Smart grid deployment: from a bibliometric analysis to a survey

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 3, June 2024, pp. 2436~2448
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i3.pp2436-2448  2436
Journal homepage: http://ijece.iaescore.com
Smart grid deployment: from a bibliometric analysis to a survey
Stéphane Wetinhoun1
, Pélagie Houngue1
, Sèmèvo Arnaud Roland M. Ahouandjinou1,2
, Jules Degila1
1
Laboratory of Data Science and Digital Inclusion, Institut de Mathématiques et de Science Physiques, Université d’Abomey-Calavi,
Port-Novo, Bénin
2
Computer Science and Applications Research Laboratory, Institut de Formation et de Recherche en Informatique,
Université d’Abomey-Calavi, Port-Novo, Bénin
Article Info ABSTRACT
Article history:
Received Sep 5, 2022
Revised Feb 5, 2024
Accepted Feb 9, 2024
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Keywords:
Bibliometric analysis
Bibliometrix
Deployment
Distributed energy resources
Energy efficiency
Smart grid
Smart grid deployment
This is an open access article under the CC BY-SA license.
Corresponding Author:
Stéphane Wetinhoun
Institut de Mathématiques et de Science Physiques, Université d’Abomey-Calavi
BP 613, Porto-Novo, Bénin
Email: stephane.wetinhoun@imsp-uac.org
1. INTRODUCTION
The advent of electricity at the beginning of the 19th
century marked the beginning of the second
industrial revolution. Indeed, for more than 150 years, the industrial sector has known unprecedented growth
and with it, the need for electrical energy. Some relevant factors have accentuated this growing need for
energy. These include population growth, emerging economies, the cities' expansion, and the constant search
for improving the quality of life, provided mainly by new technological processes and environmental issues
and challenges [1], [2]. Unfortunately, the conventional electrical grid became less suitable to efficiently
meet this need due to a low automation level in the distribution networks, a poor response to demand
management, and a long transmission distance which causes numerous online losses during distribution [3],
[4]. In addition, some critical incidents on the electrical grid have revealed important drawbacks of the grid
and their impacts on human activities [5]. To overcome these deficiencies from the existing grid known as
the traditional grid (TG) [6], the advancement of new information and communication technologies (NICT)
was used to propose the concept of a smart grid. A smart grid according to the United States Department of
Energy (DoE) [2], is an energy supply network that supports bi-directional power flow, it is distributed and

Int J Elec & Comp Eng ISSN: 2088-8708 
Smart grid deployment: from a bibliometric analysis to a survey (Stéphane Wetinhoun)
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automated in nature and permits real-time balancing of demand and supply via distributed and high-speed
computing and communications. smart grid has been built on top of the traditional electrical grid. So, it has
the primary conventional grid's features with some additional characteristics. As mentioned in [2] and [7], a
bi-directional power flow property and a distributed generation profile are the main characteristics of the
smart grid. Moreover, it is self‐healing from power disturbance events (self‐healing). In addition, Smart grid
enables the active participation of consumers in demand response (DR) management and operates resiliently
against physical and cyber‐attacks as mentioned in [3], [8]–[11]. Thus, some countries such as China, the
USA, and India have implemented the smart grid. At the same time, some notable institutions, such as the
IEEE, defined some standards for the smart grid. Also, much research about smart grid evolved in the
literature all over the world to understand smart grid's key functionalities, characteristics and advantages.
Mainly, the work was focused on finding the suitable architecture through which smart grids (SGs) can be
deployed. For instance, Petrovic et al. [12] and Ghasempour [13] studied how internet of things (IoT) can be
used for an smart grid-based architecture.
Moreover, an in-depth overview of smart grid elements and key functionalities has been highlighted
in studies [8], [14]–[16]. Useful backgrounds have been released in studies [17]–[20]. Therefore, we can
evaluate the study level on the topic using bibliometric analysis. As in previous works, Kpoze et al. [21]
conducted bibliometric research to evaluate the global trends of the research on smart grid architecture using
documents published in the Web of Science (WoS) database from 2000 to 2022. Mainly, their study shows
the most relevant work (authors and papers) and future research avenues on SG architecture. In the same
vein, Ananthavijayan et al. [22] conducted a bibliographical survey about software architecture for smart grid
systems. Indeed, bibliometric analysis requires a constant update of its insights.
Consequently, this paper provides an up-to-date overview of the evolution of smart grid research but
specifically focuses on smart grid’s deployment by combining two databases: WoS and Scopus. Mainly, the
paper emphasizes the trend of evolution of research on smart grid’s deployment, the most productive authors,
sources, countries. Furthermore, the study presents a survey of the main technological approaches used to
deploy smart grid, as mentioned in the literature. To give effect to the aforementioned directions, the
following research questions are addressed:
- What is the current volume of literature published on smart grid deployment, and how has it changed
over time?
- What are the top relevant authors and journals that have published the most cited papers on smart grid
deployment, and what are their characteristics?
- What main concepts have been studied on smart grid deployment, and how are they related?
- What technological approaches are used in smart grid deployment and what is their contribution?
- What is the potential research agenda for the smart grid deployment?
The rest of this paper is organized as follows: the methodology of our study is presented in section 2.
Section 3 presents the results and discussion. In section 4, we offer the recurrent smart grid deployment
technology existing in the literature. Finally, the conclusion and future research agenda are parts of section 5.
2. METHOD
To achieve the first objective of this work which is to understand the research development level on
smart grid deployment, we performed a bibliometric analysis on two datasets extracted from Web of Science
and Scopus databases respectively. Bibliometric analysis is the statistical analysis of bibliographic data,
mainly in scientific and technical literature. It measures the scientific activity in a subject category, journal,
country, topic, or other areas of interest [23]. A search was done within the WoS database on May 12th
, 2022,
using the search string: “smart grid” AND (deployment OR implementation). As a result, 5,663 relevant
documents were found and exported to BibTeX format. Using the same search string on the Scopus database
on May 18th
, 2022, we obtained a total of 7,015 documents. With the exportation constraints with the Scopus
database, we could only export the first 5,872 documents in the BibTeX format using a multi-step extraction
based on years. Then, the two datasets were analyzed using the biblioshiny interface of a bibliometric tool
called bibliometrix [24]. Afterwards, for presentation reasons, the tables extracted from this analysis were
processed in Microsoft Excel to produce more explicit graphs. Finally, we selected and read a number of
papers published mainly between 2014 and 2022 in order to highlight the different technological
approaches used in the deployment of smart grid. Figure 1 depicts the methodology used in this study. Key
outputs are provided in the sections below. This diagram illustrates the methodology used for bibliometric
study. The process was the same for both databases (Scopus and WoS). Then, several papers were read to
make a synthesis of the technologies useful for the deployment of smart grid. The result is presented in
section 4 of this paper.

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Int J Elec & Comp Eng, Vol. 14, No. 3, June 2024: 2436-2448
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Figure 1. Methodology
3. RESULTS ANALYSIS AND DISCUSSION
This section provides an in-depth analysis of the findings derived from our bibliometric study,
which involved an extensive examination of scholarly publications related to the deployment of smart grid.
The results offer valuable insights into the current state of research on smart grid implementation,
highlighting emerging trends and areas for future exploration. These findings are crucial for informing future
research directions and policy decisions in the field of smart grid technology.
3.1. Main information: global overview
Figure 2(a) and Figure 2(b) show a short overview of the preliminary information about the datasets
we extracted from the WoS and Scopus databases. Based on the output, we noticed fewer articles than other
types of documents in the two datasets: 2,098 articles versus 3,234 proceedings papers in WoS and 2,182
articles versus 3,004 conference papers in the Scopus dataset. Moreover, documents of the WoS dataset are
from 1978 different sources, while those of the Scopus dataset is from 2,170 sources. All this information
shows how the Smart grid implementation topic has gained researchers' interest and excitement.
3.2. Annual scientific production and key sources production
Figure 3(a) and Figure 3(b) present a graph depicting the annual scientific production on smart grid
implementation. In other words, it indicates the yearly number of papers related to smart grid deployment
that have been published in the literature over time as indexed in WoS and Scopus databases. Here, we
noticed that publications about Smart grid implementation started in 2006 in WoS with only one document
and faced a few changes until 2008. From 2008 to 2009, the number of published documents doubled (10 to
23). Then, we observed an explosion of paper publications, which maintained increased growth rates up to
2015, the number of publications reached its first peak (586 documents). After that, it declined slightly from
586 in 2015 to 562 in 2016 before resuming growth till 2018, when the maximum number of 660 documents
was published. This may be due to the trend of expanding new technologies in the industry worldwide during

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the last two decades. Thus, researchers have felt the urgency to renew the existing electrical network by
integrating NICTs. Since 2018, although the publication has stayed important, the number decreased slightly:
554 in 2019, 568 in 2020, and 416 in 2021. The decrease period (2019–2021) corresponds to the coronavirus
disease (COVID-19) pandemic. Thus, finding a vaccine was a global concern. The great majority of the
research of this period was focused on that objective. This could explain the decrease in the number of
publications in smart grid. However, by May 12th, 2022, the WoS database had already recorded 121
documents published on smart grid accounting for this year (2022), which is higher than the overall
publications on the topic noted at the beginning of 2006.
Coming to the Scopus dataset, although we could not obtain the whole documents available in the
database, it is noteworthy that the first 5,872 documents we extracted were published between 2013 and
2022, as shown in Figure 3(b). They represent 83.70% of the total (7015). Comparing our datasets between
2014–2022, we found out that the Scopus database has registered more documents than the Web of Science
(4559 versus 5341, respectively). Also, the publication trend in Scopus seems the same as in WoS. However,
the yearly documents registered in Scopus were superior to those reported in the WoS database except in
2015; WoS has registered 586 documents while Scopus has countered 547. Another critical point is that the
two databases registered many records in 2018.
(a) (b)
Figure 2. Main information about smart grid deployment documents published in two databases
(a) main information for WoS and (b) main information for Scopus
(a) (b)
Figure 3. Annual scientific production: (a) in WoS and (b) in Scopus
1 10 23
144
210
298
393
484
586
562
608
660
554568
416
121
2006
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
NUMBER
OF
DOCUMENTS
YEARS
Annual Scientific Production in WoS
531 556 547
614 634
758
681
638 662
251
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
NUMBER
OF
DOCUMENTS
YEARS
Annual Scientific Production in
Scopus

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What about the most relevant sources of smart grid deployment? Figure 4(a) and Figure 4(b) have
helped us focus on this question by displaying the top 10 most pertinent sources of WoS and Scopus
databases, respectively. The ranking is based on the total number of documents published by each source.
Based on these figures, we can observe that whether with WoS or with Scopus databases, the first three
positions are respectively occupied by the journals: “IEEE Transactions on Smart grid”, “Energies”, and
“IEEE Access”. This helps us to know that these journals are the most relevant in the literature concerning
smart grid deployment. Also, it seems reasonable to see the journal “IEEE Transactions on Smart grid” rank
the first position as it is known as a cross-disciplinary and internationally archival journal aimed at
disseminating results of research on smart grid that relates to, arises from, or deliberately influences energy
generation, transmission, distribution, and delivery [21].
(a) (b)
Figure 4. Top 10 most relevant sources (a) in WoS and (b) in Scopus
3.3. Source growth dynamics
Focusing on Table 1 and Table 2, we can observe how the document publication grows dynamically
in the five main sources. The tables show how the paper's publication evolves yearly in the primary five
sources among the ten we presented earlier. These sources have produced 892 documents for WoS and 555
documents for Scopus, as shown in Table 1 and Table 2 respectively. But “IEEE Transactions on Smart grid”
has the highest growth. Also, in the WoS database, as shown in Table 1, none of the five most abundant
sources has published the first document about our topic.
Table 1. Source growth dynamics in WoS
Year IEEE Transactions
on Smart grid
Energies IEEE Access IECON 2020 The 46th
Annual
Conference of the IEEE Industrial
Electronics Society
Renewable and
Sustainable Energy
Reviews
2006 0 0 0 0 0
2007 0 0 0 0 0
2008 0 0 0 0 0
2009 0 0 0 0 0
2010 13 0 0 0 0
2011 10 0 0 0 1
2012 29 1 0 0 1
2013 37 3 0 0 1
2014 41 2 0 0 6
2015 40 5 3 0 12
2016 32 9 5 0 12
2017 59 21 9 0 8
2018 75 21 10 0 17
2019 59 19 14 0 2
2020 56 22 20 68 0
2021 44 33 25 0 5
2022 17 14 9 0 2
Total 512 150 95 68 67
Top Most elevant Sources In WoS

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Table 2. Source growth dynamics in Scopus
Year IEEE Transactions on
Smart grid
Energies IEEE Access Applied Energy Renewable and Sustainable
Energy Reviews
2013 21 1 0 2 2
2014 21 2 0 2 6
2015 20 4 3 5 13
2016 15 11 6 3 12
2017 26 19 9 14 9
2018 17 15 13 18 19
2019 8 22 14 14 1
2020 11 19 19 9 3
2021 4 35 29 8 4
2022 4 17 14 7 5
Total 147 145 107 82 74
3.4. Top 10 authors based on the number of documents
In this section, we present the rank of the 10 most relevant authors with the high number of
documents about smart grid deployment or implementation recorded by WoS in Figure 5(a) and Scopus in
Figure 5(b), respectively. Figure 5(a) shows that authors who have published at least forty documents hold
the first four positions. But the author LIU has more publications with 56 papers. Wang and Zhang occupied
the second and third positions with 48 and 46 papers, respectively. Observing Figure 5(b), we noted that the
first four authors in the Scopus database have at least thirty-five documents. Zhang topped the list with
43 documents. An important point is the similarity between the two lists. Although we did retrieve all the
available documents from the Scopus database, we can note that eight (8) authors of these lists are the same
independently of their position. This information reveals that these authors have primarily published on smart
grid deployment. Another remark is that most of these authors are from Asia.
(a) (b)
Figure 5. Top 10 most relevant authors (a) in WoS and (b) in Scopus
3.5. Top 10 most cited documents
Figure 6(a) presents the most globally cited documents in WoS about smart grid deployment. It
reveals that Zhong and Weiss [25] is the most cited document with a total of 1,326 citations. It is followed by
article in [26] with 1,208 citations. Finally, Ellabban et al. [27] stands in the third position with 1,123
citations.
For the same purpose, the top 10 most cited documents in the Scopus database are highlighted in
Figure 6(b). Here, we can observe that the most cited document is Ellabban et al. [27] with 1,372 citations.
This document has been published by Ellabban et al. [27] in Renewable and Sustainable Energy Reviews.
Notably, it ranks third in the WoS database, as shown in Figure 6(a).
3.6. Top 10 country scientific production and most cited country
Figure 7(a) and Figure 7(b) present the addressed topic's top 10 most productive countries. Except
for some slight differences in the ranking, we can observe that the most productive countries are the same in
the WoS and Scopus databases. However, in both cases, the USA, China, and India occupy the first three
56
48 46
42
38 37 37
28 26 25
Number
of
articles
Authors
Top 10 Most Relevant Authors in WoS
43
38 37 35 34
30 28 26 26 26
Number
Of
Documents
Authors
Top 10 Most Relevant Authors in
Scopus

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positions. This underlines how much the researchers of these countries are interested and focused on smart
grids. The ten (10) most cited countries shown in Table 3 (for WoS) and Table 4 (for Scopus) indicate that
the two most productive countries rank the first positions: USA and China. Also, it is noteworthy that
compared to the most productive countries list, some countries such as Korea, Australia and Egypt appear in
the top 10 most cited list.
(a)
(b)
Figure 6. Top 10 most cited documents (a) in WoS and (b) in Scopus
(a) (b)
Figure 7. Top relevant country production (a) in WoS and (b) in Scopus
Total itation
Documents
Top Most ited Document in WoS
Top Most ited Document in Scopus

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Table 3. Most cited country in WoS
Rank Country Total Citations
1 USA 20,515
2 China 11,475
3 Canada 5,487
4 United Kingdom 3,445
5 Italy 2,729
6 Spain 2,541
7 India 2,438
8 Korea 1,997
9 Australia 1,710
10 Finland 1,473
Table 4. Most cited country in Scopus
Rank Country Total Citations
1 USA 7,691
2 China 7,091
3 Canada 2,439
4 United Kingdom 2,366
5 Italy 1,933
6 Spain 1,873
7 India 1,754
8 Korea 1,742
9 Australia 1,550
10 Finland 1,499
4. KEYS ENABLING TECHNOLOGIES FOR SMART GRID DEPLOYMENT: A SURVEY
In this section, we present categorization of different technologies that are used or can contribute to
SG deployment. This categorization results from a survey based on the reading of documents dealing with
technologies useful to SGs. As output, we mentioned five main technical approaches, which are: i) internet of
things approach; ii) big data, machine learning, and deep learning approach; iii) multi-agent system approach
iv) blockchain approach; and v) cloud computing approach.
4.1. IoT approach in smart grid
For developing the new smart grid, the IoT will be very useful for performing many automation and
monitoring tasks. Moreover, IoT has broadly been used in most of today's systems. Thus, IoT has presented
many characteristics and advantages which will be very profitable for smart grid. Ghasempour [13] notes
that: smart grid is one of the most important applications of IoT. In the fourth section of the report, the author
presents some main applications and services of IoT in smart grid. First, IoT will help to monitor the
electricity generation of power plants and predict the necessary power to supply consumers. Second, it can be
used to acquire electricity consumption and dispatch while protecting transmission lines and control
equipment. Third, IoT can be used to measure different parameters on the customer side using smart meters.
The author highlighted that, based on their findings, two types of integrated IoT architecture in the smart grid
exist in the literature: 3-layers and 4-layers architectures. Globally, the layers' names sometimes differ, but
their roles are almost identical. Finally, they provided some requirements and critical challenges about IoT
integration into smart grid. In addition, Hossain et al. [28] pointed out that most useful IoT devices (such as
smart meters) have already been developed. Energy generation and distribution automation are some IoT
applications. Also, expanding the use of IoT in smart grid will contribute to meeting the increasing electricity
demand and reduce energy wastage by creating a real-time tracker of distribution systems' supply and
demand sides. Moreover, authors in [15], [29]–[32] have proposed a comprehensive survey on the IoT-based
smart grid systems, which includes some existing architectures, applications, and prototypes.
4.2. Big data and machine learning approaches in smart grid
Also, the use of large monitoring devices, sensors, smart meters, phasor measurement units (PMUs),
and the bi-directional communication enabled by the smart grid leads to the generation of a considerable
quantity of data [28], [33], [34]. Mainly, according to [34], smart meters are a key source for data generation
as they are used to automatically collect energy consumption data in the grid. This introduces the concept of
Big data into the smart grid [34], [35]. This data will be drained from the control center. Based on the
analysis of the stored data, utilities and policymakers can then make relevant decisions about billings, energy
forecasting, grid fault predictions. Following this, machine learning (ML) technology is an ideal candidate
for this purpose [36]. Hossain et al. [28] and Thilakarathne et al. [36] have justified and presented some ML
and deep learning (DL) applications in the smart grid context. Specifically, Thilakarathne et al. [36]

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mentioned that energy forecasting, protection and security are the main domains where ML and DL can
broadly be used in smart grid.
4.3. Multi-agent system in smart grid
Multi-agent system (MAS) is another proposed smart grid-enabling technology. As mentioned in
[37], an Agent is a system (as an entity) that can perform autonomous actions based on interacting with the
environment and other agents. Its main characteristic is “autonomy” in making decisions and goals
achievement. Thus, a MAS is a set of agents that work collaboratively to achieve a defined purpose.
Reactivity, proactivity and sociability are other critical characteristics of an agent [37]. But why can MAS be
used in smart grids? For this purpose, Yılmaz et al. [38] highlighted that the current grid structure, as it is
known, has a limited ability to cope with fast cascading phenomena such as blockages and outages. Also,
since most of the current grid is provided by centralized power plants, maintaining continuous stability in the
grid is another critical challenge. Thus, based on the characteristics mentioned above, MAS can be used in a
distributed way through the new Smart grid architecture (a distributed architecture) to provide more reactivity
and proactivity of the grid management and control.
Regarding the same question, Merabet et al. [39] mentioned that MAS is used in different
application fields because of their architectures, which are based on agents that provide a natural way to
address current technological problems. Zhabelova et al. [37] proposed an agent with a distributed
architecture named distributed grid intelligence (DGI) as a solution. The proposed architecture is a horizontal
layered architecture based the on International Electrotechnical Commission (IEC) 61850 standard. It is made
of two layers: a reactive layer and a reflective layer, controlled by a set of Beliefs. On the other hand, Yılmaz
et al. [38] presents three applications that tackle Smart grid problems using MAS technology. These
applications are based on the Java Intelligent Agent Component (JIAC) framework. The applications and
their primary purpose are highlighted in Table 5.
Table 5. Overview of JIAC agents in smart grid applications [38]
Application Name Main Goal
ILIas (Intelligente Losungen zum Schutz vor
Kaskadenef-¨fekten in voneinander abhangigen
kritischen Infrastrukturen)
Research and create intelligent and scalable management systems that
provide prediction and reaction to cascading failure effects so that
actions to stabilize the managed infrastructure can be taken
Gesteuertes Laden V2.0 Use electric vehicles as mobile and distributed energy storage to increase
the utilization of wind energy and balance energy grids
Energy efficiency controlling (EnEffCo) project Production processes of the automotive industry in terms of energy costs
Shawon et al. [40] presented a review of technological frameworks for the application of
multi-agent systems and ICT infrastructure in smart grid. Mainly, they summarized five application areas of
MAS in smart grid implementation which are: i) smart control, operation and management; ii) protection;
iii) monitoring and diagnosis; iv) transactive energy management; and v) security. In studies [39] and [40],
key applications of MAS in smart grid are presented. Also, 'MAS' can help give maximum autonomy to the
distributed energy resources (DER) Microgrid units, achieving a high level of flexibility within smart grid. In
addition, Merabet et al. [39] completed their work with some advantages of MAS in smart grid. One of these
advantages is simulation. As the authors mentioned, smart grid has a highly dynamic infrastructure. Thus,
experimentation is the best way to make functionalities verification and proof of concept before deployment.
Unfortunately, implementations are sometimes unavailable for testing and verification as they are costly. So,
MAS could contribute to smart grid simulation based on Agent characteristics, including collaborative
interaction, negotiation, and autonomous decision-making. Therefore, Al-Hinai and Alhelou [41] proposed a
decentralized multi-agent approach to address fault restoration in automated distribution networks. Using
simulation-based analysis, they were able to highlight how MAS can efficiently improve fault restoration in
an automated distribution network by minimizing the number of affected consumers as well as the duration
of outages.
4.4. Blockchain in smart grid
Blockchain application in smart grids is presented by studies [42]–[49]. To understand how
blockchain may be used in smart grid, the authors compared the smart grid functionalities and the existing
blockchain applications. Based on the outputs, the authors mentioned a common blockchain application in
smart grid: peer-to-peer energy trading. Specifically, Agung and Handayani [42] stressed that blockchain
networks can be used to manage electricity transactions in smart grid. They justified their assumption that
smart grid has introduced a new type of user known as the prosumer. A prosumer is a consumer who, in

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addition to the energy from the grid, can produce and store its energy using renewable energy sources (solar and
wind). Then, he can share this energy with other consumers through the grid. Thus, it is possible to have an
overview of the transaction history using a blockchain network and smart contract. Also, Alladi et al. [44] also
completed the assumption by claiming that using a trading-based blockchain in the grid provides a decentralized
and secure platform that enables a peer-to-peer (p2p) trade of energy between consumers and prosumers.
Another smart grid blockchain use case is security and privacy [44]. This idea is also shared by
Arjomand et al. [43] and Erturk et al. [48] using cyber-physical infrastructure and grid enhancement terms.
Blockchain will help in data aggregation and privacy preservation [44]. As smart meters are used in the grid
to collect user consumption information, attackers could disclose 'users' private information by tracking
electricity usage information. Alladi et al. [44] mentioned that other cryptographic technologies can be used
in addition to blockchain to enable data privacy in smart grid. These technologies include zero-knowledge
proof (ZKP), elliptic curve digital signature algorithm (ECDSA) and linkable ring signatures [49]. In addition
to the use cases above, blockchain can contribute to electric vehicle charging in the smart grid [43], [44] and
[48]. More specifically, the Arjomand et al. [43] referenced a document in which they claimed that
blockchain would help electric vehicles reduce power fluctuations by finding their nearest available charging
station through bidding for charging.
4.5. Cloud computing in smart grid
Cloud computing is an emerging computation model that provides on-demand facilities and shared
resources over the internet. According to [50]–[53], cloud computing can be used in smart grid to store and
manage the huge quantity of data generated by sensing devices of the grid. Mainly, Forcan and Maksimović
[53] mentioned that the cloud could be combined with big data analytics for efficient load balancing in power
production and supply. In [50], a survey of cloud computing applications in smart grid is presented. The main
applications are demand response, micro-grid management, pick demand and dynamic pricing, real-time
monitoring, power monitoring and early-warning system, information interaction using a mobile agent, and
dynamic demand response. Furthermore, cloud applications in smart grid could contribute to improving some
characteristics of smart grid such as confidentiality and privacy, distributed data management, and
interoperability. However, there are some challenges with cloud computing applications in smart grid. These
include issues of latency, locality and network congestion due to the continuous sending of large volumes of
data to the cloud [53]. To overcome these issues, Bera et al. [50] and Forcan and Maksimović [53] argued the
use of fog and edge computing. As they mentioned, edge computing and fog computing are suitable to
overcome the issues as they allow decentralized and intelligent processing of data closer to the location they
are generated.
5. CONCLUSION
This study, as its primary goal, was proposed to show an overview of the current research level
about smart grid deployment. We could provide helpful insight regarding that purpose by using a
bibliometric analysis on two data sets extracted from WoS and Scopus databases. The main insights include
the annual scientific production, the most abundant sources, authors, countries and the most cited document
and authors. For example, we find that whatever we consider WoS or Scopus datasets, the top two most
productive countries are the same: USA and China. This means that researchers from these countries are
most interested in the smart grid topic.
On the other hand, research from less developed countries is expected to fill the gap to provide more
information about the challenges of smart grid deployment in each region of the world. Also, the three most
abundant sources in the two databases are respectively: “IEEE Transactions on Smart Grid”, “Energies”, and
“IEEE Access”. It is also noteworthy that the growth of the number of documents published per year in the
two databases is slightly the same. But 2018 has encountered a high volume of documents. This study also
highlighted the leading technologies (internet of things, artificial intelligence, blockchain, multi-agent
systems, and cloud computing), which can be used to tackle most of the problems the smart grid faces. Future
research could focus on real case studies of these technologies' usage in smart grid.
CONTRIBUTION
The goal of this study has been two-phased. Firstly, we have provided a cross overview of the
research trend about smart grid deployment or implementation using two databases. Secondly, we presented
key enabling technologies that can contribute to deploying smart grid. Using bibliometric analysis, our study
provides insights to researchers, scholars, and decision-makers. First, by providing outputs such as the annual
scientific production, we hope to help researchers, especially those in electrical and computer engineering,
find smart grids an interesting research domain. Specifically, research about its deployment is still needed

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despite the significant literature contribution over the years. In addition, by presenting the most relevant
sources, authors, and most cited documents, we hope to direct the researcher's choice when they need to find
documents to learn more about the topic. Second, the results could help scholars (electrical and computer
engineering scholars especially), adapt their course programs by pointing out key technologies suitable for
smart grid deployment that have been presented in the literature. It could also be useful for decision-makers
when designing an action plan for the next-generation electrical grid, the smart grid. This study also wishes
to establish to the research community the fact that the smart grid domain is a rapidly evolving and thus
promising field. Also, by using two relevant scientific databases, WoS and Scopus, our study has confirmed
the assumption that smart grid deployment or implementation has gained interest from researchers over time.
Finally, our study reveals that five technology approaches can mainly contribute to smart grid deployment:
i) internet of things, ii) big data, machine learning and deep learning, iii) multi-agent systems, iv) blockchain
and v) cloud computing.
LIMITATIONS
Behind the useful outputs and contributions of this study, some limitations can be pointed out. First,
as we used keywords for the search, we could not cover all published documents about the topics. Also,
although using two databases (WoS and Scopus) has expanded the scope of the study, it could eventually
limit the search. Next studies could combine more databases. Second, as we could not extract all documents
found in the Scopus database, comparing its outputs to WoS could present some bias. Finally, another
limitation is that we selected some documents we read to offer key technologies suitable for smart grid
deployment. Therefore, it could miss some papers presenting another concept or technology not broadly
covered in the literature. A complete literature review on smart grid deployment will help tackle this
limitation.
ACKNOWLEDGEMENTS
This work has been conducted in the scope of the CYBERSCADA project. We thank the Digital
Science and Technology Network (DSTN) for allowing us to work on such a project with financial support.
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BIOGRAPHIES OF AUTHORS
Stéphane Wetinhoun received the B.Sc. in electrical and computer engineering
from the Higher National Institute of Industrial Technology (former IUT) of Lokossa, Benin
Republic, in 2019. He graduated for a M.Sc. in data science from the Institute of Mathematical
Sciences and Physics (IMSP) in Benin Republic, which is part of the University of Abomey-
Calavi. He is interested in the internet of things, applications, networks and systems, software
engineering and data science. He can be contacted at: stephane.wetinhoun@imsp-uac.org.
Pélagie Houngue received the B.Sc. and M.Sc. degrees in computer science from
the University of Abomey-Calavi (UAC), Benin, respectively in 2003 and 2007. Furthermore,
she received her Ph.D degree in computer science security at the University of Milan, Italy, in
2011. Since 2015, she is lecturer/researcher in computer science at the Institute of
Mathematical Sciences and Physics (IMSP) in Benin, which is part of the University of
Abomey-Calavi. She is currently responsible for preparatory classes in computer science at the
same Institute. She had co-organized many conferences, held in Benin, which papers have
been published in Springer. Her research interests include applications of IoT, security issues
in IoT, machine learning in health and education fields, P2P network security and
security/architectures in smart grid. She can be contacted at email: pelagie.houngue@imsp-
uac.org.
Sèmèvo Arnaud Roland M. Ahouandjinou is associate professor in computer
science and Head of IT Security Department at the Institute of Research Training in computer
science of Abomey-Calavi University from Benin. He is also research associate member of
LISIC laboratory, University of the Littoral Opal Coast (ULCO) from France. He received the
Ph.D. degree in computer engineering, automatic, image processing and signal at ULCO,
France. His current research deals with the design of digital smart system for human activities
monitoring in natural and farming environment. We have developed the Farmer Interface
Application (FIA) project with the International Institute of Tropical Agriculture (IITA) of
Benin and the Norwegian Institute of Bioeconomy Research (NIBIO), from Norway. We are
currently investigated another research projects proposed for the DSTN - Digital Science and
Technology of ACE- Impact (Africa Higher Education Centers of Excellence) which are smart
grid through the SCADA and AI4CARDIO projects. He can be contacted at email:
arnaud.ahouandjinou@univ-littoral.fr and arnaud.ahouandjinou@uac.bj.
Jules Degila is a professor of computer science at the University of Abomey-
Calavi, Benin. His research interests include networks and systems, the internet of things,
blockchain and inclusive ICT. He has led the Operations Research and Computer Science
department at the Institute of Mathematics and Physics (IMSP). During the last fifteen years,
he has held different management, executive, and board member positions for Western and
African telecommunications companies. Guest speakers at various technologies vulgarization
events, he has also advised many companies and governments as a strategist in ICT for
development. Jules received a Ph.D. in electrical engineering from École Polytechnique de
Montréal, Canada, in 2004. He can be contacted at email: jules.degila@imsp-uac.org.

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