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Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems

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

Deadline for manuscript submissions: 10 September 2026 | Viewed by 3880

Special Issue Editors

Dr. Wanjun Huang

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: power system voltage stability; power grid reconfiguration; optimal power flow; application of machine learning in power systems
Prof. Dr. Lipeng Zhu

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Interests: power system stability; optimal power flow; active distribution network; voltage control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing penetration of renewable energy sources (RESs), such as solar and wind, presents significant challenges to the stability and optimal operation of modern power systems. The inherent intermittency and stochasticity of RESs lead to critical challenges, such as increased frequency deviations, voltage instabilities, and operational constraints. Furthermore, the distributed and decentralized characteristics of these resources demand novel, scalable methodologies for grid management that can maintain system stability and optimize performance in real-time. Machine Learning (ML) approaches offer a promising, data-driven paradigm to address these challenges. By leveraging advanced ML techniques—such as deep neural networks, reinforcement learning, and generative adversarial networks—grid operators can move beyond traditional model-based methods. ML-based approaches enable sophisticated real-time monitoring, high-fidelity forecasting, and advanced control strategies. This empowers operators to proactively manage grid imbalances, mitigate operational risks, and optimize system performance in the dynamic environment of high RES integration.

This Special Issue seeks to consolidate cutting-edge research and innovative applications of machine learning for enhancing the stability and operational efficiency of power systems.

Topics of interest for publication include, but are not limited to:

  • Renewable generation/load forecasting;
  • Power system dynamics modeling and identification;
  • Power system stability/security assessment;
  • Power system fault/event detection;
  • Optimal power flow;
  • Economic dispatch;
  • Network planning;
  • Distribution network reconfiguration;
  • Preventive and corrective cyber-defense;
  • Cyber-resilient control;
  • Cybersecurity enhancement.

Dr. Wanjun Huang
Prof. Dr. Lipeng Zhu
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

  • stability assessment
  • security assessment
  • fault detection
  • reconfiguration
  • planning
  • optimal power flow
  • forecast
  • cyber security

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Published Papers (5 papers)

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Research

19 pages, 2821 KB  
Article
A DMPC-Based Secondary Harmonic Compensation Strategy via Adaptive Virtual Admittance Tuning
by Fang Chen, Zhengyu Wang, Meng Liu, Junjie Sun, Han Yang, Ming Yang, Yanyi Fu, Weihao Shuai and Yelun Peng
Energies 2026, 19(10), 2281; https://doi.org/10.3390/en19102281 - 8 May 2026
Viewed by 301
Abstract
A large number of grid-connected inverters have been connected to distribution networks and can be used to mitigate harmonics at the system level. Deploying distributed power electronic devices for harmonic mitigation is a cost-effective solution for distribution networks. However, existing coordination methods typically [...] Read more.
A large number of grid-connected inverters have been connected to distribution networks and can be used to mitigate harmonics at the system level. Deploying distributed power electronic devices for harmonic mitigation is a cost-effective solution for distribution networks. However, existing coordination methods typically depend on highly reliable, low-latency communications. Communication delays or interruptions can significantly degrade coordination performance and even exacerbate harmonic distortion. This paper presents a hierarchical, coordinated harmonic compensation method for multiple multifunctional grid-tied inverters (MFGTIs). At the primary control level, harmonic domain virtual admittance is incorporated, enabling each device to adaptively inject harmonic compensation currents using only local measurements, maintaining baseline compensation capability when communication is limited or interrupted. At the secondary control level, a distributed model predictive control (DMPC) scheme is derived from the harmonic steady-state equivalent circuit. The virtual admittance parameters are updated iteratively using measurements exchanged only among neighboring nodes, enabling coordinated sharing of compensation currents without requiring global information or frequent harmonic power flow calculations. Case studies demonstrate that the proposed method reduces nodal harmonic voltages under communication constrained conditions while significantly lowering the computational burden. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems)
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15 pages, 1365 KB  
Article
A Multi-Level Ensemble Model-Based Method for Power Quality Disturbance Identification
by Hao Bai, Ruotian Yao, Chang Liu, Tong Liu, Shiqi Jiang, Yuchen Huang and Yiyong Lei
Energies 2026, 19(3), 730; https://doi.org/10.3390/en19030730 - 29 Jan 2026
Viewed by 449
Abstract
With the large-scale integration of renewable energy and power electronic devices, power quality disturbances exhibit strong nonlinearity and complex dynamic behavior. Traditional methods are limited by insufficient feature extraction and cumbersome classification, often failing to meet practical accuracy and robustness requirements. To address [...] Read more.
With the large-scale integration of renewable energy and power electronic devices, power quality disturbances exhibit strong nonlinearity and complex dynamic behavior. Traditional methods are limited by insufficient feature extraction and cumbersome classification, often failing to meet practical accuracy and robustness requirements. To address this issue, this paper proposes a multi-level ensemble method for power quality disturbance identification. A time–frequency dual-branch feature extraction module was designed, combining residual networks and bidirectional temporal convolutional networks to capture both local discriminative features and long-range temporal dependencies in the time and frequency domains. A cross-attention mechanism was further employed to fuse the time–frequency features, enabling adaptive focus on the most critical information for disturbance classification. The fused features were fed into fully connected layers and a Softmax classifier for multi-class identification. Experimental results demonstrated superior accuracy, robustness, and generalization capability compared with existing methods, validating the effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems)
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18 pages, 2272 KB  
Article
Energy Consumption Modeling and Elastic Space Computation of Data Centers Considering Spatiotemporal Transfer Flexibility
by Shuting Chen, Sen Xu, Yajie Li, Gang Liang, Mengnan Ma, Junhan Jiang and Wei Lin
Energies 2025, 18(24), 6449; https://doi.org/10.3390/en18246449 - 9 Dec 2025
Viewed by 948
Abstract
With the rapid expansion of data centers and the growing demand for cloud computing, their share in total electricity consumption has surged, making them a major high-power load in power systems. Consequently, accurately modeling their energy consumption and quantifying the feasible region have [...] Read more.
With the rapid expansion of data centers and the growing demand for cloud computing, their share in total electricity consumption has surged, making them a major high-power load in power systems. Consequently, accurately modeling their energy consumption and quantifying the feasible region have become critical research challenge. Existing studies have focused on energy consumption models for single data centers and single time periods, while limited attention has been given to multi-data centers energy optimization that considers spatiotemporal workload migration. This paper presents an energy consumption model for multi-data centers that accounts for the spatiotemporal transfer flexibility of delay-tolerant workloads. By enabling task migration across data centers (spatial dimension) and workload deferral within each center (temporal dimension), the model dynamically adjusts the operational states of IT equipment to minimize overall system operating costs while satisfying computational demands. To address the computational challenges caused by the large number of integer variables, the sliding window method and equipment aggregation method are employed to ensure the model can be efficiently solved. To further capture the flexibility of data center energy consumption, a method for computing the energy consumption elasticity space is proposed based on multi-parametric programming. This elasticity space characterizes the feasible range of energy consumption under operational constraints and provides boundary conditions for power system dispatch optimization. Simulation studies using real operational data from a large-scale Internet enterprise show that the proposed model reduces the total operational cost by approximately 3.4% compared to the baseline model without flexibility, decreases the frequency of IT equipment state transitions, and enhances the flexibility of data centers in supporting power system supply-demand balance and renewable energy integration. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems)
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23 pages, 4471 KB  
Article
A Differential Planning Strategy for Distribution Network Resilience Enhancement Considering Decision Dependence Uncertainty
by Xuming Chen, Le Liu and Xiaoning Kang
Energies 2025, 18(23), 6353; https://doi.org/10.3390/en18236353 - 3 Dec 2025
Cited by 2 | Viewed by 749
Abstract
To reduce the impact of extreme natural disasters on urban distribution networks and improve the interpretability of planning decisions, this paper proposes a distributionally robust planning strategy for distribution networks that considers decision-dependent uncertainty. First, a decision-dependent uncertainty model is established to represent [...] Read more.
To reduce the impact of extreme natural disasters on urban distribution networks and improve the interpretability of planning decisions, this paper proposes a distributionally robust planning strategy for distribution networks that considers decision-dependent uncertainty. First, a decision-dependent uncertainty model is established to represent the relationship between power line failure probability and reinforcement decisions, with uncertainty described using norm-bounded fuzzy sets. Then, a three-level distributionally robust multi-grade reinforcement model is developed, which retains typical fault scenarios to reduce computational complexity and improve efficiency. Next, a global sensitivity analysis method based on the Sobol’ approach is introduced to analyze the marginal effects of resilience investments and quantify the impact of specific reinforcement measures on total planning cost and overall power system resilience. Finally, simulations based on the IEEE 33-bus test system verify the effectiveness of the proposed planning strategy. The results show that the proposed method can effectively enhance grid resilience while improving the interpretability of planning strategies. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems)
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21 pages, 531 KB  
Article
An Efficient Heuristic Algorithm for Stochastic Multi-Timescale Network Reconfiguration for Medium- and High-Voltage Distribution Networks with High Renewables
by Wanjun Huang, Mingrui Xu, Xinran Zhang and Le Zheng
Energies 2025, 18(21), 5861; https://doi.org/10.3390/en18215861 - 6 Nov 2025
Cited by 2 | Viewed by 889
Abstract
To handle the uncertainties brought by the increasing penetration of renewable energy sources and random loads, we design a stochastic multi-timescale distribution network reconfiguration (SMTDNR) framework to coordinate diverse scheduling resources across different timescales and develop an efficient heuristic algorithm to solve this [...] Read more.
To handle the uncertainties brought by the increasing penetration of renewable energy sources and random loads, we design a stochastic multi-timescale distribution network reconfiguration (SMTDNR) framework to coordinate diverse scheduling resources across different timescales and develop an efficient heuristic algorithm to solve this complex NP-hard combinatorial optimization problem with high efficiency for medium- and high-voltage distribution networks. First, the SMTDNR problem, incorporating distributed renewable generators, fuel generators, energy storage systems, and controllable loads, is simplified through circular constraint linearization, Jabr relaxation, and second-order cone (SOC) relaxation techniques. Then, a one-stage multi-timescale successive branch reduction (MTSBR) algorithm is developed for distribution networks with one redundant branch, which transforms the SMTDNR problem into a stochastic multi-timescale optimal power flow (SMTOPF) problem. This is extended to a two-stage MTSBR algorithm for general networks with multiple redundant branches, which iteratively runs the proposed one-stage MTSBR algorithm. Numerical results on modified IEEE 33-bus and 123-bus distribution networks validate the superior optimality, feasibility, and computational efficiency of the proposed algorithms, particularly in scenarios of high renewable penetration and increased uncertainty, offering robust and feasible solutions where traditional methods may fail. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems)
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