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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: 15 October 2025 | Viewed by 2786

Special Issue Editors

Dr. Bernardi Pranggono

E-Mail Website
Guest Editor
School of Computing and Information Science, Anglia Ruskin University, Cambridge, UK
Interests: smart grid; cybersecurity; cloud computing; Internet of Things; green ICT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Teddy Surya Gunawan

E-Mail Website
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

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Published Papers (2 papers)

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Research

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24 pages, 3773 KiB  
Article
Smart Grid System Based on Blockchain Technology for Enhancing Trust and Preventing Counterfeiting Issues
by Ala’a Shamaseen, Mohammad Qatawneh and Basima Elshqeirat
Energies 2025, 18(13), 3523; https://doi.org/10.3390/en18133523 - 3 Jul 2025
Viewed by 277
Abstract
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in [...] Read more.
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids. Full article
(This article belongs to the Special Issue Internet of Things, Edge Computing, and Artificial Intelligence for Smart Grid—2nd Edition)
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Review

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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
Cited by 7 | Viewed by 1973
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
(This article belongs to the Special Issue Internet of Things, Edge Computing, and Artificial Intelligence for Smart Grid—2nd Edition)
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