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Internet of Things, Edge Computing, and Artificial Intelligence for Smart Grid—2nd Edition

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 September 2024 | Viewed by 410

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Interests: engineering technology; electrical and electronic engineering; signal processing; parallel computing; IoT; instrumentation and control

Special Issue Information

Dear Colleagues,

Smart grid is considered as the next generation power grid infrastructure, with advanced information and communication technologies (ICTs) to control and optimize power generation, transmission, and distribution. The Internet of Things (IoTs) and edge computing have 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 aims to focus on the issues around the Internet of Things, edge computing, and artificial intelligence in smart grids. Potential topics of 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
Prof. Dr. Teddy Surya Gunawan
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
  • green ICT

Published Papers (1 paper)

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Review

19 pages, 2707 KiB  
Review
Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission
by Israth Jahan Chowdhury, Siti Hajar Yusoff, Teddy Surya Gunawan, Suriza Ahmad Zabidi, Mohd Shahrin Bin Abu Hanifah, Siti Nadiah Mohd Sapihie and Bernardi Pranggono
Energies 2024, 17(11), 2595; https://doi.org/10.3390/en17112595 - 28 May 2024
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Abstract
A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has [...] Read more.
A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS. Full article
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