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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 3971

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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Published Papers (6 papers)

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Research

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20 pages, 536 KiB  
Article
Quantum Neural Networks for Solving Power System Transient Simulation Problem
by Mohammadreza Soltaninia and Junpeng Zhan
Energies 2025, 18(10), 2525; https://doi.org/10.3390/en18102525 - 13 May 2025
Viewed by 136
Abstract
Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential-algebraic equations (DAEs). We introduce two [...] Read more.
Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential-algebraic equations (DAEs). We introduce two novel Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the Polynomial-Friendly QNN, proposing them as effective alternatives to conventional simulation techniques. Our application of these QNNs successfully simulates two small power systems, demonstrating their potential to achieve good accuracy. We further explore various configurations, including time intervals, training points, and the selection of classical optimizers, to optimize the solving of DAEs using QNNs. This research not only marks a pioneering effort in applying quantum computing to power system simulations but also expands the potential of quantum technologies in addressing intricate engineering challenges. Full article
(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)
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17 pages, 4201 KiB  
Article
Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning
by Wenxiang Luo, Yang Shen, Zewen Li and Fangming Deng
Energies 2025, 18(7), 1796; https://doi.org/10.3390/en18071796 - 3 Apr 2025
Viewed by 338
Abstract
In a distributed photovoltaic system, photovoltaic data are affected by heterogeneity, which leads to the problems of low adaptability and poor accuracy of photovoltaic power prediction models. This paper proposes a distributed photovoltaic power prediction scheme based on Personalized Federated Multi-Task Learning (PFL). [...] Read more.
In a distributed photovoltaic system, photovoltaic data are affected by heterogeneity, which leads to the problems of low adaptability and poor accuracy of photovoltaic power prediction models. This paper proposes a distributed photovoltaic power prediction scheme based on Personalized Federated Multi-Task Learning (PFL). The federal learning framework is used to enhance the privacy of photovoltaic data and improve the model’s performance in a distributed environment. A multi-task module is added to PFL to solve the problem that an FL single global model cannot improve the prediction accuracy of all photovoltaic power stations. A cbam-itcn prediction algorithm was designed. By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics’ information and improve the feature extraction ability of time series data in photovoltaic power prediction. The experimental analysis shows that CBAM-iTCN is 45.06% and 42.16% lower than a traditional LSTM, Mae, and RMSE. Compared with FL, the MAPE of the PFL proposed in this paper is reduced by 9.79%, and for photovoltaic power plants with large data feature deviation, the MAPE experiences an 18.07% reduction. Full article
(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)
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18 pages, 3423 KiB  
Article
Multi-Time Scale Scenario Generation for Source–Load Modeling Through Temporal Generative Adversarial Networks
by Liang Ma, Shigong Jiang, Yi Song, Chenyi Si and Xiaohan Li
Energies 2025, 18(6), 1462; https://doi.org/10.3390/en18061462 - 17 Mar 2025
Viewed by 243
Abstract
With the large-scale integration of distributed power sources, distribution network planning is undergoing significant transformations. To further enhance the efficiency and practicality of distribution network planning, it is essential to model the uncertainties in source–load dynamic scenarios. However, traditional scenario generation methods struggle [...] Read more.
With the large-scale integration of distributed power sources, distribution network planning is undergoing significant transformations. To further enhance the efficiency and practicality of distribution network planning, it is essential to model the uncertainties in source–load dynamic scenarios. However, traditional scenario generation methods struggle with high-dimensional variables and complex spatiotemporal characteristics, posing severe challenges for distribution network planning. To address these issues, this paper proposes a multi-time scale source–load scenario generation method based on temporal convolutional networks and multi-head attention mechanisms within a temporal generative adversarial network framework. This algorithm not only enhances the richness and robustness of source–load scenarios in distribution networks but also serves as a valuable reference for medium-long-term analysis and planning. Finally, the results present a set of daily, weekly, and monthly multi-time scale source–load scenarios, and multiple evaluation indicators are utilized to evaluate the quality of the generated scenarios; the accuracy of the generated scenarios is increased by about 2%. Full article
(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)
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17 pages, 908 KiB  
Article
Deep Reinforcement Learning-Based Distribution Network Planning Method Considering Renewable Energy
by Liang Ma, Chenyi Si, Ke Wang, Jinshan Luo, Shigong Jiang and Yi Song
Energies 2025, 18(5), 1254; https://doi.org/10.3390/en18051254 - 4 Mar 2025
Viewed by 685
Abstract
Distribution networks are an indispensable component of modern economic societies. Against the background of building new power systems, the rapid growth of distributed renewable energy sources, such as photovoltaic and wind power, has introduced many challenges for distribution network planning (DNP), including different [...] Read more.
Distribution networks are an indispensable component of modern economic societies. Against the background of building new power systems, the rapid growth of distributed renewable energy sources, such as photovoltaic and wind power, has introduced many challenges for distribution network planning (DNP), including different source-load compositions, complex network topologies, and varied application scenarios. Traditional heuristic algorithms are limited in scalability and struggle to address the increasingly complex optimization problems of DNP. The emergence of new artificial intelligence provides a new way to solve this problem. Based on the above discussion, this paper proposes a DNP method based on deep reinforcement learning (DRL). By defining state space and action space, a Markov decision process model tailored for DNP is formulated. Then, a multi-objective optimization function and a corresponding reward function including construction costs, voltage deviation, renewable energy penetration, and electricity purchase costs are designed to guide the generation of network topology schemes. Based on the proximal policy optimization algorithm, an actor-critic-based autonomous generation and adaptive adjustment model for DNP is constructed. Finally, the representative test case is selected to verify the effectiveness of the proposed method, which indicates that the proposed method can improve the efficiency of DNP and promote the digital transformation of DNP. Full article
(This article belongs to the Special Issue AI Facilitated Cyber–Physical Energy Systems—Planning, Operation, and Markets)
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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
Viewed by 591
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)
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Review

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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
Viewed by 1005
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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