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Artificial Intelligence in Energy Sector
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Artificial Intelligence in Energy Sector

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: closed (30 June 2025) | Viewed by 2223

Special Issue Editors

Dr. Yongli Zhu

E-Mail Website
Guest Editor
School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: power system stability; power system simulation; microgrid
Dr. Qianzhi Zhang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: distribution systems; microgrids; resilience enhancement; transportation electrification
Special Issues, Collections and Topics in MDPI journals
Dr. Yishen Wang

E-Mail Website
Guest Editor
China Electric Power Research Institute, Beijing 102209, China
Interests: power system operation; generative machine learning
Dr. Chaoxian Wu

E-Mail Website
Guest Editor
School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: transportation electrification; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern power systems have evolved with the development of advanced artificial intelligence (AI) and big data technology such as large language models (LLMs), graph machine learning, graph computing, explainable AI, federated learning, few-shot learning, embedded machine learning, edge computing and so on. This Special Issue aims to collect and publish recent progress made pertaining to either theoretical innovation or practical applications of recent cutting-edge AI methods in energy system-related areas (e.g., electrical power, gas, heat). The topics include, but are not limited to, the following:

  1. AI methods for active distribution networks (outage detection, restoration, etc.);
  2. AI methods for power markets (trading, auction, mechanism design, etc.);
  3. AI methods for microgrid operation and control (island operation, protection, etc.);
  4. AI methods for power system dynamics (simulation, model reduction, etc.);
  5. AI methods for power system reliability analysis (Monte Carlo acceleration, etc.);
  6. AI hardware for power system applications (edge computing, embedded AI, etc.);
  7. AI for building energy optimization and control;
  8. AI for EV charging scheduling;
  9. Other topics involving novel AI progress in energy systems.

Dr. Yongli Zhu
Dr. Qianzhi Zhang
Dr. Yishen Wang
Dr. Chaoxian 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 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

  • artificial intelligence (AI)
  • large language model (LLM)
  • graph machine learning
  • federated learning
  • energy system
  • microgrid

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Published Papers (4 papers)

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Research

30 pages, 2871 KB  
Article
Intelligent Management of Renewable Energy Communities: An MLaaS Framework with RL-Based Decision Making
by Rafael Gonçalves, Diogo Gomes and Mário Antunes
Energies 2025, 18(13), 3477; https://doi.org/10.3390/en18133477 - 1 Jul 2025
Viewed by 334
Abstract
Given the increasing energy demand and the environmental consequences of fossil fuel consumption, the shift toward sustainable energy sources has become a global priority. Renewable Energy Communities (RECs)—comprising citizens, businesses, and legal entities—are emerging to democratise access to renewable energy. These communities allow [...] Read more.
Given the increasing energy demand and the environmental consequences of fossil fuel consumption, the shift toward sustainable energy sources has become a global priority. Renewable Energy Communities (RECs)—comprising citizens, businesses, and legal entities—are emerging to democratise access to renewable energy. These communities allow members to produce their own energy, sharing or selling any surplus, thus promoting sustainability and generating economic value. However, scaling RECs while ensuring profitability is challenging due to renewable energy intermittency, price volatility, and heterogeneous consumption patterns. To address these issues, this paper presents a Machine Learning as a Service (MLaaS) framework, where each REC microgrid has a customised Reinforcement Learning (RL) agent and electricity price forecasts are included to support decision-making. All the conducted experiments, using the open-source simulator Pymgrid, demonstrate that the proposed agents reduced operational costs by up to 96.41% compared to a robust baseline heuristic. Moreover, this study also introduces two cost-saving features: Peer-to-Peer (P2P) energy trading between communities and internal energy pools, allowing microgrids to draw local energy before using the main grid. Combined with the best-performing agents, these features achieved trading cost reductions of up to 45.58%. Finally, in terms of deployment, the system relies on an MLOps-compliant infrastructure that enables parallel training pipelines and an autoscalable inference service. Overall, this work provides significant contributions to energy management, fostering the development of more sustainable, efficient, and cost-effective solutions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Sector)
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24 pages, 4175 KB  
Article
Joint Planning of Renewable Energy and Electric Vehicle Charging Stations Based on a Carbon Pricing Optimization Mechanism
by Shanli Wang, Bing Fang, Jiayi Zhang, Zewei Chen, Mingzhe Wen, Huanxiu Xiao and Mengyao Jiang
Energies 2025, 18(13), 3462; https://doi.org/10.3390/en18133462 - 1 Jul 2025
Viewed by 355
Abstract
The integration of renewable energy and electric vehicle (EV) charging stations into distribution systems presents critical challenges, including the inherent variability of renewable generation, the complex behavioral patterns of EV users, and the need for effective carbon emission mitigation. To address these challenges, [...] Read more.
The integration of renewable energy and electric vehicle (EV) charging stations into distribution systems presents critical challenges, including the inherent variability of renewable generation, the complex behavioral patterns of EV users, and the need for effective carbon emission mitigation. To address these challenges, this paper proposes a novel distribution system planning method based on the carbon pricing optimization mechanism. First, to address the strong randomness and volatility of renewable energy, a prediction model for renewable energy output considering climatic conditions is established to characterize the output features of wind and solar power. Subsequently, a charging station model is constructed based on the behavioral characteristics of electric vehicle users. Then, an optimized carbon trading price mechanism incorporating the carbon price growth rate is introduced into the carbon emission cost accounting. Based on this, a joint planning model for the power and transportation systems is developed, aiming to minimize the total economic cost while accounting for renewable energy integration and electric vehicle charging station deployment. In the case study, the proposed model is validated using the actual operational data of a specific region and a modified IEEE 33-node system, demonstrating the rationality and effectiveness of the model. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Sector)
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18 pages, 5750 KB  
Article
A Data-Driven Method for Deriving the Dynamic Characteristics of Marginal Carbon Emissions for Power Systems
by Bing Fang, Jiayi Zhang, Shuangyin Chen, Li Li, Shanli Wang and Mingzhe Wen
Energies 2025, 18(13), 3297; https://doi.org/10.3390/en18133297 - 24 Jun 2025
Viewed by 341
Abstract
Understanding the dynamic carbon emission status is vital for turning a power system into a low-carbon system. However, the existing research has normally considered the average carbon emissions as the indicator for the operation and planning of power systems. Detailed carbon emission responsibility [...] Read more.
Understanding the dynamic carbon emission status is vital for turning a power system into a low-carbon system. However, the existing research has normally considered the average carbon emissions as the indicator for the operation and planning of power systems. Detailed carbon emission responsibility is not well allocated to different demands within power systems, leading to inefficient emission control. To address this problem, this paper develops a data-driven method for accurately finding the characteristics of the nodal marginal emission factor without the requirement of real-time optimal power flow (OPF) simulation. First, the nodal marginal emission factor system is derived based on actual data covering a timespan of one year on top of the IEEE 118 system. Then, a Graphical Neural Network (GNN) is adopted to map both the spatial and temporal relationship between nodal marginal emission and other features, thereby identifying the marginal emission characteristics for different nodes of power transmission systems. Through case studies, fine-tuned GNNs estimate all nodal marginal emission factor (NMEF) values for power systems without the requirement of OPF simulation and achieve a 5.75% Normalized Root Mean Squared Error (nRMSE) and 2.52% Normalized Mean Absolute Error (nMAE). Last but not least, this paper brings a new finding: a strong inclination to reduce marginal emission rates would compromise economic operation for power systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Sector)
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22 pages, 7040 KB  
Article
Accelerating Solar PV Site Selection: YOLO-Based Identification of Sound Barriers Along Highways
by João Tavares and Carlos Santos Silva
Energies 2025, 18(9), 2366; https://doi.org/10.3390/en18092366 - 6 May 2025
Cited by 1 | Viewed by 596
Abstract
The exponential growth of the installation of solar photovoltaic systems has been a significant step in the energy transition toward reducing dependence on fossil fuels and mitigating climate change. This growth has raised concerns about land use, particularly in regions where large tracts [...] Read more.
The exponential growth of the installation of solar photovoltaic systems has been a significant step in the energy transition toward reducing dependence on fossil fuels and mitigating climate change. This growth has raised concerns about land use, particularly in regions where large tracts are allocated to solar farms. Highway infrastructures such as sound barriers occupy large land surfaces which are under-utilized and could therefore contribute to renewable energy generation without increasing the land use. This study proposes the application of the YOLO object detection algorithm to automatically identify and analyse the locations of sound barriers along highways using video or image data, and to estimate the potential energy output from photovoltaic systems installed on these barriers. The model has been trained and tested on sound barriers along Portuguese highways, achieving a mean average precision exceeding 0.84 for YOLOv10 when using training datasets containing more than 600 images. Using the geolocation of the images and the identification of the number of sound barriers from YOLO, it is possible to estimate the potential generation of electricity and inform decision makers on the technical–economic feasibility of using this infrastructure for energy generation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Sector)
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