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Energies
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Developments in IoT and Smart Power Grids
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Developments in IoT and Smart Power Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 1459

Special Issue Editors

Dr. David (Bong Jun) Choi

E-Mail Website
Guest Editor
Department of Computer Science, Soongsil University, Seoul 06978, Republic of Korea
Interests: Internet of Things; distributed intelligent energy grid; distributed algorithms; communication networks; network security
Special Issues, Collections and Topics in MDPI journals
Dr. Neetesh Saxena

E-Mail Website
Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
Interests: cyber-physical system security; smart grid; V2G and communication networks
Special Issues, Collections and Topics in MDPI journals
Dr. Sung-Guk Yoon

E-Mail Website
Guest Editor
Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea
Interests: deep learing using energy big data; data-driven active distribution network optimization, real-time energy demand and response; electric vehicle charging scheduling

Special Issue Information

Dear Colleagues,

The integration of the Internet of Things (IoT) with smart power grids is reshaping the energy sector, creating more resilient, efficient, and sustainable power systems. Recent advancements in IoT technology, such as edge computing, 5G networks, and AI-powered analytics, have greatly enhanced real-time monitoring, predictive maintenance, and decentralized energy management in smart grids. Key research topics in this domain include demand response optimization, cybersecurity in IoT-enabled grids, integration of renewable energy sources, and the development of interoperable communication protocols. Innovations in energy storage, electric vehicle (EV) charging, and the use of blockchain for secure energy transactions are also critical areas of exploration. As the energy landscape transitions towards greater decentralization, IoT-based smart grids are crucial for improving energy efficiency, ensuring grid stability, and facilitating the adoption of green technologies. Ongoing research focuses on addressing challenges related to scalability, privacy, and the management of massive data streams, ensuring the scalability and robustness of future smart grid infrastructures.

This Special Issue aims to highlight key advancements and research areas driving this evolution, from real-time grid management to the seamless integration of renewable energy sources. We invite researchers, engineers, and policymakers to contribute state-of-the-art research that explores the immense potential of IoT-driven smart grids to power the next generation of green energy solutions.

The proposed papers should consist of novel and original ideas and results, including, but not limited to, theoretical and applied research in the following topics:

  • IoT applications;
  • Data analytics;
  • Machine learning;
  • Demand response;
  • Edge computing;
  • Electric vehicles;
  • Communication networks;
  • Cybersecurity;
  • Privacy preservation;
  • Blockchain.

Dr. David (Bong Jun) Choi
Dr. Neetesh Saxena
Dr. Sung-Guk Yoon
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
  • IoT
  • machine learning
  • artificial intelligence
  • cyber security
  • electric vehicle
  • optimization
  • decentralized control
  • data-driven technologies

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Published Papers (3 papers)

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Research

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38 pages, 1179 KiB  
Article
Smart-Grid Technologies and Climate Change: How to Use Smart Sensors and Data Processing to Enhance Grid Resilience in High-Impact High-Frequency Events
by Eleni G. Goulioti, Theodora Μ. Nikou, Vassiliki T. Kontargyri and Christos A. Christodoulou
Energies 2025, 18(11), 2793; https://doi.org/10.3390/en18112793 - 27 May 2025
Viewed by 260
Abstract
Smart-grid technologies are essential to achieving sustainable high-level grid resilience. Integrating sensors and monitoring devices throughout grid infrastructure provides additional data on weather-related parameters in real-time, enabling the smart grid to respond appropriately to inclement weather and its associated challenges. The recording of [...] Read more.
Smart-grid technologies are essential to achieving sustainable high-level grid resilience. Integrating sensors and monitoring devices throughout grid infrastructure provides additional data on weather-related parameters in real-time, enabling the smart grid to respond appropriately to inclement weather and its associated challenges. The recording of all these data associated with each extreme weather event helps in the study and development of methodological tools for decision-making on issues of restoration and modification of the electricity network, with a view to enhancing its resilience and consequently ensuring the uninterrupted supply of electricity, even during the occurrence of these weather phenomena. This article focuses on enabling the utilization of meteorological data archives of past events, which demonstrate that natural disasters and extreme weather phenomena nowadays require network designs that can cope with the more frequent occurrence (high frequency) of events that have a significant impact (high impact) on the smooth operation of the network. Full article
(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)
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22 pages, 3026 KiB  
Article
Optimal Configuration of Mobile–Stationary Hybrid Energy Storage Considering Seismic Hazards
by Chengcheng Deng, Xiaodong Shen and Xisheng Tang
Energies 2025, 18(8), 2052; https://doi.org/10.3390/en18082052 - 16 Apr 2025
Viewed by 253
Abstract
The occurrence of extreme disasters, such as seismic hazards, can significantly disrupt transportation and distribution networks (DNs), consequently impacting the post-disaster recovery process. Restoring load using distributed generation represents an important approach to improving the resilience of DNs. However, using these resources to [...] Read more.
The occurrence of extreme disasters, such as seismic hazards, can significantly disrupt transportation and distribution networks (DNs), consequently impacting the post-disaster recovery process. Restoring load using distributed generation represents an important approach to improving the resilience of DNs. However, using these resources to provide resilience is not enough to justify having them installed economically. Therefore, this paper proposes a two-stage stochastic mixed-integer programming (SMIP) model for the configuration of stationary energy storage systems (SESSs) and mobile energy storage systems (MESSs) during earthquakes. The proposed model comprehensively considers both normal and disaster operation scenarios of DNs, maximizing the grid’s economic efficiency and security. The first stage is to make decisions about the location and size of energy storage, using a hybrid configuration scheme of second-life batteries (SLBs) for SESSs and fresh batteries for MESSs. In the second stage, the operating costs of DNs are evaluated by minimizing normal operating costs and reducing load loss during seismic events. Additionally, this paper proposes a scenario reduction method based on hierarchical sampling and distance reduction to generate representative fault scenarios under varying earthquake magnitudes. Finally, the progressive hedging algorithm (PHA) is employed to solve the model. The case studies of the IEEE 33-bus and 12-node transportation network are conducted to validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)
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Review

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31 pages, 372 KiB  
Review
Privacy-Preserving Machine Learning for IoT-Integrated Smart Grids: Recent Advances, Opportunities, and Challenges
by Mazhar Ali, Moharana Suchismita, Syed Saqib Ali and Bong Jun Choi
Energies 2025, 18(10), 2515; https://doi.org/10.3390/en18102515 - 13 May 2025
Viewed by 456
Abstract
Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies [...] Read more.
Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. Integrated IoT devices directly exchange data with the SG cloud, which increases the vulnerability and security threats to the energy system. The review aims to provide a comprehensive analysis of privacy-preserving machine learning (PPML) applications in IoT-Integrated SGs, focusing on non-intrusive load monitoring, fault detection, demand forecasting, generation forecasting, energy-management systems, anomaly detection, and energy trading. The study also highlights the importance of data privacy and security when integrating these applications to enable intelligent decision-making in smart grid domains. Furthermore, the review addresses performance issues (e.g., accuracy, latency, and resource constraints) associated with PPML techniques, which may impact the security and overall performance of IoT-integrated SGs. The insights of this study will provide essential guidelines for in-depth research in the field of IoT-integrated smart grid privacy and security in the future. Full article
(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)
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