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Developments in IoT and Smart Power Grids

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 9804

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Special Issue Editors

Dr. David (Bong Jun) Choi

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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

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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

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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 250 words) can be sent to the Editorial Office for assessment.

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 (5 papers)

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Research

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19 pages, 6991 KB

Open AccessArticle

An Adaptive Algorithm for Cellular IoT Network Selection for Smart Grid Last-Mile Communications

by Tanayoot Sangsuwan and Chaiyod Pirak

Energies 2026, 19(8), 1963; https://doi.org/10.3390/en19081963 - 18 Apr 2026

Viewed by 340

Abstract

Reliable last-mile connectivity at the cell edge remains a central challenge for Advanced Metering Infrastructure (AMI) in smart grids. This work addresses how to select between LTE-M and NB-IoT communications under weak-coverage conditions by combining field measurements with distribution-based channel modeling. We analyze multi-month Reference Signal Received Power (RSRP) datasets from three areas of a real AMI deployment (N = 30, 35, and 38 m, respectively) and fit canonical fading surrogates—Rayleigh, Rician, and Nakagami—to the normalized measurements. The principal decision statistic is the probability that RSRP falls below a practical threshold (−105 dBm), obtained from empirical and modeled CDF and translated into the predicted number of meters requiring fallback to NB-IoT. Across areas, Nakagami consistently provides the lowest or near-lowest Root Mean Square Error (RMSE) against empirical CDF and the closest agreement with observed fallback counts at −105 dBm, whereas Rayleigh tends to underestimate deep fade tails and Rician degrades when line-of-sight is weak. A threshold sweep sensitivity study (−110 to −89 dBm) using Area 3 illustrates how the predicted fallback population changes monotonically with the decision threshold and supports policy tuning. Overall, a CDF-anchored, Nakagami-guided rule at −105 dBm aligns technology selection with measured channel statistics, improving the robustness of Cellular IoT (CIoT) last-mile communications. Full article

(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)

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27 pages, 10840 KB

Open AccessArticle

Deep Multi-Task Forecasting of Net-Load and EV Charging with a Residual-Normalised GRU in IoT-Enabled Microgrids

by Muhammed Cavus, Jing Jiang and Adib Allahham

Energies 2026, 19(2), 311; https://doi.org/10.3390/en19020311 - 7 Jan 2026

Cited by 2 | Viewed by 674

Abstract

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments. Full article

(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)

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42 pages, 1179 KB

Open AccessFeature PaperArticle

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

Cited by 3 | Viewed by 2335

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 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 KB

Open AccessArticle

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 788

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 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 KB

Open AccessEditor’s ChoiceReview

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

Cited by 9 | Viewed by 4636

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 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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