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Energies
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AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets
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AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1589

Special Issue Editors

Dr. Stefanie Kuenzel

E-Mail Website
Guest Editor
Department of Electronic Engineering, Royal Holloway, University of London, Egham, UK
Interests: HVDC transmission systems; wind generation; smart meters
Dr. Xiaoyu Zhang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei, China
Interests: smart grid; deep reinforcement learning; federated learning; intelligent decision-making; information security; new energy and distributed generation

Special Issue Information

Dear Colleagues,

We are pleased to invite contributions to this Special Issue on the topic of “AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets”. Prospective authors’ work may focus on a single or multiple topics included in this Special Issue. Modern power systems are rapidly evolving in a manner that is characterized by the convergence of physical infrastructure with advanced communication and computational layers. This transformation has ushered in an era where artificial intelligence (AI) plays a pivotal role in enhancing the resilience, efficiency, and sustainability of power systems. In today’s interconnected world, the integration of AI within cyber–physical energy systems has become indispensable. From optimizing grid operations to revolutionizing energy markets, AI technologies are driving unprecedented innovation. These advancements are not without challenges, particularly concerning security, privacy, and the seamless integration of legacy systems. This Special Issue seeks to explore these complexities, offering a platform for novel research and practical insights that address the multifaceted nature of AI-driven energy systems.

This Special Issue encourages new insight and discussion from experts across academia and industry and topics of interest for publication include, but are not limited to:

  1. The application of AI technologies in the optimization and control of power systems;
  2. Power system planning assisted by AI;
  3. Energy markets and energy trade using AI technologies;
  4. Security and privacy issues in cyber–physical energy systems;
  5. The application of generative AI for decision making in energy systems;
  6. Energy prediction and monitoring using deep learning;
  7. Smart homes and building energy management based on reinforcement learning;
  8. Case studies sharing experience from practitioners in the field;
  9. The decommissioning of legacy infrastructure aided by AI.

Dr. Stefanie Kuenzel
Dr. Xiaoyu Zhang
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

  • cyber–physical systems
  • deep learning
  • generative AI
  • reinforcement learning
  • security and privacy
  • case studies in planning, operations, and markets

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

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18 pages, 3640 KiB  
Article
Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining Strategy
by Rongbin Li, Jian Zhang and Fangming Deng
Energies 2025, 18(3), 716; https://doi.org/10.3390/en18030716 - 4 Feb 2025
Abstract
As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis [...] Read more.
As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. The SCO-WFP algorithm is then applied to extract frequent itemsets, revealing deep associations between accident severity and risk factors. Experimental results show that, compared to traditional methods, the SCO-WFP algorithm significantly improves both accuracy and execution speed. The findings demonstrate the method’s effectiveness in mining frequent itemsets from text semantics, facilitating a deeper understanding of the relationship between risk factors and accident severity. Full article
(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)
►▼ Show Figures

Figure 1

Figure 1
<p>CBOW and Skip-gram model architectures.</p> Full article ">Figure 2
<p>Process of mining FP-Growth frequent itemsets.</p> Full article ">Figure 3
<p>Process of mining power accident risk factors.</p> Full article ">Figure 4
<p>Segmentation clustering visualisation.</p> Full article ">Figure 5
<p>Change in support of feature items before and after text weight.</p> Full article ">Figure 6
<p>Change in support of feature terms before and after combining sparse coefficients.</p> Full article ">Figure 7
<p>Comparison of the degree of association of the three methods.</p> Full article ">Figure 8
<p>(<b>a</b>) Comparison of running time and (<b>b</b>) comparison of memory usage under different support thresholds.</p> Full article ">Figure 9
<p>Degree of correlation between different feature terms and accident levels.</p> Full article ">Figure 10
<p>Comprehensive ranking of risk features.</p> Full article ">

Review

Jump to: Research

26 pages, 3839 KiB  
Review
Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
by Chisom E. Ogbogu, Jesse Thornburg and Samuel O. Okozi
Energies 2025, 18(4), 994; https://doi.org/10.3390/en18040994 - 19 Feb 2025
Abstract
Smart grid reliability and efficiency are critical for uninterrupted service, especially amidst growing demand and network complexity. Wide-Area Measurement Systems (WAMS) are valuable tools for mitigating faults and reducing fault-clearing time while simultaneously prioritizing cybersecurity. This review looks at smart grid WAMS implementation [...] Read more.
Smart grid reliability and efficiency are critical for uninterrupted service, especially amidst growing demand and network complexity. Wide-Area Measurement Systems (WAMS) are valuable tools for mitigating faults and reducing fault-clearing time while simultaneously prioritizing cybersecurity. This review looks at smart grid WAMS implementation and its potential for cyber-physical power system (CPPS) development and compares it to traditional Supervisory Control and Data Acquisition (SCADA) infrastructure. While traditionally used in smart grids, SCADA has become insufficient in handling modern grid dynamics. WAMS differ through utilizing phasor measurement units (PMUs) to provide real-time monitoring and enhance situational awareness. This review explores PMU deployment models and their integration into existing grid infrastructure for CPPS and smart grid development. The review discusses PMU configurations that enable precise measurements across the grid for quicker, more accurate decisions. This study highlights models of PMU and WAMS deployment for conventional grids to convert them into smart grids in terms of the Smart Grid Architecture Model (SGAM). Examples from developing nations illustrate cybersecurity benefits in cyber-physical frameworks and improvements in grid stability and efficiency. Further incorporating machine learning, multi-level optimization, and predictive analytics can enhance WAMS capabilities by enabling advanced fault prediction, automated response, and multilayer cybersecurity. Full article
(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)
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Figure 1

Figure 1
<p>Schematic of a typical WAMS implementation [<a href="#B15-energies-18-00994" class="html-bibr">15</a>].</p> Full article ">Figure 2
<p>A typical infinite-bus power grid model.</p> Full article ">Figure 3
<p>Centralized wide-area measurement system (WAMS) architecture [<a href="#B46-energies-18-00994" class="html-bibr">46</a>].</p> Full article ">Figure 4
<p>SGAM layers, zones, and domains [<a href="#B17-energies-18-00994" class="html-bibr">17</a>].</p> Full article ">Figure 5
<p>Operationalization of PMU-based WAMS in infinite-bus grid model with communication system.</p> Full article ">Figure 6
<p>(<b>a</b>) Block diagram of how WAMS integrates into physical power system through strategic PMU deployment to become CPPS. (<b>b</b>) Schematic of potential application in multi-energy system with wide-area network (WAN) and neighborhood-area network (NAN).</p> Full article ">
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