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

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

Deadline for manuscript submissions: 15 June 2026 | Viewed by 7171

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Special Issue Editors

Dr. Sunhua Huang

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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

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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
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Dr. Jiapeng Li

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Guest Editor

School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: application of artificial intelligence in power systems
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Dr. Jian Wang

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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
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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:

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

Dr. Sunhua Huang
Dr. Yang Zhou
Dr. Jiapeng Li
Dr. Jian Wang
Guest Editors

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Keywords

power systems
renewable energy penetration
inverter-based resources
grid
AC/DC
stability analysis
optimal operation strategy

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Published Papers (8 papers)

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Research

22 pages, 4405 KB

Open AccessArticle

Neural Network-Based Submodule Capacitance Monitoring in Modular Multilevel Converters for Renewable Energy Conversion Systems

by Mustapha Asnoun, Adel Rahoui, Koussaila Mesbah, Boussad Boukais, David Frey, Idris Sadli and Seddik Bacha

Electronics 2026, 15(7), 1486; https://doi.org/10.3390/electronics15071486 - 2 Apr 2026

Viewed by 443

Abstract

The widespread development of medium-voltage and high-voltage direct current transmission systems has highlighted the modular multilevel converter (MMC) as a crucial enabling technology. However, the overall performance and lifetime of the MMC strongly depend on the integrity of its submodules (SMs), making online capacitance condition monitoring a critical requirement. Unlike recent related studies that rely on computationally heavy matrix-based algorithms or “black-box” artificial neural networks requiring massive offline training datasets, this paper proposes a parametric, adaptive linear neuron network. Mapped directly to the physical equations of the MMC, the method simultaneously exploits the arm current, SM switching state, and capacitor voltage to identify online parametric variations caused by aging or harsh conditions. The proposed scheme is fully non-intrusive, requiring no additional hardware sensors or signal injections, thereby reducing implementation complexity. The simulation results obtained in MATLAB/Simulink (vR2024b) demonstrate the method’s fast convergence and a quantified steady-state estimation error within ±1%. Furthermore, the estimator exhibits strong robustness under severe operating conditions, successfully maintaining accuracy during a 20% capacitance reduction, a 100% active power step variation, dc-link voltage fluctuations, measurement noise, grid unbalances, and harmonic perturbations. 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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32 pages, 12307 KB

Open AccessArticle

An SST-Based Emergency Power Sharing Architecture Using a Common LVDC Feeder for Hybrid AC/DC Microgrid Clusters and Segmented MV Distribution Grids

by Sergio Coelho, Joao L. Afonso and Vitor Monteiro

Electronics 2026, 15(3), 496; https://doi.org/10.3390/electronics15030496 - 23 Jan 2026

Viewed by 644

Abstract

The growing incorporation of distributed energy resources (DER) in power distribution grids, although pivotal to the energy transition, increases operational variability and amplifies the exposure to disturbances that can compromise resilience and the continuity of service during contingencies. Addressing these challenges requires both a shift toward flexible distribution architectures and the adoption of advanced power electronics interfacing systems. In this setting, this paper proposes a resilience-oriented strategy for medium-voltage (MV) distribution systems and clustered hybrid AC/DC microgrids interfaced through solid-state transformers (SSTs). When a fault occurs along an MV feeder segment, the affected microgrids naturally transition to islanded operation. However, once their local generation and storage become insufficient to sustain autonomous operation, the proposed framework reconfigures the power routing within the cluster by activating an emergency low-voltage DC (LVDC) power path that bypasses the faulted MV section. This mechanism enables controlled power sharing between microgrids during prolonged MV outages, ensuring the supply of priority loads without oversizing SSTs or reinforcing existing infrastructure. Experimental validation on a reduced-scale SST prototype demonstrates stable grid-forming and grid-following operation. The reliability of the proposed scheme is supported by both steady-state and transient experimental results, confirming accurate voltage regulation, balanced sinusoidal waveforms, and low current tracking errors. All tests were conducted at a switching frequency of 50 kHz, highlighting the robustness of the proposed architecture under dynamic operation. 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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25 pages, 31548 KB

Open AccessArticle

Large-Signal Stability Analysis of VSC-HVDC System Based on T-S Fuzzy Model and Model-Free Predictive Control

by Zhaozun Sun, Yalan He, Zhe Cao, Jingrui Jiang, Tongkun Li, Pizheng Tan, Kaixuan Mei, Shujie Gu, Tao Yu, Jiashuo Zhang and Linyun Xiong

Electronics 2026, 15(2), 492; https://doi.org/10.3390/electronics15020492 - 22 Jan 2026

Cited by 1 | Viewed by 455

Abstract

Voltage source converter-based–high voltage direct current (VSC-HVDC) systems exhibit strong nonlinear characteristics that dominate their dynamic behavior under large disturbances, making large-signal stability assessment essential for secure operation. This paper proposes a large-signal stability analysis framework for VSC-HVDC systems. The framework combines a unified Takagi–Sugeno (T–S) fuzzy model with a model-free predictive control (MFPC) scheme to enlarge the estimated domain of attraction (DOA) and bring it closer to the true stability region. The global nonlinear dynamics are captured by integrating local linear sub-models corresponding to different operating regions into a single T–S fuzzy representation. A Lyapunov function is then constructed, and associated linear matrix inequality (LMI) conditions are derived to certify large-signal stability and estimate the DOA. To further reduce the conservatism of the LMI-based iterative search, we embed a genetic-algorithm-based optimizer into the model-free predictive controller. The optimizer guides the improved LMI iteration paths and enhances the DOA estimation. Simulation studies in MATLAB 2023b/Simulink on a benchmark VSC-HVDC system confirm the feasibility of the proposed approach and show a less conservative DOA estimate compared with conventional methods. 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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15 pages, 2333 KB

Open AccessArticle

Transient Synchronization Stability Analysis of DFIG-Based Wind Turbines with Virtual Resistance Demagnetization Control

by Xiaohe Wang, Xiaofei Chang, Ming Yan, Zhanqi Huang and Chao Wu

Electronics 2026, 15(2), 467; https://doi.org/10.3390/electronics15020467 - 21 Jan 2026

Cited by 2 | Viewed by 403

Abstract

With the increasing penetration of wind power, the transient synchronization stability of doubly fed induction generator (DFIG)-based wind turbines during grid faults has become a critical issue. While conventional fault ride-through methods like Crowbar protection can ensure safety, they compromise system controllability and worsen grid voltage conditions. Virtual resistance demagnetization control has emerged as a promising alternative due to its simple structure and effective flux damping. However, its impact on transient synchronization stability has not been revealed in existing studies. To fill this gap, this paper presents a comprehensive analysis of the transient synchronization stability of DFIG systems under virtual resistance control, introducing a novel fourth-order transient synchronization model that explicitly captures the coupling between the virtual resistance demagnetization control and phase-locked loop (PLL) dynamics. The model reveals the emergence of transient power and positive damping terms induced by the virtual resistance, which play a pivotal role in stabilizing the system. Furthermore, this work theoretically investigates how the virtual resistance and current loop’s proportional-integral (PI) parameters jointly influence transient stability, demonstrating that increasing the virtual resistance while reducing the integral gain of the current loop significantly enhances synchronization stability. Simulation results validate the accuracy of the model and the effectiveness of the proposed analysis. The findings provide a theoretical foundation for optimizing control parameters and improving the stability of DFIG-based wind turbines during grid faults. 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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17 pages, 3010 KB

Open AccessArticle

Research on Transient Stability Optimization Control of Photovoltaic–Storage Virtual Synchronous Generators

by Fen Gong, Xiangyang Xia, Xianliang Luo, Wei Hu and Yijie Zhu

Electronics 2025, 14(24), 4979; https://doi.org/10.3390/electronics14244979 - 18 Dec 2025

Viewed by 451

Abstract

In the case of small disturbances in the power grid, virtual synchronous generators (VSGs) often exhibit active power steady-state errors and significant frequency overshoot, and it is difficult to balance the reduction of active power steady-state errors and the mitigation of frequency overshoot. This paper proposes an improved control method based on active power differential compensation (APDC). First, an active power differential compensation loop is introduced, effectively addressing the issues of active power steady-state deviation and frequency overshoot caused by fixed parameters in the traditional VSG. Secondly, by incorporating a fuzzy logic control (FLC) algorithm, an adaptive PID tuning strategy is proposed as a replacement for the traditional fixed virtual inertia; the PID parameters are dynamically adjusted in real time according to the power–angle deviation and its rate of change, thereby enhancing the small-disturbance dynamic performance of the VSG. Finally, MATLAB R2020b/Simulink simulations and StarSim hardware-in-the-loop simulations validate the effectiveness and accuracy of the proposed control strategy. Simulation results indicate that, compared to traditional control strategies, under peak regulation conditions, the frequency overshoot is reduced by approximately 4.4%, and the active power overshoot is reduced by approximately 5%; under frequency regulation conditions, the frequency overshoot is reduced by approximately 0.26%, and the power overshoot is reduced by approximately 12%. 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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29 pages, 3925 KB

Open AccessArticle

Synergetic Allocation and Operation of Hybrid Energy Storage System and Unified Power Quality Conditioner for Power Quality Enhancement of Distribution Networks with Renewables

by Yanbing Li, Fangmin Bao, Shanlian Guan, Li Zhou, Yu Niu and Peng Zhuang

Electronics 2025, 14(22), 4455; https://doi.org/10.3390/electronics14224455 - 14 Nov 2025

Viewed by 710

Abstract

With the penetration of renewable power generation (RPG) in the distribution network (DN), power quality issues caused by RPG fluctuations have become more prominent than ever, let alone the integration of new types of power loads like electrified trains and electric vehicles that are major harmonic sources. Traditional power quality enhancement approaches are mostly dedicated to the smoothing of RPG power output or active compensation of harmonics, but fail to incorporate both routines into one single power quality enhancement scheme. Out of this research motivation, this paper aims to propose a synergetic allocation scheme for the hybrid energy storage system (HESS) and the unified power quality conditioner (UPQC) to achieve superior power quality enhancement. Firstly, a novel comprehensive vulnerability index of the DN suited for the power quality issues is presented to reflect the key factors that may impact the bus voltage security. Afterwards, the capacity specifications of HESS and UPQC for power smoothing and load side harmonic compensation are deduced with variational mode decomposition and inverter capacity configurations. Subsequently, the synergetic allocation method of HESS and UPQC are proposed by formulating an optimization problem, with the former obtained capacity specifications acting as the main constraints. After that, a dynamic hourly network reconfiguration approach is proposed to enhance the vulnerability level of the DN by dynamically changing its topology, and ensuring better power quality with the optimally allocated HESS and UPQC. Finally, simulation tests and comparative studies are conducted to evaluate the effectiveness and performance of the proposed scheme by comparing with existing methods. The comparative study has shown that the proposed method can reduce bus voltage deviation by 2.63%; meanwhile, it can reduce the total harmonic distortion by 1.83%. 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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30 pages, 5989 KB

Open AccessArticle

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

Cited by 6 | Viewed by 1603

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, 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 KB

Open AccessArticle

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 1641

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 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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