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Advanced in Modeling, Analysis and Control of Microgrids

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 25 June 2026 | Viewed by 2910

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Special Issue Editors

Prof. Dr. Gang Lin

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Guest Editor

College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: microgrid modeling and stability analysis; microgrid stability control and integrated energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Jiayan Liu

E-Mail Website
Guest Editor

College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: power system planning modeling; electric vehicle charging station planning and optimal operation; active distribution network stability and economic analysis; integrated energy system coupling operation research
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Special Issue Information

Dear Colleagues,

A microgrid is a power supply system composed of distributed energy resources, electrical loads, and an energy management system, and is capable of achieving an internal balance of power and energy. Compared with the traditional large-scale power grid, where electric energy is transmitted unidirectionally from power plants through transmission lines to end users, microgrids adopt a bidirectional interaction model, enabling local generation and consumption. This approach maximizes the efficiency of distributed energy utilization and has a positive impact on environmental protection and economic performance. Depending on their functions, operating modes, and application scenarios, microgrids can be classified into various types, with each designed to meet specific requirements. With the continuous development of microgrid technology, microgrids have become an integral part of the emerging modern power system. As a result, research on microgrids has attracted enhanced attention in recent years.

This Special Issue aims to introduce and disseminate recent advancements related to microgrids, including theoretical modeling, the design of control strategies, stability analysis, and operational planning.

The scope of this Special Issue includes, but is not limited to, the following topics:

Modelling methods and stability analysis of microgrids;
Power system planning and modelling;
Planning and optimal operation of electric vehicle charging stations;
Stability and economic analysis of active distribution networks;
Coupled operation of integrated energy systems;
Theoretical modelling of power electronic converters;
Stability analysis methods for grid-connected distributed energy resources;
Energy management technologies for microgrids;
Stability control methods for interconnected microgrids;
Clustered microgrids formed by the interconnection of distributed energy resources.

Prof. Dr. Gang Lin
Dr. Jiayan Liu
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 250 words) can be sent to the Editorial Office for assessment.

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

modelling and stability analysis
power electronic converters
stability control

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Published Papers (5 papers)

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Research

22 pages, 3075 KB

Open AccessArticle

Accurate and Efficient Harmonic Estimation for LCC-HVDC Systems

by Dan Wang, Sijia Hu, Jinjie Lin, Yong Li, Yi Zhang and Jian Song

Energies 2026, 19(7), 1758; https://doi.org/10.3390/en19071758 - 3 Apr 2026

Viewed by 360

Abstract

Modern grids’ dual-high characteristics elevate the role of wideband impedance measurement in operational risk assessment. In thyristor-based line-commutated converter-based high-voltage direct-current (LCC-HVDC) systems, where severe waveform distortion and high harmonic content prevail, nonintrusive wideband techniques rely on precise spectral estimation. Accurate identification of harmonic parameters (frequency, amplitude, and phase) is therefore essential. This work presents a Hann-window-based three-point interpolated discrete Fourier transform (I3pDFT) for precise harmonic parameter estimation. The method suppresses long-range spectral leakage, enhances frequency resolution, and employs robust amplitude and phase estimators that are resilient to noise and negative-frequency interference. Extensive simulations across frequency deviations, noise levels, sampling rates, and record lengths show that the proposed approach outperforms two classical I3pDFT variants in accuracy while maintaining low computational loads suitable for embedded implementation. These results confirm the effectiveness and practicality of the proposed I3pDFT-Hann method for real-world harmonic measurements in LCC-HVDC systems. Full article

(This article belongs to the Special Issue Advanced in Modeling, Analysis and Control of Microgrids)

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18 pages, 4042 KB

Open AccessArticle

Markov Transition Fields-Based Dual-Modal Fusion Method on Transient Stability Assessment for Power Systems

by Min Yan, Qian Chen, Zhihua Huang, Beiqi Qian, Lei Zhang, Yifan Ding and Zehua Su

Energies 2026, 19(6), 1417; https://doi.org/10.3390/en19061417 - 11 Mar 2026

Viewed by 280

Abstract

There is an extremely urgent need to develop a transient stability assessment method for new power systems with greater rapidity and higher accuracy due to the increased complexity and difficulty caused by massive nonlinear power electronics-dominated generation and loads. In recent years, computing power has increased significantly, meaning that artificial intelligence (AI) algorithms have develop rapidly, and large-scale AI models have become available. Among them, deep learning (DL) algorithms have received more attention due to their inherent advantages, on which assessment strategy and methods are based, but these algorithms are still not sufficiently applicable. Therefore, a Markov Transition Field (MTF)-based dual-modal fusion method for transient stability assessment of power systems is proposed in this paper. First, the influence and effect on transient stability assessment by the fusion of both image modality and time series modality are studied. Then, for enhancing key features, the strategy to convert the time series modality into image modality by MTF is established, which allows the features to be described at multiple time scales and the feature correlation between different time points to be strengthened. Thus, features from image modality and time series modality are extracted, respectively, by Convolutional Neural Networks (CNNs), and gated recurrent units are adopted; the extracted features are further fused by a concatenation fusion method. It is demonstrated by the simulation results that the accuracy of the transient stability assessment is improved effectively by the aforementioned fusion method. Full article

(This article belongs to the Special Issue Advanced in Modeling, Analysis and Control of Microgrids)

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23 pages, 3588 KB

Open AccessArticle

Physics-Regularized and Safety-Enhanced Bi-GAT Reinforcement Learning Framework for Voltage Control

by Hui Qin, Binbin Zhong, Kai Wang, Youbing Zhang and Licheng Wang

Energies 2026, 19(4), 1036; https://doi.org/10.3390/en19041036 - 16 Feb 2026

Viewed by 534

Abstract

With more renewables being integrated into distribution grids, the problem of voltage fluctuation has become prominent. Effective Volt/VAR regulation is a commonly used method to ensure the safe operation of distribution networks. Model-based approaches tend to work well only if detailed network parameters are available, while data-driven approaches can suffer from overfitting and may not generalize well. We created the PHY-GAT-SAC framework to address these issues. Physics-regularized reinforcement learning uses bidirectional graph attention, which combines a physics-informed model with a safety projection method that relies on sensitivity matrices. This makes it so that the voltage regulation is practical, interpretable, and secure. The framework works with two combined branches. One branch takes care of the nonlinear mapping from power injections to voltage states using a forward graph encoder and a reverse consistency constraint. At the same time, another branch extracts features directly from the voltages to improve the perception of system violation risk. The framework has a sensitivity-based safety layer as well. This layer projects every control action into a feasible area formed by linearized voltage restrictions, thus securing operation safety. Experiments on an IEEE 33-node system show that the framework works well. A safety layer guarantees a safe operating range without exact impedance values. And PHY-GAT-SAC greatly lowers voltage violations compared to multi-agent deep reinforcement learning. By successfully combining physics with learning, this study gives a unified framework for merging graph neural networks and reinforcement learning within intricate grid management. Full article

(This article belongs to the Special Issue Advanced in Modeling, Analysis and Control of Microgrids)

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19 pages, 6478 KB

Open AccessArticle

An Intelligent Dynamic Cluster Partitioning and Regulation Strategy for Distribution Networks

by Keyan Liu, Kaiyuan He, Dongli Jia, Huiyu Zhan, Wanxing Sheng, Zukun Li, Yuxuan Huang, Sijia Hu and Yong Li

Energies 2026, 19(2), 384; https://doi.org/10.3390/en19020384 - 13 Jan 2026

Viewed by 532

Abstract

As distributed generators (DGs) and flexible adjustable loads (FALs) further penetrate distribution networks (DNs), to reduce regulation complexity compared with traditional centralized control frameworks, DGs and FALs in DNs should be packed in several clusters to enable their dispatch to become standard in the industry. To mitigate the negative influence of DGs’ and FALs’ spatiotemporal distribution and uncertain output characteristics on dispatch, this paper proposes an intelligent dynamic cluster partitioning strategy for DNs, from which the DN’s resources and loads can be intelligently aggregated, organized, and regulated in a dynamic and optimal way with relatively high implementation efficiency. An environmental model based on the Markov decision process (MDP) technique is first developed for DN cluster partitioning, in which a continuous state space, a discrete action space, and a dispatching performance-oriented reward are designed. Then, a novel random forest Q-learning network (RF-QN) is developed to implement dynamic cluster partitioning by interacting with the proposed environmental model, from which the generalization and robust capability to estimate the Q-function can be improved by taking advantage of combining deep learning and decision trees. Finally, a modified IEEE-33-node system is adopted to verify the effectiveness of the proposed intelligent dynamic cluster partitioning and regulation strategy; the results also indicate that the proposed RF-QN is superior to the traditional deep Q-learning (DQN) model in terms of renewable energy accommodation rate, training efficiency, and portioning and regulation performance. Full article

(This article belongs to the Special Issue Advanced in Modeling, Analysis and Control of Microgrids)

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20 pages, 3859 KB

Open AccessArticle

Pulsed Eddy Current Electromagnetic Signal Noise Suppression Method for Substation Grounding Grid Detection

by Su Xu, Yanjun Zhang, Ruiqiang Zhang, Xiaobao Hu, Bin Jia, Ming Ma and Jingang Wang

Energies 2025, 18(21), 5737; https://doi.org/10.3390/en18215737 - 31 Oct 2025

Viewed by 677

Abstract

As the primary discharge channel for fault currents, substation grounding grids are crucial for ensuring the safe and stable operation of power systems. Due to its non-destructive and efficient nature, the pulsed eddy current (PEC) method has become a research hotspot in grounding grid detection in recent years. However, during the detection process, the signal is severely interfered with by substation noise, seriously affecting data quality and interpretation accuracy. To address the problem of suppressing both power frequency and random noise, this paper proposes a composite denoising method that combines bipolar cancellation, minimum noise fraction (MNF), and mask-guided self-supervised denoising. First, based on the periodic characteristics of power frequency noise, a bipolar pulse excitation and differential averaging process is designed to effectively filter out power frequency interference. Subsequently, an MNF algorithm is introduced to identify and reconstruct random noise, improving signal purity. Furthermore, a mask-guided self-supervised denoising model is constructed, using a segmentation convolutional neural network to extract signal-noise masks from noisy data, achieving refined suppression of residual noise. Comparative experiments with simulation and actual substation noise data show that the proposed method outperforms existing typical noise reduction algorithms in terms of signal-to-noise ratio improvement and waveform fidelity, significantly improving the availability and interpretation reliability of pulsed eddy current data. Full article

(This article belongs to the Special Issue Advanced in Modeling, Analysis and Control of Microgrids)

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