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AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of...
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AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 6248

Editors

Dr. Song Ke

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: mechanism analysis and scalable intelligent control of frequency stability in vehicle-grid interaction
Dr. Siyuan Chen

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Wuhan University, Wuhan, China
Interests: power system operation and dispatch; hybrid model data-driven method
Dr. Hao Wu

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Interests: resilience enhancement and restoration of power system/integrated energy system

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the role of artificial intelligence (AI) in enhancing the operation and resilience of next-generation power systems. As modern grids undergo rapid transformation due to the deep integration of renewable energy, electric vehicles (EVs), and distributed storage, both the structure and dynamics of power system operation are evolving. In particular, this publication will target the development of AI-driven strategies to address these complexities and to enable intelligent, flexible, and robust grid operation under conditions of growing uncertainty and volatility.

We invite original research articles and review papers addressing AI-enabled modeling, analysis, and control methods that align with the new operational paradigms of power systems. Topics of interest include, but are not limited to, the following areas:

  • Artificial intelligence in next-generation power systems;
  • Power system resilience enhancement;
  • Vehicle to grid (V2G) technologies and grid interaction modeling;
  • Coordination and control of distributed energy storage systems;
  • AI-driven forecasting of load and renewable generation;
  • Real-time optimization and intelligent control of power systems;
  • Data-driven decision-making under grid uncertainty;
  • Modeling of new resources and load behaviors in modern grids;
  • Resilience-oriented grid planning and real-time reconfiguration;
  • Reinforcement learning for distributed and coordinated control.

With the development of next-generation power systems, resources such as EVs and distributed storage are inevitably influencing grid operation, both proactively (e.g., demand-side participation, V2G) and passively (e.g., stochastic behavior, reverse power flow). As a result, the operational objectives of power systems are transitioning from traditional cost-effectiveness and reliability toward enhanced resilience, flexibility, and intelligence. This Special Issue aims to provide a dedicated platform for academic exchange on this topic, grounded in real-world engineering challenges. We seek to attract cutting-edge research from the power systems community and encourage interdisciplinary collaboration between academia and industry. We are particularly keen to publish contributions that combine advanced AI methods with practically relevant modelling approaches, with the goal of jointly addressing the emerging operational challenges of future grids.

While previous studies have explored AI applications in power systems or investigated the integration of emerging resources separately, there is a growing need for systematic research that addresses both dimensions in a unified framework. This Special Issue contributes to the existing literature by positioning AI, not just as a computational tool, but as a transformative enabler for managing the complexity and uncertainty introduced by new energy technologies. By bridging data-driven intelligence and domain-specific modeling, this Issue aims to enrich current knowledge and accelerate the transition toward resilient, adaptive, and intelligent power system operations.

Dr. Song Ke
Dr. Siyuan Chen
Dr. Hao Wu
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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

  • artificial intelligence
  • power system resilience
  • vehicle-to-grid (V2G)

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Published Papers (8 papers)

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Research

24 pages, 5577 KB  
Article
Resilient SDN-Based Communication Architecture for Adaptive Control in Green Hydrogen Hybrid Microgrids
by Joaquín Ascencio Villagra, Ricardo Pérez Guzmán, Marco Rivera, Patrick Wheeler and Frede Blaabjerg
Electronics 2026, 15(11), 2335; https://doi.org/10.3390/electronics15112335 - 28 May 2026
Viewed by 318
Abstract
Integrating green hydrogen systems into hybrid microgrids introduces nonlinear dynamics that compromise control stability during operational transitions. The performance of the advanced control loops depends on the latency and reliability provided by the communication infrastructure. This paper proposes a Software-Defined Networking (SDN) architecture [...] Read more.
Integrating green hydrogen systems into hybrid microgrids introduces nonlinear dynamics that compromise control stability during operational transitions. The performance of the advanced control loops depends on the latency and reliability provided by the communication infrastructure. This paper proposes a Software-Defined Networking (SDN) architecture integrated with an adaptive Quality of Service (AQoS) framework to support time-critical data flows in a hybrid microgrid with green hydrogen integration. An emulated network topology in GNS3, with OpenDaylight as the SDN controller and Open vSwitch as the forwarding plane, reproduces IEC 61850 traffic patterns, including GOOSE, control set-points and MMS. These traffic classes coordinate key microgrid components, including electrolysers, fuel cells and battery storage. Experimental results show that the SDN-AQoS framework reduces latency variance by 60% compared to unmanaged SDN configurations and delivers 49.4% higher throughput than traditional TCP/IP networks under congestion. The SDN-AQoS configuration achieves a median latency of 9.68 ms, keeping 97.5% of the measurements below the 20 ms safety threshold for electrolyser control. This level of reliability represents a substantial improvement over the plain TCP/IP at 90%, unmanaged SDN at 66.7% and static QoS policing at 60%. QoS rules are configured through the RESTCONF interface and remain fixed during each experiment while enabling the future integration of reinforcement learning agents for autonomous QoS adaptation. At the same time, this framework supports the bounded communication delay required to sustain frequency control and electrolyser safety coordination in low-inertia hydrogen microgrids during network congestion. The physical layer impact of these communication improvements remains a subject of future hardware-in-the-loop validation. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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29 pages, 2055 KB  
Article
Resilience Assessment and Enhancement Strategy for Transmission Lines Based on Distributed Fibre Optic Sensing
by Menghao Zhang, Qingwu Gong, Xiuyi Li and Hui Qiao
Electronics 2026, 15(8), 1739; https://doi.org/10.3390/electronics15081739 - 20 Apr 2026
Viewed by 528
Abstract
Typhoon-induced wind loads pose severe threats to transmission systems. However, existing resilience assessment approaches typically rely on sparse meteorological station data and assume spatially uniform wind speed distributions along transmission corridors, which fail to capture the span-level spatial difference of wind fields. To [...] Read more.
Typhoon-induced wind loads pose severe threats to transmission systems. However, existing resilience assessment approaches typically rely on sparse meteorological station data and assume spatially uniform wind speed distributions along transmission corridors, which fail to capture the span-level spatial difference of wind fields. To address this limitation, this paper proposes a distributed optical fiber sensing (DOFS)-driven span-level resilience assessment and hardening optimization framework for transmission networks. First, a phase-sensitive optical time domain reflectometry (Φ-OTDR)-based distributed optical fiber sensing system is employed, utilizing optical fibers embedded in existing OPGW cables as sensing media. By capturing vibration responses of the fiber induced by wind–structure interaction, real-time spatiotemporal wind speed sequences at the individual span level are reconstructed through signal processing and inversion algorithms, providing high-spatial-resolution environmental input data for resilience evaluation. Second, a span-level failure probability quantification method is established using a load–strength interference model. On this basis, a resilience evaluation framework—“span-level asset damage cost—line-level critical corridor identification—system-level load shedding assessment”—is constructed, enabling cross-scale resilience quantification from component damage to system-level performance degradation. Third, a span-level gradient hardening optimization model is developed. By adopting a scenario pre-calculation and iterative updating strategy, coordinated solving of reinforcement decisions and failure scenarios is achieved, thereby maximizing resilience enhancement benefits. The proposed framework is validated using DOFS-measured wind speed data collected from a 500 kV transmission line along the Fujian coast during three real typhoon events—Typhoon Shantuo, Typhoon Trami, and Typhoon Koinu—supporting the reliability of the acquired span-level wind speed information. Case studies conducted on a modified IEEE RTS-24 system demonstrate that the proposed span-level hardening strategy can substantially reduce reinforcement cost compared with the conventional line-level hardening strategy. In the reported benchmark case, it achieves zero load-shedding penalty with a markedly lower hardening cost, and under the same budget constraint, it further yields lower expected load shedding and lower expected asset damage. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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25 pages, 6093 KB  
Article
Reliability-Aware Heterogeneous Graph Attention Networks with Temporal Post-Processing for Electronic Power System State Estimation
by Qing Wang, Jian Yang, Pingxin Wang, Yaru Sheng and Hongxia Zhu
Electronics 2026, 15(7), 1536; https://doi.org/10.3390/electronics15071536 - 7 Apr 2026
Viewed by 479
Abstract
Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity [...] Read more.
Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity of large-scale grids. To address these issues, this paper proposes ST-ResGAT, a spatio-temporal residual graph attention framework for nonlinear state estimation under heterogeneous sensing conditions. The proposed method models the problem on an augmented heterogeneous factor graph, employs a reliability-aware heterogeneous graph attention mechanism with residual propagation to adaptively fuse measurements of different quality, and further refines the graph-based estimates through a lightweight LSTM post-processing module that exploits short-term temporal continuity. All datasets are generated using pandapower on the IEEE 30-bus, IEEE 118-bus, and IEEE 1354-bus benchmark systems to ensure full reproducibility of the experimental pipeline. Experimental results show that the proposed method consistently achieves lower estimation errors than WLS, DNN, GAT, and PINN baselines across all three systems, while also exhibiting more compact node-level error distributions and stronger spatial consistency. Multi-seed ablation studies further indicate that residual propagation, reliability-aware attention, and temporal refinement play complementary roles across different system scales. Robustness experiments additionally show that, under random measurement exclusion as well as bias, Gaussian, and mixed corrupted-measurement settings, ST-ResGAT exhibits smooth and progressive degradation, including on the newly added large-scale IEEE 1354-bus benchmark. These results suggest that the proposed framework is a promising direction for data-driven state estimation under controlled mixed-measurement benchmark conditions. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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28 pages, 4423 KB  
Article
A Neighbor Feature Aggregation-Based Multi-Agent Reinforcement Learning Method for Fast Solution of Distributed Real-Time Power Dispatch Problem
by Baisen Chen, Chenghuang Li, Qingfen Liao, Wenyi Wang, Lingteng Ma and Xiaowei Wang
Electronics 2026, 15(7), 1415; https://doi.org/10.3390/electronics15071415 - 28 Mar 2026
Viewed by 378
Abstract
To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph [...] Read more.
To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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15 pages, 698 KB  
Article
Hierarchical Control of EV Virtual Power Plants: A Strategy for Peak-Shaving Ancillary Services
by Youzhuo Zheng, Hengrong Zhang, Anjiang Liu, Yue Li, Shuqing Hao, Yu Miao, Yujie Liang and Siyang Liao
Electronics 2026, 15(3), 578; https://doi.org/10.3390/electronics15030578 - 28 Jan 2026
Cited by 1 | Viewed by 509
Abstract
In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address [...] Read more.
In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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24 pages, 5577 KB  
Article
A Novel Strategy for Preventing Commutation Failures During Fault Recovery Using PLL Phase Angle Error Compensation
by Junpeng Deng, Liangzhong Yao, Jinglei Deng, Shuai Liang, Rongxiang Yuan, Guoju Zhang and Xuefeng Ge
Electronics 2025, 14(23), 4651; https://doi.org/10.3390/electronics14234651 - 26 Nov 2025
Cited by 1 | Viewed by 631
Abstract
Existing studies on commutation failure during fault recovery (CFFR) in line-commutated converter high-voltage direct current (LCC-HVDC) systems often neglect the critical influence of phase-locked loop phase tracking error (PLL-PTE) and fail to provide effective control strategies to address this issue. This paper investigates [...] Read more.
Existing studies on commutation failure during fault recovery (CFFR) in line-commutated converter high-voltage direct current (LCC-HVDC) systems often neglect the critical influence of phase-locked loop phase tracking error (PLL-PTE) and fail to provide effective control strategies to address this issue. This paper investigates the influence of PLL-PTE on CFFR through electromagnetic transient simulations based on a modified CIGRE benchmark model. The study reveals that phase angle jump (PAJ) caused by DC power fluctuations (DPF) and AC network reconfigurations (ANR) is the fundamental source of PLL-PTE, which in turn leads to the occurrence of CFFR. To mitigate this, a novel control strategy is proposed that dynamically adjusts the extinction angle based on historical and predicted PAJ data. Simulation results demonstrate that the proposed method effectively suppresses CFFR under various fault conditions, including different fault types, locations, resistances, and initiation times. Compared with existing control schemes, the proposed approach avoids adverse side effects while exhibiting strong robustness and adaptability. The proposed control strategy significantly enhances the stability and reliability of LCC-HVDC systems, offering great potential for practical application in increasingly complex power grid environments. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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22 pages, 2507 KB  
Article
Resilience-Oriented Repair Strategy for Integrated Electricity and Natural Gas Systems with Line Pack Consideration
by Yuwei Wang, Wenchao Liu, Jian Gao, Boqiang Li, Yong Wang, Yunfei Chu and Xinyu Wu
Electronics 2025, 14(19), 3765; https://doi.org/10.3390/electronics14193765 - 24 Sep 2025
Cited by 1 | Viewed by 1061
Abstract
Earthquakes threaten the security and stability of urban integrated energy systems. Enhancing system resilience improves the ability to withstand seismic hazards. This paper proposes a coordinated post-disaster restoration strategy for integrated electricity and natural gas systems (IENGSs) that exploits natural gas line pack [...] Read more.
Earthquakes threaten the security and stability of urban integrated energy systems. Enhancing system resilience improves the ability to withstand seismic hazards. This paper proposes a coordinated post-disaster restoration strategy for integrated electricity and natural gas systems (IENGSs) that exploits natural gas line pack under seismic conditions. First, a line pack model is developed to quantify its impact on IENGS resilience. Subsequently, leveraging the load-supporting capability of line pack, we investigate how distribution network reconfiguration influences IENGS load recovery. Accounting for cross-system fault propagation during earthquakes, we formulate a post-disaster repair strategy incorporating line pack flexibility. Case studies using the IEEE 33-bus power system and a 7-node natural gas system validate the proposed strategy’s effectiveness and feasibility in enhancing seismic resilience. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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27 pages, 11539 KB  
Article
Adaptive Transient Power Angle Control for Virtual Synchronous Generators via Physics-Embedded Reinforcement Learning
by Jiemai Gao, Siyuan Chen, Shixiong Fan, Jun Jason Zhang, Deping Ke, Hao Jun, Kezheng Jiang and David Wenzhong Gao
Electronics 2025, 14(17), 3503; https://doi.org/10.3390/electronics14173503 - 1 Sep 2025
Cited by 2 | Viewed by 1501
Abstract
With the increasing integration of renewable energy sources and power electronic converters, Grid-Forming (GFM) technologies such as Virtual Synchronous Generators (VSGs) have emerged as key enablers of future power systems. However, conventional VSG control strategies with fixed parameters often fail to maintain transient [...] Read more.
With the increasing integration of renewable energy sources and power electronic converters, Grid-Forming (GFM) technologies such as Virtual Synchronous Generators (VSGs) have emerged as key enablers of future power systems. However, conventional VSG control strategies with fixed parameters often fail to maintain transient stability under dynamic grid conditions. This paper proposes a novel adaptive GFM control framework based on physics-informed reinforcement learning, targeting transient power angle stability in systems with high renewable penetration. An adaptive controller, termed the 3N-D controller, is developed to periodically update the virtual inertia and damping coefficients of VSGs based on real-time system observations, enabling anticipatory adjustments to evolving operating conditions. The controller leverages a reinforcement learning architecture embedded with physical priors, which captures the high-order differential relationships between rotor angle dynamics and control variables. This approach enhances generalization, reduces data dependency, and mitigates the risk of local optima. Comprehensive simulations on the IEEE-39 bus system with varying VSG penetration levels validate the proposed method’s effectiveness in improving system stability and control flexibility. The results demonstrate that the physics-embedded GFM strategy can significantly enhance the transient stability and adaptability of future power grids. Full article
(This article belongs to the Special Issue AI-Enhanced Strategies for Power Grid Operation and Resilience: Integration of Emerging Resources and Technologies)
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