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Trends and Challenges in Power System Stability and Control
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Trends and Challenges in Power System Stability and Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 5276

Special Issue Editors

Prof. Dr. Ke Xu

E-Mail Website
Guest Editor
Department of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Interests: power system control; smart grid; power system protection; renewable energy; AI in power system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuan Liao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: energy management system; intelligent techniques; power quality Monitoring and analysis; power system economics and optimization; protective relaying security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the energy sector undergoes rapid evolution, power systems worldwide face unprecedented challenges. The integration of renewable energy sources, the advent of distributed generation, and the growing demands of modern infrastructure are reshaping how power systems are designed, operated, and controlled.

This Special Issue aims to highlight the latest trends, research, and innovations in the field of power system stability and control.

Prof. Dr. Ke Xu
Prof. Dr. Yuan Liao
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. Energies 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 2600 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 control
  • power system stability
  • power system operation
  • renewable energy resources
  • intelligent techniques
  • power system analysis
  • power system protection
  • renewable energy integration
  • smart grid
  • microgrid

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Published Papers (7 papers)

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Research

26 pages, 13524 KiB  
Article
ANN-Based Maximum Power Tracking for a Grid-Synchronized Wind Turbine-Driven Doubly Fed Induction Generator Fed by Matrix Converter
by Mohamed A. Alarabi and Sedat Sünter
Energies 2025, 18(10), 2521; https://doi.org/10.3390/en18102521 - 13 May 2025
Viewed by 232
Abstract
The integration of renewable energy sources, such as wind power, into the electrical grid is essential for the development of sustainable energy systems. Doubly fed induction generators (DFIGs) have been significantly utilized in wind energy conversion systems (WECSs) because of their efficient power [...] Read more.
The integration of renewable energy sources, such as wind power, into the electrical grid is essential for the development of sustainable energy systems. Doubly fed induction generators (DFIGs) have been significantly utilized in wind energy conversion systems (WECSs) because of their efficient power generation and variable speed operation. However, optimizing wind power extraction at variable wind speeds remains a major challenge. To address this, an artificial neural network (ANN) is adopted to predict the optimal shaft speed, ensuring maximum power point tracking (MPPT) for a wind energy-driven DFIG connected to a matrix converter (MC). The DFIG is controlled via field-oriented control (FOC), which allows independent power output regulation and separately controls the stator active and reactive power components. Through its compact design, bidirectional power flow, and enhanced harmonic performance, the MC, which is controlled by the simplified Venturini modulation technique, improves the efficiency and dependability of the system. Simulation outcomes confirm that the ANN-based MPPT enhances the power extraction efficiency and improves the system performance. This study shows how wind energy systems can be optimized for smart grids by integrating advanced control techniques like FOC and simplified Venturini modulation with intelligent algorithms like ANN. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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31 pages, 10538 KiB  
Article
Comprehensive Control Strategy for Hybrid Energy Storage System Participating in Grid Primary Frequency Regulation
by Haorui Jiang, Kuihua Han, Weiyu Bao and Yahui Li
Energies 2025, 18(10), 2423; https://doi.org/10.3390/en18102423 - 8 May 2025
Viewed by 214
Abstract
The increasing integration of renewable energy sources has posed significant challenges to grid frequency stability. To maximize the advantages of energy storage in primary frequency regulation, this paper proposes a comprehensive control strategy for a hybrid energy storage system (HESS) based on supercapacitor [...] Read more.
The increasing integration of renewable energy sources has posed significant challenges to grid frequency stability. To maximize the advantages of energy storage in primary frequency regulation, this paper proposes a comprehensive control strategy for a hybrid energy storage system (HESS) based on supercapacitor battery. Firstly, considering the characteristics of the HESS and different control strategies, the battery responds to virtual droop control to reduce frequency deviation, while the supercapacitor responds to inertia control to suppress frequency drops and facilitate frequency recovery. Simultaneously, a reasonable dynamic dead zone is configured to prevent frequent actions of the battery and thermal unit while allowing flexible adjustments according to the load condition. Thirdly, an algebraic S-curve-based adaptive droop coefficient incorporating SOC is proposed, while the inertia coefficient additionally considers load type, enhancing adaptability. Furthermore, to better maintain the battery’s SOC, an improved adaptive recovery strategy within the battery dead zone is proposed, considering both SOC recovery requirements and system frequency deviation constraints. Finally, a simulation validation was conducted in MATLAB/Simulink. Compared to the conventional strategy, the proposed control strategy reduces the frequency drop rate by 17.43% under step disturbance. Under compound disturbances, the RMS of frequency deviation decreases by 13.34%, and the RMS of battery SOC decreases by 68.61%. The economic benefit of this strategy is 3.212 times that of the single energy storage scheme. The results indicate that the proposed strategy effectively alleviates sudden frequency disturbances, suppresses frequency fluctuations, and reduces battery output while maintaining the SOC of both the supercapacitor and the battery, thereby extending the battery lifespan and improving economic performance. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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25 pages, 1167 KiB  
Article
Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids
by Alessandro Niccolai, Silvia Trimarchi, Lisa Francesca Barbazza, Alessandro Gandelli, Riccardo Zich, Francesco Grimaccia and Sonia Leva
Energies 2025, 18(9), 2384; https://doi.org/10.3390/en18092384 - 7 May 2025
Viewed by 164
Abstract
The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to [...] Read more.
The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to identify efficient scheduling plans and improve performance. In this paper, a new approach based on evolutionary algorithms (EAs) is designed, implemented, and tested on a real microgrid architecture to evaluate its effectiveness. The proposed approach effectively combines heuristic information with the optimization capabilities of EAs, achieving excellent results with reasonable computational effort. The proposed system is highly flexible, making it applicable to different network architectures and various objective functions. In this work, the optimization algorithm directly manages the microgrid Energy Management System, allowing for a large number of degrees of freedom that can be exploited to achieve highly competitive solutions. This method was compared with a standard scheduling approach, and an average improvement of 11.87% in fuel consumption was achieved. After analyzing the differences between the solutions obtained, the importance of the features introduced with this new approach was demonstrated. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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19 pages, 4832 KiB  
Article
Analysis and Control Parameters Optimization of Wind Turbines Participating in Power System Primary Frequency Regulation with the Consideration of Secondary Frequency Drop
by Ketian Liu, Zhengxi Chen, Xiang Li and Yi Gao
Energies 2025, 18(6), 1317; https://doi.org/10.3390/en18061317 - 7 Mar 2025
Viewed by 493
Abstract
With the increasing integration of wind energy into power systems, maintaining frequency stability has become a significant challenge. To address the issue of secondary frequency drop caused by wind turbines exiting the primary frequency regulation of power systems, this paper presents a control [...] Read more.
With the increasing integration of wind energy into power systems, maintaining frequency stability has become a significant challenge. To address the issue of secondary frequency drop caused by wind turbines exiting the primary frequency regulation of power systems, this paper presents a control parameters optimization method of wind turbines participating in power system primary frequency regulation. Initially, with the assumption of constant wind speed and linearization of the wind power coefficient, the relationship between the mechanical power and rotor speed of the wind turbines is established. Subsequently, the primary frequency regulation component of wind turbines is integrated into the classical system frequency response (SFR) model, accounting for the effects of exiting time and rotor speed variations. Following this, the dynamic frequency of the power system is computed with the modified SFR model, and the time domain expressions for both primary and secondary frequency drops are derived. Furthermore, an optimization model for the control parameters of wind turbines participating in primary frequency regulation is developed, aiming to minimize the values both of primary and secondary frequency drops. Finally, a case study is constructed to validate the efficacy of the proposed method. The results demonstrate that the optimization method introduced in this paper significantly enhances the dynamic characteristics of the system frequency. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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14 pages, 3177 KiB  
Article
Identification and Correction of Abnormal, Incomplete Power Load Data in Electricity Spot Market Databases
by Jingjiao Li, Yifan Lv, Zhou Zhou, Zhiwen Du, Qiang Wei and Ke Xu
Energies 2025, 18(1), 176; https://doi.org/10.3390/en18010176 - 3 Jan 2025
Cited by 1 | Viewed by 720
Abstract
The development of electricity spot markets necessitates more refined and accurate load forecasting capabilities to enable precise dispatch control and the creation of new trading products. Accurate load forecasting relies on high-quality historical load data, with complete load data serving as the cornerstone [...] Read more.
The development of electricity spot markets necessitates more refined and accurate load forecasting capabilities to enable precise dispatch control and the creation of new trading products. Accurate load forecasting relies on high-quality historical load data, with complete load data serving as the cornerstone for both forecasting and transactions in electricity spot markets. However, historical load data at the distribution network or user level often suffers from anomalies and missing values. Data-driven methods have been widely adopted for anomaly detection due to their independence from prior expert knowledge and precise physical models. Nevertheless, single network architectures struggle to adapt to the diverse load characteristics of distribution networks or users, hindering the effective capture of anomaly patterns. This paper proposes a PLS-VAE-BiLSTM-based method for anomaly identification and correction in load data by combining the strengths of Variational Autoencoders (VAE) and Bidirectional Long Short-Term Memory Networks (BiLSTM). This method begins with data preprocessing, including normalization and preliminary missing value imputation based on Partial Least Squares (PLS). Subsequently, a hybrid VAE-BiLSTM model is constructed and trained on a loaded dataset incorporating influencing factors to learn the relationships between different data features. Anomalies are identified and corrected by calculating the deviation between the model’s reconstructed values and the actual values. Finally, validation on both public and private datasets demonstrates that the PLS-VAE-BiLSTM model achieves average performance metrics of 98.44% precision, 94% recall rate, and 96.05% F1 score. Compared with VAE-LSTM, PSO-PFCM, and WTRR models, the proposed method exhibits superior overall anomaly detection performance. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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10 pages, 1793 KiB  
Article
Power Dispatching Strategy Considering the Health Status of Multi-Energy Conversion Equipment in Highway Power Supply Systems
by Xianhong Hou, Jiao Wang, Shaoyong Guo and Ketian Liu
Energies 2024, 17(17), 4499; https://doi.org/10.3390/en17174499 - 8 Sep 2024
Viewed by 890
Abstract
In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms [...] Read more.
In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms of the highway power supply system are introduced, and the structure of the multi-energy conversion equipment, the topological structures of the DC–DC and DC–AC modules, and the operating characteristics are analyzed. Secondly, the module temperatures and output voltages are used as main parameters to establish the health indexes of DC–DC and DC–AC modules, and then the health index of the multi-energy conversion equipment is further calculated. Thirdly, the new energy consumption index is defined, and a multi-objective optimization model for power dispatching of highway power supply systems is established with the goal of improving the health index of multi-energy conversion equipment and the new energy consumption index. The case study shows that the power dispatching strategy in this paper can better control the temperature of each module, improve the health status of multi-energy conversion equipment, and have a high level of new energy consumption. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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13 pages, 1640 KiB  
Article
Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response
by Lu Wei, Yiyin Li, Boyu Xie, Ke Xu and Gaojun Meng
Energies 2024, 17(6), 1286; https://doi.org/10.3390/en17061286 - 7 Mar 2024
Cited by 1 | Viewed by 1103
Abstract
With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid [...] Read more.
With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits. Full article
(This article belongs to the Special Issue Trends and Challenges in Power System Stability and Control)
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