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Electronics
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Advances in Enhancing Energy and Power System Stability and Control
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Advances in Enhancing Energy and Power System Stability and Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 8177

Special Issue Editors

Dr. Libao Shi

E-Mail Website
Guest Editor
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: complementary and coordinated dispatch technologies with multi-energy source structure; risk assessment in cyber-physical power systems; power system cascading failure and restoration control; computational intelligence and its application in smart grid; power system stability and control
Dr. Ren Wang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: trustworthy machine learning; data-driven methods in power systems; smart grids

Special Issue Information

Dear Colleagues,

With the rapid development of clean energy and power electronic equipment technology, energy and power systems will face unprecedented and profound changes. How to promote clean power generation technology, build a low-carbon clean energy system and ensure energy security are significant tasks in modern power grid development. At the same time, the nonlinearity, uncertainty, time variability and complexity of the system are constantly increasing, which not only puts forward higher requirements for the reliability and flexibility of system operation and control, but also brings greater challenges to the safety and stability of the new energy and power systems in the future. Exploring and exploiting the corresponding security assessment model and advanced control strategy will effectively reduce the risks associated with a high share of clean energy and power electric equipment and further improve the stability and controllability of the energy and power systems.

The purpose of this Special Issue aims to highlight the novel and most recent advances in theory, modeling and applications of energy and power system security assessment and control to better promote the construction and development of low-carbon clean energy and power systems. The Special Issue welcomes original articles that may focus on (but not limited to):

  1. Modeling analysis of energy and power system security assessment and control
  2. Transient stability analysis of energy and power systems
  3. Frequency stability analysis of energy and power systems
  4. Voltage stability analysis of energy and power systems
  5. Small-signal stability analysis of energy and power systems
  6. Subsynchronous torsional oscillation analysis of energy and power systems
  7. Resilience assessment of energy and power systems
  8. Data-driven technology-based energy and power system security assessment and control
  9. AI-based energy and power system stability analysis
  10. Control and protection strategies for power electronic-based energy and power systems
  11. Risk assessment and management of energy and power systems against extreme events
  12. Stability-constrained optimal planning and operation of energy and power systems

Dr. Libao Shi
Dr. Ren Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • power system dynamics
  • risk assessment
  • clean energy
  • resilience assessment
  • AI methods
  • data-driven technology
  • modeling analysis

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Published Papers (10 papers)

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Research

17 pages, 5654 KiB  
Article
A Short-Term Power Prediction Method for Photovoltaics Based on Similar Day Clustering and Spatio-Temporal Feature Extraction
by Xu Huang, Leying Wang, Leijiao Ge, Luyang Hou, Tianshuo Du, Yiwen Zheng and Yanbo Chen
Electronics 2024, 13(17), 3536; https://doi.org/10.3390/electronics13173536 - 6 Sep 2024
Viewed by 262
Abstract
Accurate PV power prediction is crucial for enhancing grid planning, optimizing dispatch operations, and advancing management strategies. In pursuit of this objective, this study proposes a short-term distributed PV power prediction method that incorporates temporal and spatial feature extraction as well as similar [...] Read more.
Accurate PV power prediction is crucial for enhancing grid planning, optimizing dispatch operations, and advancing management strategies. In pursuit of this objective, this study proposes a short-term distributed PV power prediction method that incorporates temporal and spatial feature extraction as well as similar day analysis. Firstly, to address the poor adaptability of traditional clustering methods to time-series data, the K-shape clustering algorithm is employed to categorize the time series into different weather types. Secondly, to overcome the challenges posed by varying time resolutions in similar day analysis, a novel method based on Dynamic Time Warping (DTW) is proposed. This method calculates the similarity between the target days and the days to be collected, considering both the time of day and the day of the week. Subsequently, a PV power generation prediction model based on a convolutional long short-term memory (CNN-LSTM) network is developed to enhance prediction accuracy. To tackle the difficulty of manual hyperparameter tuning, the chaos reverse sparrow search algorithm (CRSSA) is introduced. Finally, a case study is conducted on the measured data of a distributed photovoltaic power station in a certain region of China. By comparing RMSE and MAPE, compared with other prediction models, the proposed prediction model and solving algorithm effectively reduced the relative error by more than 1%, verifying the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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18 pages, 5699 KiB  
Article
Transient Synchronous Stability Modeling and Comparative Analysis of Grid-Following and Grid-Forming New Energy Power Sources
by Xin Tian, Yuyue Zhang, Yanhui Xu, Le Zheng, Lina Zhang and Zhenhua Yuan
Electronics 2024, 13(16), 3308; https://doi.org/10.3390/electronics13163308 - 21 Aug 2024
Viewed by 388
Abstract
New energy power sources can be categorized into grid-following and grid-forming types based on their synchronization characteristics with the grid. Due to the different basic control operation principles of these two types of new energy power sources, they can lead to the instability [...] Read more.
New energy power sources can be categorized into grid-following and grid-forming types based on their synchronization characteristics with the grid. Due to the different basic control operation principles of these two types of new energy power sources, they can lead to the instability of the grid-connected system through different paths. A mathematical model is established to describe the dynamic characteristics of the grid-following and the grid-forming new energy power resources, and the control block diagram of the nonlinear dynamic characteristic equation is compared and analyzed. The similarity between grid-following and grid-forming new energy power supply is revealed, and we preliminarily discuss the influencing factors of the stability of new energy sources. Using a single-machine infinite system model, the influence of key control parameters on transient stability is separately investigated for grid-following and grid-forming types. The influence of the converter synchronous loop control parameters on the system is verified, and the factors affecting the stability are discussed in combination with current limiting and frequency limiting. The research will provide reference for the design of a new energy power grid connection system. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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15 pages, 2839 KiB  
Article
Zero-Power Control Strategy and Dynamics Enhancement for Hybrid Maglev Conveyor Cart
by Xiaowei Tang, Seiji Hashimoto, Takahiro Kawaguchi and Nobuyuki Kurita
Electronics 2024, 13(15), 2921; https://doi.org/10.3390/electronics13152921 - 24 Jul 2024
Viewed by 580
Abstract
This paper presents a novel zero-power controller applied to a four-unit magnetic levitation system, aimed at addressing the challenge of maintaining stability under disturbance loads. The zero-power controller, designed based on a state feedback controller integrated with a position servo integrator, is primarily [...] Read more.
This paper presents a novel zero-power controller applied to a four-unit magnetic levitation system, aimed at addressing the challenge of maintaining stability under disturbance loads. The zero-power controller, designed based on a state feedback controller integrated with a position servo integrator, is primarily employed to control the balance of the magnetic levitation (Maglev) unit and eliminate steady-state errors. Subsequently, the zero-power controller operates after the state feedback controller to adjust the Maglev unit to a new equilibrium point, primarily utilizing permanent magnetic force to suspend against gravitational input. When loads change or disturbances occur, the system generates current to maintain balance. All designs have passed validation. Experimental results demonstrate the improved zero-power performance and disturbance rejection capabilities of the proposed Maglev system. During synchronous operation, dynamic characteristics have shown significant improvement, which has been experimentally confirmed. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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18 pages, 825 KiB  
Article
Decentralized Retrofit Model Predictive Control of Inverter-Interfaced Small-Scale Microgrids
by Milad Shojaee and S. Mohsen Azizi
Electronics 2024, 13(15), 2914; https://doi.org/10.3390/electronics13152914 - 24 Jul 2024
Viewed by 398
Abstract
In recent years, small-scale microgrids have become popular in the power system industry because they provide an efficient electrical power generation platform to guarantee autonomy and independence from the power grid, which is a critical feature in cases of catastrophic events or remote [...] Read more.
In recent years, small-scale microgrids have become popular in the power system industry because they provide an efficient electrical power generation platform to guarantee autonomy and independence from the power grid, which is a critical feature in cases of catastrophic events or remote areas. On the other hand, due to the short distances among multiple distribution generation systems in small-scale microgrids, the interconnection couplings among them increase significantly, which jeopardizes the stability of the entire system. Therefore, this work proposes a novel method to design decentralized robust controllers based on a retrofit model predictive control scheme to tackle the issue of instability due to the short distances among generation systems. In this approach, the retrofit model predictive controller receives the measured feedback signal from the interconnection current and generates a control command signal to limit the interconnection current to prevent instability. To design a retrofit controller, only the model of a robust closed-loop system, as well as an interconnection line, is required. The model predictive control signal is added in parallel to the control signal from the existing robust voltage source inverter controller. Simulation results demonstrate the superior performance of the proposed technique as compared with the virtual impedance and retrofit linear quadratic regulator techniques (benchmarks) with respect to peak-load demand, plug-and-play capability, nonlinear load, and inverter efficiency. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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14 pages, 5374 KiB  
Article
Physics-Informed Neural Network-Based VSC Back-to-Back HVDC Impedance Model and Grid Stability Estimation
by Minhyeok Chang, Yoongun Jung, Seokjun Kang and Gilsoo Jang
Electronics 2024, 13(13), 2590; https://doi.org/10.3390/electronics13132590 - 1 Jul 2024
Viewed by 660
Abstract
With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, conducting such analyses across multiple operating points requires abundant [...] Read more.
With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, conducting such analyses across multiple operating points requires abundant impedance measurement data from power electronic devices. In this paper, we propose a method for constructing impedance models of equipment with fewer impedance measurement data in voltage-source converter (VSC) back-to-back high-voltage direct current (HVDC) systems using physics-informed neural networks. Furthermore, given the power system states, we present a neural network approach to estimate grid stability at different operating points. Validation via PSCAD/EMTDC simulations and a PyTorch neural network confirmed the adequacy of these models. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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14 pages, 11620 KiB  
Article
Multi-Time-Scale Energy Storage Optimization Configuration for Power Balance in Distribution Systems
by Qiuyu Lu, Xiaoman Zhang, Yinguo Yang, Qianwen Hu, Guobing Wu, Yuxiong Huang, Yang Liu and Gengfeng Li
Electronics 2024, 13(7), 1379; https://doi.org/10.3390/electronics13071379 - 5 Apr 2024
Viewed by 1121
Abstract
As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the [...] Read more.
As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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14 pages, 3948 KiB  
Article
System Strength Reduction in an Island Grid through Transitioning to 100% Inverter-Based Resources
by Misael Rodríguez Hernández and Alexandre B. Nassif
Electronics 2024, 13(7), 1225; https://doi.org/10.3390/electronics13071225 - 26 Mar 2024
Viewed by 515
Abstract
Puerto Rico, an island heavily reliant on fossil fuels for primary electricity generation, faces challenges stemming from inadequate preventative maintenance, leading to an intermittently insufficient generation mix to meet overall load demand. Media coverage, exemplified by the Department of Energy PR100 study, delineates [...] Read more.
Puerto Rico, an island heavily reliant on fossil fuels for primary electricity generation, faces challenges stemming from inadequate preventative maintenance, leading to an intermittently insufficient generation mix to meet overall load demand. Media coverage, exemplified by the Department of Energy PR100 study, delineates a strategic roadmap for transitioning Puerto Rico to achieve 100% renewable energy generation. This shift aims not only to mitigate dependence on fossil fuels but also to replace outdated conventional plants. Integrating inverter-interfaced renewable generation into the grid introduces a challenge, as these resources cannot match the short-circuit levels typically supplied by rotational synchronous generation. Complexity arises in determining whether existing protection schemes can maintain dependability during this transition or whether upgrades, such as adjustments to protection settings or philosophical enhancements, are imperative. This paper addresses this challenge by evaluating system strength at different stages of incorporating utility-scale renewable shares in the island system. It discerns the reduction in short-circuit currents for both three-phase faults and single-line-to-ground faults as conventional plants are phased out in favor of inverter-based resources. This research work also quantifies the impact of synchronous condensers and STATCOMs as a solution to strengthen the grid and increase short-circuit levels. This research equips the transmission operator with valuable insights into the necessary future system modifications to ensure the dependability and safety of the grid. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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17 pages, 2246 KiB  
Article
Distributed Feature Selection for Power System Dynamic Security Region Based on Grid-Partition and Fuzzy-Rough Sets
by Yefa Tan, Zhaobin Du, Weixian Zhou and Baixi Chen
Electronics 2024, 13(5), 815; https://doi.org/10.3390/electronics13050815 - 20 Feb 2024
Cited by 1 | Viewed by 886
Abstract
In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive [...] Read more.
In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive model training costs and overfitting when determining DSR boundaries with high-dimensional grid features. Given this problem, a distributed feature selection method based on grid partition and fuzzy-rough sets is proposed in this paper. The method first employs the Louvain algorithm to partition the power grid and divide the original feature set so that high-dimensional features can be allocated to multiple computational units for distributed screening. At this point, the connections between features of different computational units are minimized to a relatively low level, thereby avoiding large errors in the distributed results. Then, an incremental search algorithm based on the fuzzy-rough set theory (FRST) is used for feature selection at each computational unit, which can effectively take into account the intrinsic connections between features. Finally, the results of all computational units are integrated in the coordination unit to complete the overall feature selection. The experimental results based on the IEEE-39 bus system show that the proposed method can help simplify the power system DSR analysis with high-dimensional features by screening the critical features. And compared with other commonly used filter methods, it has higher screening accuracy and lower time costs. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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21 pages, 3410 KiB  
Article
Multi-Indicator Fused Resilience Assessment of Power Grids Considering Wind-Photovoltaic Output Uncertainty during Typhoon Disasters
by Wanlin Wang, Libao Shi and Zongxu Qiu
Electronics 2024, 13(4), 745; https://doi.org/10.3390/electronics13040745 - 13 Feb 2024
Viewed by 1087
Abstract
Extreme weather events such as typhoons pose a serious threat to the safe operation of power grids. In the field of power system resilience assessment during typhoon disasters, a parametric typhoon wind field model combined with actual historical meteorological data has not been [...] Read more.
Extreme weather events such as typhoons pose a serious threat to the safe operation of power grids. In the field of power system resilience assessment during typhoon disasters, a parametric typhoon wind field model combined with actual historical meteorological data has not been well adopted, and the conventional renewable energy uncertainty modeling methods are not suitable for typhoon disaster periods. In this paper, a multi-indicator fused resilience assessment strategy considering wind-photovoltaic uncertainty and component failure during typhoon disasters is proposed. Firstly, based on the actual historical meteorological data of typhoons, an uncertainty model of typhoon wind speed is established by a rolling non-parametric Dirichlet process Gaussian mixture model. Then, a spatial–temporal contingency set is constructed by considering the best-fit wind field model and stress–strength interference model for failure probability of transmission lines. On this basis, a holistic resilience assessment framework is established from the perspectives of priority, robustness, rapidity, and sustainability, and the entropy weight method combined with the technology for order preference by similarity to an ideal solution is leveraged to obtain the comprehensive resilience indicator. Finally, numerical studies are performed on the IEEE-30 bus test system to identify vulnerable lines and improve system resilience during typhoon disasters. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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17 pages, 2545 KiB  
Article
A Comprehensive Analysis of PINNs for Power System Transient Stability
by Ignacio de Cominges Guerra, Wenting Li and Ren Wang
Electronics 2024, 13(2), 391; https://doi.org/10.3390/electronics13020391 - 17 Jan 2024
Viewed by 1510
Abstract
The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face [...] Read more.
The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45. Full article
(This article belongs to the Special Issue Advances in Enhancing Energy and Power System Stability and Control)
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