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Internet of Things, Edge Computing, and Artificial Intelligence for Smart Grid

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

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 11878

Special Issue Editors


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Guest Editor
National Centre of Excellence for Food Engineering, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK
Interests: sustainability; energy; industry 4.0; process control; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart grid is considered as the next generation power grid infrastructure, with advanced information and communication technologies (ICT) to control and optimize power generation, transmission, and distribution. The Internet of Things (IoT) has been implemented in smart grids to enhance the efficiency, availability, and reliability of power systems by supporting various network functions throughout the generation, transmission, distribution, and consumption of electric power. IoT devices such as sensors, actuators, and smart meters have been deployed to monitor various areas in the smart grid ecosystem. Smart grid is arguably the largest and most complex IoT implementation in the world as smart grid can potentially connect millions of IoT devices distributed in very large areas running different communication protocols. Artificial intelligence (AI) can have a key function in synthesizing and discovering valuable insights from the increasingly massive and complex data generated from IoT-integrated smart grids. AI techniques can also be used to automate and optimize the smart grid.

This Special Issue focuses on the issues around the Internet of Things, edge computing, and artificial intelligence in smart grids. Topics of interest for publication include but are not limited to the following:

  • Internet of Things and edge computing implementation in smart grid systems;
  • Artificial intelligence implementation in smart grids;
  • Cybersecurity of IoT-based smart grids;
  • Data analytics in IoT-based smart grids;
  • Renewable energy and smart grids;
  • Smart cities and smart grids

Dr. Bernardi Pranggono
Dr. Hongwei 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

  • smart grid
  • Internet of Things
  • artificial intelligence
  • optimization
  • Industry 4.0
  • Internet of Things
  • green ICT

Published Papers (5 papers)

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Research

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13 pages, 660 KiB  
Article
Integrating Internet-of-Things-Based Houses into Demand Response Programs in Smart Grid
by Walied Alharbi
Energies 2023, 16(9), 3699; https://doi.org/10.3390/en16093699 - 26 Apr 2023
Cited by 1 | Viewed by 2462
Abstract
This paper presents a novel framework that mathematically and optimally quantifies demand response (DR) provisions, considering the power availability of Internet of Things (IoT)-based house load management for the provision of flexibility in the smart grid. The proposed framework first models house loads [...] Read more.
This paper presents a novel framework that mathematically and optimally quantifies demand response (DR) provisions, considering the power availability of Internet of Things (IoT)-based house load management for the provision of flexibility in the smart grid. The proposed framework first models house loads using IoT windows and occupant behavior, and then integrates IoT-based house loads into DR programs based on a novel mathematical optimization model to provide the optimal power flexibility considering the penetration of IoT-based houses in distribution systems. Numerical results that consider a 33-bus distribution system are reported and discussed to demonstrate the effectiveness of flexibility provisions, from integrating IoT-based houses into DR programs, on peak load reduction and system capacity enhancement. Full article
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19 pages, 1394 KiB  
Article
Machine Learning Based Protection Scheme for Low Voltage AC Microgrids
by Muhammad Uzair, Mohsen Eskandari, Li Li and Jianguo Zhu
Energies 2022, 15(24), 9397; https://doi.org/10.3390/en15249397 - 12 Dec 2022
Cited by 6 | Viewed by 1293
Abstract
The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification [...] Read more.
The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method. Full article
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19 pages, 2111 KiB  
Article
Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System
by Jayroop Ramesh, Sakib Shahriar, A. R. Al-Ali, Ahmed Osman and Mostafa F. Shaaban
Energies 2022, 15(21), 7981; https://doi.org/10.3390/en15217981 - 27 Oct 2022
Cited by 4 | Viewed by 2883
Abstract
Distribution transformers are an integral part of the power distribution system network and emerging smart grids. With the increasing dynamic service requirements of consumers, there is a higher likelihood of transformer failures due to overloading, feeder line faults, and ineffective cooling. As a [...] Read more.
Distribution transformers are an integral part of the power distribution system network and emerging smart grids. With the increasing dynamic service requirements of consumers, there is a higher likelihood of transformer failures due to overloading, feeder line faults, and ineffective cooling. As a consequence, their general longevity has been diminished, and the maintenance efforts of utility providers prove inadequate in efficiently monitoring and detecting transformer conditions. Existing Supervisory Control and Data Acquisition (SCADA) metering points are sparsely allocated in the network, making fault detection in feeder lines limited. To address these issues, this work proposes an IoT system for real-time distribution transformer load monitoring and anomaly detection. The monitoring system consists of a low-cost IoT gateway and sensor module which collects a three-phase load current profile, and oil levels/temperature from a distributed transformer network, specifically at the feeder side. The data are communicated through the publish/subscribe paradigm to a cloud IoT pipeline and stored in a cloud database after processing. An anomaly detection algorithm in the form of Isolation Forest is implemented to intelligently detect likely faults within a time window of 24 h prior. A mobile application was implemented to interact with the cloud database, visualize the real-time conditions of the transformers, and track them geographically. The proposed work can therefore reduce transformer maintenance costs with real-time monitoring and facilitate predictive fault analysis. Full article
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27 pages, 1848 KiB  
Article
The Internet of Things through Internet Access Using an Electrical Power Transmission System (Power Line Communication) to Improve Digital Competencies and Quality of Life of Selected Social Groups in Poland’s Rural Areas
by Wioletta Knapik, Magdalena Katarzyna Kowalska, Monika Odlanicka-Poczobutt and Marek Kasperek
Energies 2022, 15(14), 5018; https://doi.org/10.3390/en15145018 - 09 Jul 2022
Cited by 3 | Viewed by 1654
Abstract
In the 21st century, society has been undergoing a technology-driven transformation which heralds a new revolution that has potential to strengthen the position of an individual and community but may also lead to the marginalization of certain groups. The Internet of Things takes [...] Read more.
In the 21st century, society has been undergoing a technology-driven transformation which heralds a new revolution that has potential to strengthen the position of an individual and community but may also lead to the marginalization of certain groups. The Internet of Things takes advantage of the technology’s potential to improve digital competencies and the quality of life in society. The purpose of this paper is to obtain information about the digital competencies and needs of contemporary seniors and pre-senior age people, as well as socially sensitive groups from Poland’s rural areas. To strength the level of internet infrastructure in rural areas, power line communication (PLC) systems that utilize high-voltage line(s) between transformer substations are presented as a cost-effective communication tool. PowerLink IP has made PLC systems today more attractive and efficient than ever before. Based on nation-wide representative surveys conducted in deliberately selected groups, we collected information on digital competencies and formulated recommendations pertaining to the structure and contents of an innovative internet portal as regards offering, sharing, and the availability of commercial and social services targeted at seniors and other dependent groups. The recommended portal combines the needs of target groups with interests of entrepreneurs, self-government authorities, and NGOs. Full article
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Review

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27 pages, 5816 KiB  
Review
Model Predictive Control Based Energy Management System Literature Assessment for RES Integration
by Nor Liza Tumeran, Siti Hajar Yusoff, Teddy Surya Gunawan, Mohd Shahrin Abu Hanifah, Suriza Ahmad Zabidi, Bernardi Pranggono, Muhammad Sharir Fathullah Mohd Yunus, Siti Nadiah Mohd Sapihie and Asmaa Hani Halbouni
Energies 2023, 16(8), 3362; https://doi.org/10.3390/en16083362 - 11 Apr 2023
Cited by 4 | Viewed by 2594
Abstract
Over the past few decades, the electric power industry evolved in response to growing concerns about climate change and the rising price of fossil fuels. The usage of renewable energy sources (RES) rose as a remedy for these problems. The increased penetration of [...] Read more.
Over the past few decades, the electric power industry evolved in response to growing concerns about climate change and the rising price of fossil fuels. The usage of renewable energy sources (RES) rose as a remedy for these problems. The increased penetration of RES in the existing generation system increased the need for an intelligent energy management system (EMS) so that the system can operate in any possible circumstances. Many sectors of society, including the education sector, are working to realize the importance of this sustainable energy system. This paper reviews the process of selecting an efficient control technique for continuous power flow from different RES to meet the load demand requirement using an enhanced model predictive control (MPC)-based EMS framework. This EMS is a software platform to provide fundamental support services and applications to deliver the functionality needed for the effective operation of electrical generation and transmission facilities to ensure adequate security of energy supply at minimum cost. The centralized EMS with technical objectives focusing on power quality and seamless power flow can be achieved through dynamically enhanced MPC. Full article
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