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

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 5371

Special Issue Editors

Dr. Shilin Gao

E-Mail Website
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
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
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

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

  • AC/DC power system
  • renewable energy
  • stability analysis
  • control
  • modelling

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Published Papers (6 papers)

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Research

23 pages, 8091 KiB  
Article
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
Viewed by 274
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 [...] Read more.
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 KiB  
Article
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
Viewed by 354
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 [...] Read more.
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 KiB  
Article
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 3 | Viewed by 893
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, [...] Read more.
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 KiB  
Article
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
Viewed by 778
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 [...] Read more.
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 KiB  
Article
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 1 | Viewed by 888
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 [...] Read more.
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 KiB  
Article
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 4 | Viewed by 1309
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 [...] Read more.
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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