENCOs) in the competitive Electricity market. The energy and spinning reserve allocation are considered to maximize the profit of power generation companies. Methods: A new and recently developed optimization technique of Chaotic Sea-Horse Optimizer (CSHO) is planned for an optimal solution PBUC problem. The control parameters of CSHO are the number of Population is 50, the Chaotic map parameter is 4, the dimensions are 10, and the maximum number of iterations is 100 are considered to achieve the global optimal solutions. The competitive study with standard golden approaches of the LR method and other soft computing techniques such as GA, PSO, etc. are considered for performance evaluations of CSHO. The proposed CSHO is mainly based on the natural behavior of Sea-Horse in the ocean. The MATLAB 15.0 platform is utilized for the expected problem. Findings: The CSHO was tested on a standard IEEE-39 bus system (10 generators and 24 hours) with reserve allocation. The CSHO technique improves the total profit of GENCOs with various test loads and reserve demand. The simulation results like Optimal UC schedule, power generation of thermal plants, fuel cost, startup cost, revenue, and profit are displayed. The percentage of total profit of 29.76% is improved when compared with the mathematical approach of the LR method with 102 second time period. Novelty: The CSHO has currently developed an effective algorithm that easily reaches the global optimal solutions. The CSHO mimics the movement, hunting, and breeding behavior of sea-horses in nature. Implementation of chaotic maps helps to improve the convergence speed of the proposed method. Keywords: Deregulation, Profit Based Unit Commitment, Energy and Reserve Allocation, Cost Minimization, Profit of GENCOs, Chaotic Sea-Horse Optimizer

A robust sliding mode control strategy for DC voltage stabilization in solar powered electric vehicle charging station

ABSTRACT Integrating DC power sources and AC grid in an electric vehicle charging station through converters can introduce oscillations, potentially leading to system instability. This paper explored the utilization of a sliding mode controller in a bidirectional DC-DC buck-boost converter for DC bus voltage control within the charging station. This controller aims to promptly respond to power fluctuations, offering robustness against variations in system parameters, external disturbances, and uncertainties. Control strategies play a critical role in mitigating DC link voltage fluctuations and ensuring power stability. The system entails a photovoltaic array employing maximum power point tracking for optimal energy harnessing. In addition, a bidirectional buck-boost converter is employed to effectively manage battery charging in buck mode and discharging in boost mode. A comparative analysis between Proportional-Integral control and sliding mode control incorporating a washout filter was conducted. This assessment is simulated and validated using MATLAB/Simulink, and the Sim Power System toolbox. The proposed model offers a comprehensive solution for integrating solar power, battery storage, and grid-based charging by enhancing overall system efficiency and reliability.

A Fuzzy Multi-Criteria Approach for Selecting Sustainable Power Systems Simulation Software in Undergraduate Education

A Fuzzy Multi-Criteria Approach for Selecting Sustainable Power Systems Simulation Software in Undergraduate Education

A novel discrete-time state-space model for real-time simulation of power system based on characteristics of capacitor and inductor

With the development of the power system, the dynamic characteristics of power systems become more and more complex because of the growing scales. As a critical tool in power system researches, the real-time simulation suffers from issues of slow speed and low accuracy. This paper proposes a novel discrete-time state-space model based on characteristics of capacitor and inductor, which aims to improve the speed and accuracy of real-time simulation in power systems. In the proposed method, the characteristic equations of capacitor and inductor are firstly discretized by numerical integration methods. Subsequently, mathematical methods and formulas are employed to derive the new discrete-time state-space model. Compared with the traditional discrete-time state-space model, the proposed method effectively reduces computational cost when applying the same implicit numerical integration method of third-order or higher. Additionally, the accuracy and speed of real-time simulation can be synthetically improved by employing higher order numerical integration methods for the selected state variables. Finally, the advantages of the proposed method are validated through three real-time simulations of an RLC circuit system, a grid-connected inverter system, and a boost-inverter system.

Comparative analysis of online voltage stability indices based on synchronized PMU measurements

The need for reliable real-time information on voltage stability margins of electrical power systems is an increasingly relevant concern within the current trend of electrification and deployment of power electronics-based devices. This paper conducts the assessment and comparison of four Voltage Stability Indices (VSIs) proposed for this application and based exclusively on synchronized phasor measurements. The robustness and accuracy of each method in identifying the point of maximum power transfer are evaluated as the correlation between load characteristics and consistent estimation of voltage stability margins is explored. In addition, the likelihood inherent to each VSI formulation of triggering false alarms under certain system dynamics is addressed in detail. The comparative analyses are derived from dynamic simulation data of a 3-bus test system, the IEEE 9-bus network and the IEEE 39-bus network, all modelled in the open-source Python-based power system simulator DynPSSimPy. Case studies cover placement of monitoring device, different load types, line disconnection events and presence of measurement noise. The results presented serve as a reference point for the development and/or enhancement of VSIs suitable for real-time applications, highlighting their most significant advantages and drawbacks and providing insights on potential trade-offs that need to be considered when employing such approaches within control centre settings.

Active power balance control of wind-photovoltaic-storage power system based on transfer learning double deep Q-network approach

IntroductionThis study addresses the challenge of active power (AP) balance control in wind-photovoltaic-storage (WPS) power systems, particularly in regions with a high proportion of renewable energy (RE) units. The goal is to effectively manage the AP balance to reduce the output of thermal power generators, thereby improving the overall efficiency and sustainability of WPS systems.MethodsTo achieve this objective, we propose the transfer learning double deep Q-network (TLDDQN) method for controlling the energy storage device within WPS power systems. The TLDDQN method leverages the benefits of transfer learning to quickly adapt to new environments, thereby enhancing the training speed of the double deep Q-network (DDQN) algorithm. Additionally, we introduce an adaptive entropy mechanism integrated with the DDQN algorithm, which is further improved to enhance the training capability of agents.ResultsThe proposed TLDDQN algorithm was applied to a regional WPS power system for experimental simulation of AP balance control. The results indicate that the TLDDQN algorithm trains agents more rapidly compared to the standard DDQN algorithm. Furthermore, the AP balance control method based on TLDDQN can more accurately manage the storage device, thereby reducing the output of thermal power generators more effectively than the particle swarm optimization-based method.DiscussionOverall, the TLDDQN algorithm proposed in this study can provide some insights and theoretical references for research in related fields, especially those requiring decision making.

Power system event detection using Teager Kaiser Energy Operator and autoencoder based algorithm

As the global demand for electricity increases, modern power systems are operating closer to their stability limits. This increases power system events that vary in intensity. It is essential to detect these power system events as early as possible and to take the necessary control actions to prevent escalation. This paper proposes a power system event detection method based on the Teager Kaiser Energy Operator (TKEO) and Autoencoder. In the proposed method, the rules-based algorithm performs primary detection, and the detected events are confirmed using the TKEO and autoencoder-based algorithms. The effectiveness of the proposed method was tested using test signals, simulated power system signals, and real-time power system data obtained from an actual power system. The results reveal the robustness and accuracy of the proposed method in detecting single and multiple power system events in the PMU data.

Preface

On behalf of the organizing committee, we are very pleased to introduce the proceedings of 2024 The 17th International Conference on Computer and Electrical Engineering (ICCEE 2024), which was held in Shenzhen, China (virtual) on June 28-30, 2024. Because more than two-thirds of the authors chose to attend the conference online, we had to switch to totally virtual conference to ensure the effectiveness of the conference. It is with great pleasure and anticipation that we convene once again to explore the latest advancements, challenges, and opportunities in the dynamic fields of Computer and Electrical Engineering. In today’s interconnected world, computer and electrical engineering play critical roles in shaping the technological landscape. ICCEE 2024 serves as a platform for fostering interdisciplinary collaboration, knowledge sharing, and networking among researchers, practitioners, and industry leaders. ICCEE 2024 lasted 3 days. There are online test sessions on ZOOM on the first day, and then followed with a wonderful array of 2 keynote speeches and 2 invited speeches, along with 4 technical sessions during June 29-30. The authors presentations cover topics on Power System Control Model and Simulation Analysis, Electrical Fault Detection and Maintenance Strategy, Digital Communication and Information Systems, Intelligent Electronic Systems and Status Monitoring, with 15 minutes for each presentation, including 2 minutes for the Q&A. Additionally, there are around 100 delegates attending online conference. Delegates had a wide range of sessions to choose from and had enough time in deciding which sessions to attend. List of Committee and Statement of Peer Review are available in this Pdf.

Investigation of Second Order Taylor Series in the Coarse Operator of Parareal Algorithm for Power System Simulation

Investigation of Second Order Taylor Series in the Coarse Operator of Parareal Algorithm for Power System Simulation

Accurate measurement-based convex model to estimate transmission loss coefficients in power systems

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 Ng generators, 0.5NgNg+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.

Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty

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.