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Artificial Intelligence Technologies Applied to Smart Grids
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Artificial Intelligence Technologies Applied to Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 3493

Special Issue Editors

Dr. Kanendra Naidu

E-Mail Website
Guest Editor
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Selangor, Shah Alam 40450, Malaysia
Interests: artificial intelligence in power system; graph theory; load frequency control; machine learning
Dr. Jagatheesan Kaliannan

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Paavai Engineering College, Namakkal 637018, India
Interests: power system operation and control; stability analysis; digital controllers and bio-inspired optimization techniques
Dr. Mohamad Binti Hasmaini

E-Mail Website
Guest Editor
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Selangor, Shah Alam 40450, Malaysia
Interests: power system; smart grid; islanding; electric vehicle

Special Issue Information

Dear Colleagues,

Advancements in artificial intelligence have led to new opportunities in enhancing smart grids which utilize advanced digital communication and control technologies to better manage electricity generation, transmission, and consumption. AI is able to improve grids’ efficiency, reliability, and sustainability by providing real-time monitoring, analysis, and control of grid operations. AI has been successfully applied for myriad smart grid challenges, such as the optimization of power flow, forecasting of energy demand and generation, transmission expansion problem, grid resiliency against cyber-attacks and environmental threats, renewable energy integration, fault detection and control system optimization. Enhancing smart grids with artificial intelligence will provide further benefits, such as minimizing greenhouse gas emissions, optimizing operation cost, and improving the performance and sustainability of the smart grid.

This Special Issue aims to provide a comprehensive and up-to-date overview of the latest research and insights on the application of AI in smart grid systems, with the goal of advancing the field and promoting sustainable energy solutions. This includes design, optimization, management, case studies, experimental results, and new methodologies for applying AI to smart grids.

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

  • All aspects of artificial intelligence, machine learning, and optimization integration related to power system components;
  • Renewable energy integration and management and hybrid renewable energy systems (HRESs);
  • Energy demand forecasting and optimization of the energy market;
  • Energy management systems (EMSs) and energy storage systems (ESSs);
  • Fault detection and diagnosis;
  • Load balancing and optimization;
  • Grid resiliency assessment and distributed energy resource management;
  • Smart grid communication and controller optimization;
  • Edge computing and data analytics for smart grids;
  • Life-cycle assessment of energy and decarbonization roadmaps;
  • Internet of things and cyber-physical energy systems.

Dr. Kanendra Naidu
Dr. Jagatheesan Kaliannan
Dr. Mohamad Binti Hasmaini
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

  • smart grid
  • energy management
  • grid resilience
  • artificial intelligence
  • sustainability
  • intelligent control

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Published Papers (2 papers)

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Research

23 pages, 4617 KiB  
Article
Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention
by Di Wang, Sha Li and Xiaojin Fu
Energies 2024, 17(16), 4142; https://doi.org/10.3390/en17164142 - 20 Aug 2024
Cited by 4 | Viewed by 1095
Abstract
Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition [...] Read more.
Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies Applied to Smart Grids)
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17 pages, 2851 KiB  
Article
Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning
by Zhiwei Liao, Ye Liu, Bowen Wang and Wenjuan Tao
Energies 2024, 17(7), 1762; https://doi.org/10.3390/en17071762 - 7 Apr 2024
Cited by 2 | Viewed by 1193
Abstract
Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address [...] Read more.
Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies Applied to Smart Grids)
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