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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: 15 September 2024 | Viewed by 3056

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

Published Papers (5 papers)

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Research

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 - 05 Apr 2024
Viewed by 379
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 248
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
Viewed by 545
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 639
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 787
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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