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Stability Analysis and Optimal Operation Strategy for Power Systems with...
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Stability Analysis and Optimal Operation Strategy for Power Systems with High Renewable Energy Penetration

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 691

Special Issue Editors

Dr. Sunhua Huang

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: power system stability analysis and control; AD/DC microgrid control
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Zhou

E-Mail Website
Guest Editor
School of Electrical & Information Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: operation and control of hybrid AC/DC power systems; energy storage systems control; flexibility of energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jiapeng Li

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710048, China
Interests: protection and control of power systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Wang

E-Mail Website
Guest Editor
College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: control and operation of new type power system; smart operation of virtual power plant; renewable energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global transition toward low-carbon energy has led to a rapid increase in the penetration of renewable energy sources, such as wind and solar power, into power systems. These resources are typically interfaced to the grid through power electronic converters, which fundamentally alter the dynamic characteristics and operational behavior of modern power systems. As the proportion of power electronic-interfaced generation continues to grow, traditional assumptions regarding system inertia, damping, and stability no longer hold, posing significant challenges for secure and efficient power system operation. This Special Issue aims to provide a platform for researchers and practitioners to present the latest advances in the modeling, analysis, control, and optimization of power systems with high shares of power electronics. Topics of interest include both theoretical developments and practical implementations, covering small-signal and large-signal stability, frequency and voltage control, the coordinated operation of diverse converter-based resources, and grid-forming control strategies.

Topics of interest include, but are not limited to:

  1. Modeling and stability analysis of power electronic-dominated power systems;
  2. Grid-forming and grid-following control strategies and their impact on system dynamics;
  3. Multi-timescale stability: transient, small-signal, and long-term stability;
  4. Coordinated control and optimization of inverter-based resources (IBRs);
  5. Interaction between power electronic devices and synchronous machines;
  6. Advanced frequency and voltage control in low-inertia systems;
  7. Stability assessment and control of hybrid AC/DC systems with VSC-HVDC;
  8. Data-driven and AI-based approaches for stability prediction and operation optimization;
  9. Cyber–physical security and resilience of power electronic-dominated power systems;
  10. Stability analysis and control for the AC/DC microgrid.

Dr. Sunhua Huang
Dr. Yang Zhou
Dr. Jiapeng Li
Dr. Jian 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 systems
  • renewable energy penetration
  • inverter-based resources
  • grid
  • AC/DC
  • stability analysis
  • optimal operation strategy

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Published Papers (2 papers)

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Research

29 pages, 5989 KiB  
Article
Risk Analysis Method of Aviation Critical System Based on Bayesian Networks and Empirical Information Fusion
by Xiangjun Dang, Yongxuan Shao, Haoming Liu, Zhe Yang, Mingwen Zhong, Maohua Sun and Wu Deng
Electronics 2025, 14(12), 2496; https://doi.org/10.3390/electronics14122496 - 19 Jun 2025
Viewed by 161
Abstract
The intrinsic hazards associated with high-pressure hydrogen, combined with electromechanical interactions in hybrid architectures, pose significant challenges in predicting potential system risks during the conceptual design phase. In this paper, a risk analysis methodology integrating systems theoretic process analysis (STPA), D-S evidence theory, [...] Read more.
The intrinsic hazards associated with high-pressure hydrogen, combined with electromechanical interactions in hybrid architectures, pose significant challenges in predicting potential system risks during the conceptual design phase. In this paper, a risk analysis methodology integrating systems theoretic process analysis (STPA), D-S evidence theory, and Bayesian networks (BN) is established. The approach employs STPA to identify unsafe control actions and analyze their loss scenarios. Subsequently, D-S evidence theory quantifies the likelihood of risk factors, while the BN model’s nodal uncertainties to construct a risk network identifying critical risk-inducing events. This methodology provides a comprehensive risk analysis process that identifies systemic risk elements, quantifies risk probabilities, and incorporates uncertainties for quantitative risk assessment. These insights inform risk-averse design decisions for hydrogen–electric hybrid powered aircraft. A case study demonstrates the framework’s effectiveness. The approach bridges theoretical risk analysis with early-stage engineering practice, delivering actionable guidance for advancing zero-emission aviation. Full article
(This article belongs to the Special Issue Stability Analysis and Optimal Operation Strategy for Power Systems with High Renewable Energy Penetration)
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19 pages, 1566 KiB  
Article
Short-Term Power Load Forecasting Based on the Quantum Genetic Algorithm Artificial Recurrent Memory Network
by Qian Zhang, Yang Zhou, Sunhua Huang, Chenyang Guo, Linyun Xiong, Shuaihu Li, Yong Li and Yijia Cao
Electronics 2025, 14(12), 2417; https://doi.org/10.3390/electronics14122417 - 13 Jun 2025
Viewed by 345
Abstract
Accurate power load forecasting is crucial for maintaining the equilibrium between power supply and demand and for safeguarding the stability of power systems. Through a comprehensive optimization of both the parameters and structure of the traditional load forecasting model, this study developed a [...] Read more.
Accurate power load forecasting is crucial for maintaining the equilibrium between power supply and demand and for safeguarding the stability of power systems. Through a comprehensive optimization of both the parameters and structure of the traditional load forecasting model, this study developed a short-term power load prediction model (QGA-RMNN) based on a quantum genetic algorithm to optimize an artificial recurrent memory network. The model utilizes the principle of quantum computing to improve the search mechanism of the genetic algorithm. It also combines the memory characteristics of the recurrent neural network, combining the advantages of the maturity and stability of traditional algorithms, as well as the intelligence and efficiency of advanced algorithms, and optimizes the memory, input, and output units of the LSTM network by using the artificial excitation network, thus improving the prediction accuracy. Then, the hyperparameters of the RMNN are optimized using quantum genetics. After that, the proposed prediction model was rigorously validated using case studies employing load datasets from a microgrid and the Elia grid in Belgium, Europe, and was compared and analyzed against the classical LSTM, GA-RBF, GM-BP, and other algorithms. Compared to existing algorithms, the results show that this model demonstrates significant advantages in predictive performance, offering an effective solution for enhancing the accuracy and stability of load forecasting. Full article
(This article belongs to the Special Issue Stability Analysis and Optimal Operation Strategy for Power Systems with High Renewable Energy Penetration)
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