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Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (15 September 2025) | Viewed by 18191

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Special Issue Editors

Dr. Shilin Gao

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Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power system stability and control; renewable energy; stability analysis; electromagnetic transient simulation and modelling; digital twin; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Zongsheng Zheng

E-Mail Website
Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power system stability and control; artificial intelligence; parameter identification
Special Issues, Collections and Topics in MDPI journals

Dr. Jianquan Liao

E-Mail Website
Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power system protection and control; power quality of the DC distribution network; power system stability and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, large-scale renewable energy and HVDC are been integrated into the power system and the penetration of renewable energy has been increasing year by year. As a result, the nonlinear characteristics of renewable energy and HVDC increase the difficulty of performing modelling analysis and controlling AC/DC power systems. Furthermore, the fluctuation, intermittency and vulnerability properties of renewable energy may increase the risk of the instability of AC/DC power systems.

In order to discuss the key technologies and issues related to AC/DC power systems with high penetration of renewable energy in modelling, analysis and control, we invite experts and scholars to submit papers discussing the latest academic and technological achievements. Topics to be covered in this Special Issue include, but are not limited, to the following:

Modelling of AC/DC power systems;
Optimal control method for power systems;
Frequency regulation techniques for power systems;
Wide-band frequency oscillation analysis for AC/DC power systems;
Power quality improvement technique for AC/DC power systems.

Dr. Shilin Gao
Dr. Zongsheng Zheng
Dr. Jianquan Liao
Guest Editors

Manuscript Submission Information

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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

AC/DC power system
renewable energy
stability analysis
control
modelling

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Related Special Issue

Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy—2nd Edition in Energies

Published Papers (10 papers)

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Research

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19 pages, 8178 KB

Open AccessArticle

SpectralNet-Enabled Root Cause Analysis of Frequency Anomalies in Solar Grids Using μPMU

by Arnabi Modak, Maitreyee Dey, Preeti Patel and Soumya Prakash Rana

Energies 2026, 19(1), 268; https://doi.org/10.3390/en19010268 - 4 Jan 2026

Viewed by 644

Abstract

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit (

μ

PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in

μ

PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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23 pages, 3168 KB

Open AccessArticle

Spatio-Temporal Feature Fusion-Based Hybrid GAT-CNN-LSTM Model for Enhanced Short-Term Power Load Forecasting

by Jia Huang, Qing Wei, Tiankuo Wang, Jiajun Ding, Longfei Yu, Diyang Wang and Zhitong Yu

Energies 2025, 18(21), 5686; https://doi.org/10.3390/en18215686 - 29 Oct 2025

Cited by 1 | Viewed by 1424

Abstract

Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load forecasting. The model integrates three core components synergistically: Graph Attention Network (GAT) dynamically captures spatial correlations via adaptive node weighting, resolving static topology constraints; a CNN-LSTM module extracts multi-scale temporal features—convolutional kernels decompose load fluctuations, while bidirectional LSTM layers model long-term trends; and a gated fusion mechanism adaptively weights and fuses spatio-temporal features, suppressing noise and enhancing sensitivity to critical load periods. Experimental validations on multi-city datasets show significant improvements: the model outperforms baseline models by a notable margin in error reduction, exhibits stronger robustness under extreme weather, and maintains superior stability in multi-step forecasting. This study concludes that the hybrid model balances spatial topological analysis and temporal trend modeling, providing higher accuracy and adaptability for STLF in complex power grid environments. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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18 pages, 5140 KB

Open AccessArticle

Computational Efficiency–Accuracy Trade-Offs in EMT Modeling of ANPC Converters: Comparative Study and Real-Time HIL Validation

by Xinrong Yan, Zhijun Li, Jiajun Ding, Ping Zhang, Jia Huang, Qing Wei and Zhitong Yu

Energies 2025, 18(19), 5173; https://doi.org/10.3390/en18195173 - 29 Sep 2025

Cited by 1 | Viewed by 961

Abstract

With the increasing demands of the grid on power electronic converters, active neutral-point-clamped (ANPC) converters have been widely adopted due to their flexible modulation strategies and wide-range power regulation capabilities. To address grid-integration testing requirements for ANPC converters, this paper comparatively studies three electromagnetic transient (EMT) modeling approaches: switch-state prediction method (SPM), associated discrete circuit (ADC), and time-averaged method (TAM). Steady-state and transient simulations reveal that the SPM model achieves the highest accuracy (error ≤ 0.018%), while the TAM-based switching function model optimizes the efficiency–accuracy trade-off with 6.4× speedup versus traditional methods and acceptable error (≤2.62%). Consequently, the TAM model is implemented in a real-time hardware-in-the-loop (HIL) platform. Validation under symmetrical/asymmetrical grid faults confirms both the model’s efficacy and the controller’s robust fault ride-through capability. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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23 pages, 8091 KB

Open AccessArticle

Neural ODE-Based Dynamic Modeling and Predictive Control for Power Regulation in Distribution Networks

by Libin Wen, Jinji Xi, Hong Hu, Li Xiong, Guangling Lu and Tannan Xiao

Energies 2025, 18(13), 3419; https://doi.org/10.3390/en18133419 - 29 Jun 2025

Cited by 4 | Viewed by 2263

Abstract

The increasing penetration of distributed energy resources (DERs) and power electronic loads challenges the modeling and control of modern distribution networks (DNs). The traditional models often fail to capture the complex aggregate dynamics required for advanced control strategies. This paper proposes a novel framework for DN power regulation based on Neural Ordinary Differential Equations (NODEs) and Model Predictive Control (MPC). NODEs are employed to develop a data-driven, continuous-time dynamic model capturing the aggregate relationship between the voltage at the point of common coupling (PCC) and the network’s power consumption, using only PCC measurements. Building upon this NODE model, an MPC strategy is designed to regulate the DN’s active power by manipulating the PCC voltage. To ensure computational tractability for real-time applications, a local linearization technique is applied to the NODE dynamics within the MPC, transforming the optimization problem into a standard Quadratic Programming (QP) problem that can be solved efficiently. The framework’s efficacy is comprehensively validated through simulations. The NODE model demonstrates high accuracy in predicting the dynamic behavior in a DN against a detailed simulator, with maximum relative errors below 0.35% for active power. The linearized NODE-MPC controller shows effective tracking performance, constraint handling, and computational efficiency, with typical QP solve times below 0.1 s within a 0.1 s control interval. The validation includes offline tests using the NODE model and online co-simulation studies using CloudPSS and Python via Redis. Application scenarios, including Conservation Voltage Reduction (CVR) and supply–demand balancing, further illustrate the practical potential of the proposed approach for enhancing the operation and efficiency of modern distribution networks. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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23 pages, 1861 KB

Open AccessArticle

A Scalable Data-Driven Surrogate Model for 3D Dynamic Wind Farm Wake Prediction Using Physics-Inspired Neural Networks and Wind Box Decomposition

by Qiuyu Lu, Yuqi Cao, Pingping Xie, Ying Chen and Yingming Lin

Energies 2025, 18(13), 3356; https://doi.org/10.3390/en18133356 - 26 Jun 2025

Cited by 3 | Viewed by 1936

Abstract

Wake effects significantly reduce efficiency and increase structural loads in wind farms. Therefore, accurate and computationally efficient models are crucial for wind farm layout optimization and operational control. High-fidelity computational fluid dynamics (CFD) simulations, while accurate, are too slow for these tasks, whereas faster analytical models often lack dynamic fidelity and 3D detail, particularly under complex conditions. Existing data-driven surrogate models based on neural networks often struggle with the high dimensionality of the flow field and scalability to large wind farms. This paper proposes a novel data-driven surrogate modeling framework to bridge this gap, leveraging Neural Networks (NNs) trained on data from the high-fidelity SOWFA (simulator for wind farm applications) tool. A physics-inspired NN architecture featuring an autoencoder for spatial feature extraction and latent space dynamics for temporal evolution is introduced, motivated by the time–space decoupling structure observed in the Navier–Stokes equations. To address scalability for large wind farms, a “wind box” decomposition strategy is employed. This involves training separate NN models on smaller, canonical domains (with and without turbines) that can be stitched together to represent larger farm layouts, significantly reducing training data requirements compared to monolithic farm simulations. The development of a batch simulation interface for SOWFA to generate the required training data efficiently is detailed. Results demonstrate that the proposed surrogate model accurately predicts the 3D dynamic wake evolution for single-turbine and multi-turbine configurations. Specifically, average velocity errors (quantified as RMSE) are typically below 0.2 m/s (relative error < 2–5%) compared to SOWFA, while achieving computational accelerations of several orders of magnitude (simulation times reduced from hours to seconds). This work presents a promising pathway towards enabling advanced, model-based optimization and control of large wind farms. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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17 pages, 6363 KB

Open AccessArticle

Modeling and Simulation Analysis of Three-Phase Saturable Transformers: A Study on the Effects of Geomagnetically Induced Current on Transformers

by Junhong Duan, Yanyan Bao, Guangdong Zhang, Xiaofei Wang, Pin Jiang, Wei Niu, Hailong Zhang, Wenxi Zhen, Yue Xia and Ruikai Song

Energies 2025, 18(4), 824; https://doi.org/10.3390/en18040824 - 11 Feb 2025

Cited by 7 | Viewed by 2181

Abstract

The saturation model of the transformer is one of the core tools of multi-physics simulation. By combining it with multi-physics simulation, researchers can more comprehensively evaluate the performance of a transformer in actual applications. Geomagnetically induced currents (GIC) induce DC bias in transformers, leading to core saturation and a host of adverse effects. Traditional transformer models often struggle to accurately capture the behavior of the core under nonlinear saturation conditions. To address these challenges, a saturable transformer unified magnetic-equivalent (UMEC) model that directly takes the B-H magnetization curve to represent a transformer’s core nonlinear characteristics is proposed. The saturable transformer model is based on the model of a magnetic circuit of the transformer core. An estimation method to obtain a transformer’s essential parameters for saturation simulation is presented. GIC effects on transformer saturation are also studied through the proposed saturable transformer and estimation method. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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14 pages, 2657 KB

Open AccessArticle

Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform

by Jiao Hao, Zongbao Zhang, Zonglin He, Zhengyuan Liu, Zhengdong Tan and Yankan Song

Energies 2024, 17(24), 6269; https://doi.org/10.3390/en17246269 - 12 Dec 2024

Cited by 1 | Viewed by 1620

Abstract

As the scale of China’s interconnected power grid continues to expand, traditional serial computing methods are no longer sufficient for the rapid analysis and computation of electrical networks with tens of thousands of nodes due to their small scale and low efficiency. To enhance the capability of online grid analysis, this paper introduces an accelerating batched power flow calculation method based on a heterogeneous CPU-GPU platform. This method, based on the fast decoupled method, combined with the tremendous parallel computing capability of GPUs with the multi-threaded parallel processing of CPUs, efficiently resolves the exceeding bus type conversion issues in GPU-batched power flow calculation and improves the accuracy of the power flow calculations. Then, a binary-based power flow data exchange format was introduced, which utilizes a single binary file for data exchange. This format significantly minimizes I/O time overhead and reduces file size, further enhancing the method’s efficiency. Case studies on real-world power grids demonstrate its high accuracy and reliability. Compared to the traditional single-threaded power flow calculation method, this method dramatically reduces time consumption in batch power flow calculations. It proves the significant advantages of dealing with large-scale power flow calculations. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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21 pages, 11906 KB

Open AccessArticle

Optimization-Based Suppression Method of Oscillations in Photovoltaic Grid-Connected Systems with Controllable Nonlinear Loads

by Tong Zhu, Gechao Huang, Xi Ye, Yanfeng Wang, Xuetong Ouyang, Weilin Zhang, Yangfan Cheng and Yuhong Wang

Energies 2024, 17(16), 4120; https://doi.org/10.3390/en17164120 - 19 Aug 2024

Cited by 3 | Viewed by 1381

Abstract

In order to reduce carbon emissions from the power grid, photovoltaic (PV) generation units and controllable nonlinear loads based on power electronic devices are gradually becoming more prevalent in the power system. In a PV grid-connected system featuring controllable nonlinear loads, the interplay among PV grid-connected inverters, the loads, and the grid can potentially lead to voltage oscillations. To tackle this challenge, this paper introduces an optimization-based method for suppressing oscillations, which carefully balances system stability with response performance. Firstly, an impedance model of the system is established by applying the harmonic linearization method, and system stability is analyzed using the “logarithmic frequency stability criterion”. Subsequently, impedance relative sensitivity is used to identify key parameters that affect system stability, and the interaction between key parameters is considered to analyze the stability range for these parameters. On this basis, a parameter optimization method based on the particle swarm optimization algorithm is proposed to balance system stability and response performance. The effectiveness and robustness of this method are verified through a simulation analysis. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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18 pages, 7338 KB

Open AccessArticle

Droop Frequency Limit Control and Its Parameter Optimization in VSC-HVDC Interconnected Power Grids

by Han Jiang, Yichen Zhou, Yi Gao and Shilin Gao

Energies 2024, 17(15), 3851; https://doi.org/10.3390/en17153851 - 5 Aug 2024

Cited by 5 | Viewed by 2532

Abstract

With the gradual emergence of trends such as the asynchronous interconnection of power grids and the increasing penetration of renewable energy, the issues of ultra-low-frequency oscillations and low-frequency stability in power grids have become more prominent, posing serious challenges to the safety and stability of systems. The voltage-source converter-based HVDC (VSC-HVDC) interconnection is an effective solution to the frequency stability problems faced by regional power grids. VSC-HVDC can participate in system frequency stability control through a frequency limit controller (FLC). This paper first analyses the basic principles of how VSC-HVDC participates in system frequency stability control. Then, in response to the frequency stability control requirements of the sending and receiving power systems, a droop FLC strategy is designed. Furthermore, a multi-objective optimization method for the parameters of the droop FLC is proposed. Finally, a large-scale electromagnetic transient simulation model of the VSC-HVDC interconnected power system is constructed to verify the effectiveness of the proposed droop FLC method. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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Review

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26 pages, 1538 KB

Open AccessReview

AI-Based Modeling and Optimization of AC/DC Power Systems

by Izabela Rojek, Dariusz Mikołajewski, Piotr Prokopowicz and Maciej Piechowiak

Energies 2025, 18(21), 5660; https://doi.org/10.3390/en18215660 - 28 Oct 2025

Cited by 4 | Viewed by 1821

Abstract

This review examined the latest advances in the modeling, analysis, and control of AC/DC power systems based on artificial intelligence (AI) in which renewable energy sources play a significant role. Integrating variable and intermittent renewable energy sources (such as sunlight and wind power) poses a major challenge in maintaining system stability, reliability, and optimal system performance. Traditional modeling and control methods are increasingly inadequate to capture the complex, nonlinear, and dynamic behavior of modern hybrid AC/DC systems. Specialized AI techniques, such as machine learning (ML) and deep learning (DL), and hybrid models, have become important tools to meet these challenges. This article presents a comprehensive overview of AI-based methodologies for system identification, fault diagnosis, predictive control, and real-time optimization. Particular attention is paid to the role of AI in increasing grid resilience, implementing adaptive control strategies, and supporting decision-making under uncertainty. The review also highlights key breakthroughs in AI algorithms, including federated learning, and physics-based neural networks, which offer scalable and interpretable solutions. Furthermore, the article examines current limitations and open research problems related to data quality, computational requirements, and model generalizability. Case studies of smart grids and comparative scenarios demonstrate the practical effectiveness of AI-based approaches in real-world energy system applications. Finally, it proposes future directions to narrow the gap between AI research and industrial application in next-generation smart grids. Full article

(This article belongs to the Special Issue Modelling, Analysis and Control of AC/DC Power Systems with High Penetration of Renewable Energy)

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