Submit to Electronics Review for Electronics Propose a Special Issue

Journal Menu

Journal Browser

Internet of Things for Smart Grid

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 20841

Share This Special Issue

Special Issue Editors

Dr. Imed Ben Dhaou

E-Mail Website
Guest Editor

Department of Computing, University of Turku, 20500 Turku, Finland
Interests: VLSI; computer security; embedded systems for IoT; low-power design; approximate computing; DC microgrid; blockchain technology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ahmed Abdelgawad

E-Mail Website
Guest Editor

School of Engineering & Technology, Central Michigan University, Mt Pleasant, MI 48859, USA
Interests: Wireless Sensor Network (WSN); Internet of Things (IoT); Structural Health Monitoring (SHM); data fusion techniques for WSN; low power embedded system; video processing; digital signal processing; robotics; RFID; localization; VLSI; FPGA design
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hannu Tenhunen

E-Mail Website
Guest Editor

Department of Electronic Systems, Royal Institute of Technology, Stockholm, Sweden
Interests: embedded electronics for autonomic and smart systems and the IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart grid is a revolution in the energy sector in which the aging utility grid will be replaced with a grid that, among other features, integrates advanced control algorithms for teleoperations, incorporates distributed energy resources, and supports two-way communication between customers and the utility company.

The Internet of Things (IoT) is the enabling technology for the realization of smart-grid functions and services. Machine learning algorithms are used at the cloud and edge devices to process the data gathered by distributed sensors. This Special Issue is devoted to Internet of Things technology for smart grids. We seek original papers with novel research contributions in all aspects of design, analyses, algorithms, architecture, and implementation of smart grids using IoT technology. Topics of interest for this issue include but are not limited to:

IoT for distributed intelligence;
IoT security, privacy, and trust for smart grids;
Multi-agent systems using IoT technology;
Interoperability issues and middleware technologies;
Blockchain technology;
Cloud and edge/fog computing for smart grids;
IoT-based SCADA system design;
Real-time demand, demand response schemes, dynamic pricing, and price forecasting;
AMI design using the IoT;
Smart plug technologies;
Energy management in residential, commercial, and industrial applications;
Smart meters and demand side flexibility;
Blackout prevention;
Fault localization and restoration;
Communication technologies for smart grids.

Prof. Dr. Imed Ben Dhaou
Prof. Dr. Ahmed Abdelgawad
Prof. Dr. Hannu Tenhunen
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. Electronics 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 2400 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.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

20 pages, 1701 KiB

Open AccessArticle

Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System

by Imed Ben Dhaou

Electronics 2023, 12(19), 4041; https://doi.org/10.3390/electronics12194041 - 26 Sep 2023

Cited by 1 | Viewed by 1368

Abstract

The demand response program is an important feature of the smart grid. It attempts to reduce peak demand, improve the smart grid efficiency, and ensure system reliability. Implementing demand-response programs in residential and commercial buildings requires the use of smart meters and smart plugs. In this paper, we propose an architecture for a home-energy-management system based on the fog-computing paradigm, an Internet-of-Things-enabled smart plug, and a smart meter. The smart plug measures in real-time the root mean square (RMS) value of the current, frequency, power factor, active power, and reactive power. These readings are subsequently transmitted to the smart meter through the Zigbee network. Tiny machine learning algorithms are used at the smart meter to identify appliances automatically. The smart meter and smart plug were prototyped by using Raspberry Pi and Arduino, respectively. The smart plug’s accuracy was quantified by comparing it to laboratory measurements. To assess the speed and precision of the small machine learning algorithm, a publicly accessible dataset was utilized. The obtained results indicate that the accuracy of both the smart meter and the smart plug exceeds 97% and 99%, respectively. The execution of the trained decision tree and support vector machine algorithms was verified on the Raspberry Pi 3 Model B Rev 1.2, operating at a clock speed of 600 MHz. The measured latency for the decision tree classifier’s inference was 1.59 microseconds. In a practical situation, the time-of-use-based demand-response program can reduce the power cost by about 30%. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

20 pages, 8425 KiB

Open AccessArticle

Formalizing the Semantics of DDS QoS Policies for Improved Communications in Distributed Smart Grid Applications

by Alaa Alaerjan

Electronics 2023, 12(10), 2246; https://doi.org/10.3390/electronics12102246 - 15 May 2023

Cited by 1 | Viewed by 804

Abstract

Quality communication is a major challenges in large-scale and distributed smart grid applications. Several protocols and middleware have been proposed to address communication quality issues in those applications. DDS is a standard data-centric middleware for publish/subscribe communication. It has been proposed for smart grid to address both connectivity and communication quality issues. DDS provides multiple quality of service (QoS) policies to address reliability, latency, and data availability. One of the main challenges in adopting the standard in smart grids is the complexity of adopting and tailoring its QoS policies. This is because those policies are described informally introducing ambiguities, which hinders the precise implementation of DDS. To address this, we formalize the descriptions of DDS QoS policies using the object constraint language (OCL). We also clearly defined the design structural relations among DDS entities and QoS policies. In the process, we analyzed the dependencies among QoS policies and we built clear and concise structural relations. We then proposed feature modeling and a management layer to facilitate QoS tuning and to reduce development and configuration complexity. We implemented the proposed approach in a simulated power consumption domain. The results show that the approach improves the development process. They also show that the approach significantly improves the performance of DDS-enabled applications. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

26 pages, 3780 KiB

Open AccessFeature PaperArticle

XTM: A Novel Transformer and LSTM-Based Model for Detection and Localization of Formally Verified FDI Attack in Smart Grid

by Anik Baul, Gobinda Chandra Sarker, Pintu Kumar Sadhu, Venkata P. Yanambaka and Ahmed Abdelgawad

Electronics 2023, 12(4), 797; https://doi.org/10.3390/electronics12040797 - 05 Feb 2023

Cited by 7 | Viewed by 2470

Abstract

The modern smart grid (SG) is mainly a cyber-physical system (CPS), combining the traditional power system infrastructure with information technologies. SG is frequently threatened by cyber attacks such as False Data Injection (FDI), which manipulates the states of power systems by adding malicious data. To maintain a reliable and secure operation of the smart grid, it is crucial to detect FDI attacks in the system along with their exact location. The conventional Bad Data Detection (BDD) algorithm cannot detect such stealthy attacks. So, motivated by the most recent deep learning (DL) developments and data-driven solutions, a new transformer-based model named XTM is proposed to detect and identify the exact locations of data intrusions in real-time scenarios. XTM, which combines the transformer and long short-term memory (LSTM), is the first hybrid DL model that explores the performance of transformers in this particular research field. First, a new threshold selection scheme is introduced to detect the presence of FDI, replacing the need for conventional BDD. Then, the exact intrusion point of the attack is located using a multilabel classification approach. A formally verified constraints satisfaction-based attack vector model was used to manipulate the data set. In this work, considering the temporal nature of power system, both hourly and minutely sensor data are used to train and evaluate the proposed model in the IEEE-14 bus system, achieving a detection accuracy of almost 100%. The row accuracy (RACC) metric was also evaluated for the location detection module, with values of 92.99% and 99.99% for the hourly and minutely datasets, respectively. Moreover, the proposed technique was compared with other deep learning models as well, showing that the proposed model outperforms the state-of-the-art methods mentioned in the literature. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

22 pages, 6371 KiB

Open AccessArticle

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

by Osama Bassam J. Rabie, Praveen Kumar Balachandran, Mohammed Khojah and Shitharth Selvarajan

Electronics 2022, 11(24), 4144; https://doi.org/10.3390/electronics11244144 - 12 Dec 2022

Cited by 6 | Viewed by 1723 | Correction

Abstract

Smart grids are complex cyber-physical systems that incorporate smart devices’ communication capabilities into the grid to enable remote management and the control of power systems. However, this integration reveals numerous SCADA system flaws, which could compromise security goals and pose severe cyber threats to the smart grid. In conventional works, various attack detection methodologies are developed to strengthen the security of smart grid SCADA systems. However, they have several issues with complexity, slow training speed, time consumption, and inaccurate prediction outcomes. The purpose of this work is to develop a novel security framework for protecting smart grid SCADA systems against harmful network vulnerabilities or intrusions. Therefore, the proposed work is motivated to develop an intelligent meta-heuristic-based Artificial Intelligence (AI) mechanism for securing IoT-SCADA systems. The proposed framework includes the stages of dataset normalization, Zaire Ebola Search Optimization (ZESO), and Deep Random Kernel Forest Classification (DRKFC). First, the original benchmarking datasets are normalized based on content characterization and category transformation during preprocessing. After that, the ZESO algorithm is deployed to select the most relevant features for increasing the training speed and accuracy of attack detection. Moreover, the DRKFC technique accurately categorizes the normal and attacking data flows based on the optimized feature set. During the evaluation, the performance of the proposed ZESO-DRKFC method is validated and compared in terms of accuracy, detection rate, f1-score, and false acceptance rate. According to the results, it is observed that the ZESO-DRKFC mechanism outperforms other techniques with high accuracy (99%) by precisely spotting intrusions in the smart grid systems. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

27 pages, 9631 KiB

Open AccessArticle

Game-Theory-Based Multimode Routing Protocol for Internet of Things

by Shafiullah Khan, Muhammad Muneer Umar, Chunhua Jin, Shaozhang Xiao, Zeeshan Iqbal and Noha Alnazzawi

Electronics 2022, 11(24), 4134; https://doi.org/10.3390/electronics11244134 - 12 Dec 2022

Cited by 1 | Viewed by 1562

Abstract

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

18 pages, 6732 KiB

Open AccessArticle

Human Emotions Recognition, Analysis and Transformation by the Bioenergy Field in Smart Grid Using Image Processing

by Gunjan Chhabra, Edeh Michael Onyema, Sunil Kumar, Maganti Goutham, Sridhar Mandapati and Celestine Iwendi

Electronics 2022, 11(23), 4059; https://doi.org/10.3390/electronics11234059 - 06 Dec 2022

Cited by 12 | Viewed by 3972

Abstract

The passage of electric signals throughout the human body produces an electromagnetic field, known as the human biofield, which carries information about a person’s psychological health. The human biofield can be rehabilitated by using healing techniques such as sound therapy and many others in a smart grid. However, psychiatrists and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results, confirming the transformation. The present research was based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically, the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on the prediction capability of the proposed module and their overall satisfaction. The participants were strongly satisfied with the prediction capability (~81%) of the proposed module and future recommendations (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In the future, experimentation using different therapies and integrating more advanced techniques are anticipated to open new gateways in healthcare. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

17 pages, 1163 KiB

Open AccessFeature PaperArticle

Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station

by Ana Cabrera-Tobar, Nicola Blasuttigh, Alessandro Massi Pavan, Vanni Lughi, Giovanni Petrone and Giovanni Spagnuolo

Electronics 2022, 11(23), 3948; https://doi.org/10.3390/electronics11233948 - 29 Nov 2022

Cited by 8 | Viewed by 1527

Abstract

This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and [...] Read more.

C O_{2}

emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s

C O_{2}

emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

11 pages, 1630 KiB

Open AccessArticle

RobustSTL and Machine-Learning Hybrid to Improve Time Series Prediction of Base Station Traffic

by Chih-Hsueh Lin and Ulin Nuha

Electronics 2022, 11(8), 1223; https://doi.org/10.3390/electronics11081223 - 12 Apr 2022

Cited by 1 | Viewed by 1838

Abstract

Green networking is currently becoming an urgent compulsion applied for cellular network architecture. One of the treatments that can be undertaken to fulfill such an objective is a traffic-aware scheme of a base station. This scheme can control the power consumption of the cellular network based on the number of demands. Then, it requires an understanding of estimated traffic in future demands. Various studies have undertaken experiments to obtain a network traffic prediction with good accuracy. However, dynamic patterns, burstiness, and various noises hamper the prediction model from learning the data traffic comprehensively. Furthermore, this paper proposes a prediction model using deep learning of one-dimensional deep convolutional neural network (1DCNN) and gated recurrent unit (GRU). Initially, this study decomposes the network traffic data by RobustSTL, instead of standard STL, to obtain the trend, seasonal, and residual components. Then, these components are fed into the 1DCNN-GRU as input data. Through the decomposition method using RobustSTL, the hybrid model of 1DCNN-GRU can completely capture the pattern and relationship of the traffic data. Based on the experimental results, the proposed model overall outperforms the counterpart models in MAPE, RMSE, and MAE metrics. The predicted data of the proposed model can follow the patterns of actual network traffic data. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

25 pages, 5004 KiB

Open AccessArticle

LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements

by Babak Arbab-Zavar, Emilio J. Palacios-Garcia, Juan C. Vasquez and Josep M. Guerrero

Electronics 2021, 10(21), 2680; https://doi.org/10.3390/electronics10212680 - 02 Nov 2021

Cited by 4 | Viewed by 2518

Abstract

The introduction of low-power wide-area networks (LPWANs) has changed the image of smart systems, due to their wide coverage and low-power characteristics. This category of communication technologies is the perfect candidate to be integrated into smart inverter control architectures for remote microgrid (MG) applications. LoRaWAN is one of the leading LPWAN technologies, with some appealing features such as ease of implementation and the possibility of creating private networks. This study is devoted to analyze and evaluate the aforementioned integration. Initially, the characteristics of different LPWAN technologies are introduced, followed by an in-depth analysis of LoRa and LoRaWAN. Next, the role of communication in MGs with widespread elements is explained. A point-by-point LoRa architecture is proposed to be implemented in the grid-feeding control structure of smart inverters. This architecture is experimentally evaluated in terms of latency analysis and externally generated power setpoint, following smart inverters in different LoRa settings. The results demonstrate the effectiveness of the proposed LoRa architecture, while the settings are optimally configured. Finally, a hybrid communication system is proposed that can be effectively implemented for remote residential MG management. Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

► Show Figures

Figure 1

Other

Jump to: Research

2 pages, 159 KiB

Open AccessCorrection

Correction: Rabie et al. A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security. Electronics 2022, 11, 4144

by Osama Bassam J. Rabie, Praveen Kumar Balachandran, Mohammed Khojah and Shitharth Selvarajan

Electronics 2024, 13(1), 33; https://doi.org/10.3390/electronics13010033 - 20 Dec 2023

Viewed by 346

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Internet of Things for Smart Grid)

Show export options Show export options

Select all

Export citation of selected articles as:

Displaying articles 1-10

Journal Menu

Journal Browser

Internet of Things for Smart Grid

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (10 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI