Global Research Trends in AI and Blockchain for Smart Grids: A Bibliometric Analysis with a Focus on Morocco (2014–2024)

Abstract

As Information and Communication Technologies (ICTs) are increasingly incorporated into energy systems, smart grids are becoming essential parts of modern energy infrastructures. However, this integration exposes them to significant cybersecurity risks, highlighting the need for effective prevention and mitigation strategies to enhance resilience. Due to their promising implications, blockchain and artificial intelligence (AI) have emerged as key technologies to strengthen security, improve data analysis, and optimize processes in smart grids. This bibliometric study investigates key trends, opportunities, and evolving dynamics within the field, analyzing a dataset of 9611 articles from the Scopus database, covering the period 2014–2024. To evaluate the research, we utilized a range of bibliometric tools, including Bibliometrix R, VOSviewer, and Python. We used these tools to identify impactful articles. We also analyzed country and institutional productivity, assessed prolific authors, and uncovered emerging trends. The findings highlight a shift towards advanced smart grids incorporating AI and blockchain, with significant progress in Morocco’s research since 2016. Morocco ranks 36th globally and 3rd in Africa, contributing to the National Digital Morocco 2030 Strategy, which promotes digital transition and innovation, particularly in smart grids, to bolster the country’s energy system.

1. Introduction

In light of the increasing impact of climate change and high demand for energy, the global energy system has transformed from a standard and unidirectional framework to a more flexible and open model, where consumers and other stakeholders play an integral role in its design and operation. The energy industry has undergone significant transformation through the integration of ICT, leading to the establishment of a dynamic and intelligent system that enhances efficiency across energy generation, transmission, and distribution via intelligent networks known as smart grids [1].

As stated in the Smart Grid System Report from the U.S. Department of Energy [2], smart grids are modern electricity networks that integrate advanced digital technologies, including sensing, data processing, communication, computing, and control systems to coordinate and optimize various grid operations. By incorporating sustainable solutions like microgrids, they aim to provide a greener energy system with enhanced efficiency and improved power delivery. As such, the topology of smart grids is highly flexible and can combine multiple architectures to meet the specific needs of urban, rural, or industrial areas. These distributed networks are designed to promote resilience, energy efficiency, and the integration of renewable technologies, while enabling real-time energy management and active participation from both users and manufacturers [3].

In Morocco, the adoption of smart grids is emphasized as a key section of the New Development Model (NMD), which aims to promote decentralized and efficient energy production for competitive electricity access across regions [4]. Aligned with its Energy Transition Strategy for 2021–2023 and its overarching renewable energy objectives—achieving 37.2% of installed electricity capacity from renewable sources in 2021, with a target of 52% by 2030—the country established a modern smart grid research and development center, the smart grids Test Lab, in February 2023. This facility is located in the Green City of Benguerir within the Green & Smart Building Park [5]. This center serves as a hub for testing and showcasing advanced Smart Grid technologies and is equipped with a 300 kVA microgrid.

Overall, smart grids offer substantial benefits to the energy ecosystem by utilizing ICT to improve energy distribution efficiency, enhance grid stability, and promote sustainable energy solutions. However, the distributed systems underlying smart grid operations can trigger cybersecurity risks, resulting in disruptions in infrastructure, power outages, unauthorized energy consumption, privacy violations, and other issues, as emphasized in [6]. In addition, smart grids often integrate older infrastructure with modern systems, creating compatibility gaps and potential weaknesses for cyberattacks. Furthermore, the flexible topology, which allows components to join or leave the grid dynamically, can lead to insecure configurations if not properly managed.

On the other hand, the growing number of devices at the grid’s edge is driving an exponential increase in data generation, creating a pressing need for advanced computational capabilities and improved data processing systems to effectively handle, integrate, and analyze the information. This, in turn, enhances data security and supports decision-making, both of which are crucial for maintaining the efficiency and resilience of smart grids. Consequently, researchers have been prompted to explore innovative technologies, notably AI and blockchain, to achieve these objectives.

The adoption of AI techniques in Smart Grid systems is becoming increasingly prominent, reflecting the growing recognition of their potential, as evidenced by the increasing volume of AI research dedicated to applications on smart grids throughout the last decade. Indeed, AI techniques have made significant advancements in key tasks, such as anomaly detection [7,8], which helps identify irregular grid behaviors that could lead to performance issues. In addition, electric power load forecasting can be improved through the use of machine learning and deep learning model, which enable more precise predictions of future electricity demands [9]. This capability is important for enhancing power generation, allocation and ensuring sufficient supply to meet consumer demands [10].

Alternatively, blockchain technology provides a distributed digital ledger that guarantees safe, transparent, and immutable data storage in a peer-to-peer (P2P) network [11]. In the energy sector, blockchains offer a reliable platform for managing transactions by ensuring immutability and transparency. Within smart grids, blockchain can optimize energy transactions by securely tracking the flow of energy between decentralized producers and consumers [12]. Furthermore, smart contracts within the blockchain network can automatically validate and execute transactions based on predefined conditions, ensuring data integrity and preventing unauthorized access [13]. In addition, blockchain facilitates auditing by providing access to a complete transaction history, ensuring the precision and trustworthiness of energy exchanges in smart grids. Due to its transparent and secure nature, blockchain enhances trust, accountability, and operational efficiency in energy management systems, framing it as an optimal approach to address Smart Grid challenges.

Given the diverse contributions of blockchain and AI technologies in enhancing the security of smart grids, this review adopts a bibliometric approach to assess the expanding body of scientific literature on Smart Grid security, emphasizing the integration of blockchain and AI. The focus is on Morocco’s energy transition, exploring how these technologies support and fortify the country’s efforts in securing its smart grid infrastructure. This study evaluated a dataset of 9,611 manuscripts published between 2014 and 2024, with the primary objective of developing a strategic framework for utilizing advanced technologies to tackle the evolving cybersecurity challenges confronting smart grids, both globally and within Morocco.

This paper is structured as such: Section 2 presents a summary of the existing literature on smart grid security, with a focus on the role of AI and blockchain technologies. Section 3 outlines the methodology adapted for the bibliometric analysis, detailing the data selection criteria and the tools and strategy applied. Section 4 presents the analysis results, highlighting Morocco’s contributions to the global research landscape. Section 5 presents a detailed discussion of the study’s implications and potential contributions, highlighting recent advancements in AI and blockchain technologies for securing smart grids. Finally, Section 6 summarizes the key findings and highlights opportunities for further research.

2. Related Work

This section presents an overview of recent bibliometric studies that focus on the application of AI and blockchain technologies in smart grid security. It highlights major contributions, methodological approaches, and limitations that shape the current state of research in this evolving field as presented in Table 1.

Within the past decade, researchers have extensively explored the use of AI and blockchain solutions for securing smart grids, and it is essential to examine the existing literature to highlight the limitations and opportunities within this field. This section provides a detailed exploration of recent bibliometric studies in this area as presented in Table 1.

Study [14] emphasized the role of AI in optimizing grid operations and enhancing cybersecurity, highlighting its ability to predict energy demand and detect anomalies in real time. The authors suggested that machine learning models, such as Long Short-Term Memory (LSTM) networks, can effectively analyze time-series data to anticipate and mitigate disruptions in energy distribution. This finding shows the capability of AI to enhance grid reliability and performance.

In [15], the authors explored the implementation of deep learning models for detecting anomalies, particularly in identifying threats like False Data Injection (FDI) attacks. The study emphasized that Conditional Deep Belief Networks (CDBNs) excel in recognizing patterns in complex datasets, making them suitable for detecting sophisticated cyber threats. However, the computational demands of these models remain a challenge, especially in resource-constrained environments.

The bibliometric analysis [16] highlights the role of AI in renewable energy infrastructures and its integration with smart grid technology. The authors stated that AI models improve accuracy in predicting solar radiation and wind speed, which are essential for optimizing energy generation and distribution in smart grids. Furthermore, the study demonstrates the use of AI in enhancing the stability of smart grids under varying operational conditions. Nevertheless, region-specific conditions such as unique climate conditions have been identified as potential limitations that can cause detection failures in AI models.

Table 1. Summary of recent bibliometric reviews on smart grid security, AI, and blockchain.

Study	Database (s)	${\mathrm{N}}^{\circ }$ of Articles	Time Span	Strengths and Limitations
artificial intelligence in smart grids: A bibliometric analysis and scientific mapping study (2023) [14]	Scopus, WoS	1926	2005–2022	+ Inclusion of various descriptive statistics (e.g., annual scientific production, impactful authors, affiliations, countries) and thematic evolution provides a holistic view of the field. + Highlights challenges such as international collaboration gaps, low cross-country studies. – Exclusion of the gray literature, industry reports, and non-indexed journals could limit the findings’ applicability.
Big Data and artificial intelligence Application in Energy Field: A Bibliometric Analysis (2022) [15]	SCI-E, SSCI (WoS)	3842	2001–2020	+ Divided research evolution into three distinct stages to provide historical context and to help understand shifts in research priorities. + Broad time span covering the evolution of big data, AI, and energy search over 20 years. – The bibliometric analysis is limited to specific indices (SCI-E and SSCI), potentially missing interdisciplinary contributions.
artificial intelligence in renewable energy: A comprehensive bibliometric analysis (2022) [16]	WoS	469	1985–2022	+ Use of multiple tools for bibliometric analysis enhances the depth of analysis. + Retrieved publications across a very broad timeframe of 39 years to provide an in-depth historical context. – Limited dataset size. – Reliance solely on the Web of Science Core Collection, excluding relevant publications in other databases.
blockchain in smart grids: A Bibliometric Analysis and Scientific Mapping Study (2024) [17]	Scopus, WoS	1041	2015–2022	+ Article presents evidence of the growing interest in the topic, with a 127.9% annual growth rate in research publications. – Limited time frame of 7 years means that recent breakthroughs are not fully captured. – Dominance of conference papers (424) compared to journal articles (383).
blockchain technology in the smart city: A bibliometric review (2021) [18]	Scopus	148	2016–2020	+ Introduces theoretical frameworks like SET and TCE. + Suggests managerial implications for practitioners and government officials. – Very limited time frame of 4 years. – Very limited dataset of 148 publications.
blockchain and energy: A bibliometric analysis and review (2021) [19]	WoS	166	2008–2019	+ Comprehensive methodology through the use of co-citation analysis and exploratory factor analysis to identify six distinct research streams. – Very limited dataset. – Reliance on Web of Science as the sole database.
Proposed study	Scopus	9611	2014–2024	+ A set of bibliometric tools (e.g., Bibliometrix R, VOSviewer) were used for analysis. + Usage of an exhaustive and large dataset to ensure the robustness of findings. + Includes visual representations of data, such as trends in publication types and citation networks to enhance comprehension. – Reliance on a single database (Scopus) may limit the comprehensiveness of the dataset.

Note: “+” refers to strengths and “–” refers to limitations of each study. All data are extracted from the original sources.

Table 1. Summary of recent bibliometric reviews on smart grid security, AI, and blockchain.

Study	Database (s)	${\mathrm{N}}^{\circ }$ of Articles	Time Span	Strengths and Limitations
artificial intelligence in smart grids: A bibliometric analysis and scientific mapping study (2023) [14]	Scopus, WoS	1926	2005–2022	+ Inclusion of various descriptive statistics (e.g., annual scientific production, impactful authors, affiliations, countries) and thematic evolution provides a holistic view of the field. + Highlights challenges such as international collaboration gaps, low cross-country studies. – Exclusion of the gray literature, industry reports, and non-indexed journals could limit the findings’ applicability.
Big Data and artificial intelligence Application in Energy Field: A Bibliometric Analysis (2022) [15]	SCI-E, SSCI (WoS)	3842	2001–2020	+ Divided research evolution into three distinct stages to provide historical context and to help understand shifts in research priorities. + Broad time span covering the evolution of big data, AI, and energy search over 20 years. – The bibliometric analysis is limited to specific indices (SCI-E and SSCI), potentially missing interdisciplinary contributions.
artificial intelligence in renewable energy: A comprehensive bibliometric analysis (2022) [16]	WoS	469	1985–2022	+ Use of multiple tools for bibliometric analysis enhances the depth of analysis. + Retrieved publications across a very broad timeframe of 39 years to provide an in-depth historical context. – Limited dataset size. – Reliance solely on the Web of Science Core Collection, excluding relevant publications in other databases.
blockchain in smart grids: A Bibliometric Analysis and Scientific Mapping Study (2024) [17]	Scopus, WoS	1041	2015–2022	+ Article presents evidence of the growing interest in the topic, with a 127.9% annual growth rate in research publications. – Limited time frame of 7 years means that recent breakthroughs are not fully captured. – Dominance of conference papers (424) compared to journal articles (383).
blockchain technology in the smart city: A bibliometric review (2021) [18]	Scopus	148	2016–2020	+ Introduces theoretical frameworks like SET and TCE. + Suggests managerial implications for practitioners and government officials. – Very limited time frame of 4 years. – Very limited dataset of 148 publications.
blockchain and energy: A bibliometric analysis and review (2021) [19]	WoS	166	2008–2019	+ Comprehensive methodology through the use of co-citation analysis and exploratory factor analysis to identify six distinct research streams. – Very limited dataset. – Reliance on Web of Science as the sole database.
Proposed study	Scopus	9611	2014–2024	+ A set of bibliometric tools (e.g., Bibliometrix R, VOSviewer) were used for analysis. + Usage of an exhaustive and large dataset to ensure the robustness of findings. + Includes visual representations of data, such as trends in publication types and citation networks to enhance comprehension. – Reliance on a single database (Scopus) may limit the comprehensiveness of the dataset.

Note: “+” refers to strengths and “–” refers to limitations of each study. All data are extracted from the original sources.

Blockchain technology has also gained significant attention for its role in decentralizing energy management systems. A bibliometric analysis [17] examined blockchain’s potential in data integrity and storage. The study highlights how distributed ledger technologies could ensure data authenticity and prevent unauthorized access, making blockchain a robust solution for smart grid cybersecurity. However, scalability issues and latency introduced by consensus mechanisms, such as Proof-of-Work, were identified as significant challenges to adopting these technologies.

In the bibliometric study [18], authors explored how blockchain technology presents important potential for smart grids by enhancing efficiency, security, and decentralization. Authors argued that blockchain technology allows for deploying Peer-to-Peer platforms for energy trading, allowing decentralized energy producers (e.g., households with solar panels) to trade excess energy directly with consumers. Nonetheless, the study emphasized that blockchain’s current limitations in handling large-scale transactions need to be addressed.

In another study [19], researchers reviewed the use of blockchain for securing energy transactions, focusing on its ability to provide tamper-proof records through smart contracts. The authors argued that blockchain enables secure and transparent energy trading, reducing reliance on centralized authorities. Despite its advantages, the study noted issues associated with excessive energy consumption and integration complexity.

Notably, while AI and blockchain have been extensively studied as separate technologies, their combined potential to revolutionize smart grid management remains an area of emerging research with significant opportunities for innovation. Regardless, bibliometric studies on this topic often rely on limited datasets or employ conventional methods, leading to inconsistent conclusions and clustering outputs. These limitations suggest that a more comprehensive bibliometric analysis, encompassing a larger dataset and employing advanced visualization tools, is necessary to bridge existing gaps. From these perspectives, the present study seeks to offer a detailed bibliometric overview of the adoption of AI and blockchain for smart grid security, by analyzing 9611 articles from the Scopus database through advanced tools. It seeks to advance the understanding of these technologies and their integration, ultimately contributing to the design of more robust and environmentally friendly smart grid systems.

3. Materials and Methods

A Bibliometric analysis is a systematic and quantitative method used to evaluate the literature in a research field by examining publication trends, citation relationships, and thematic trends. This approach provides researchers with knowledge of the dynamics of scientific knowledge within a given domain, facilitating the identification of key contributors, influential works, and emerging topics [20]. The present bibliometric analysis aims to comprehensively explore and evaluate the scope of research on AI and blockchain technologies for smart grid security. The steps of our process are outlined as follows:

• Formulating the research questions relevant to the study.
• Highlighting the selected data source.
• Constructing the search query and collecting data.
• Summarizing and synthesizing the findings of the study.

The papers reviewed in this study are sourced from reputable and renowned publishers, such as MDPI, IEEE, Elsevier, and Springer. These publishers are widely acknowledged for their significant contributions to scientific research and are known for publishing high-quality work across various disciplines.

3.1. Research Questions

For a comprehensive and concise analysis, a bibliometric study should be structured around clearly defined research questions. This study focuses on the following questions, aimed at examining the role of AI and blockchain in securing smart grids:

1.

What is the present state of research regarding the application of AI and blockchain in smart grids?

2.

Which authors have made the most significant contributions and demonstrated high productivity in this field?

3.

Which institutions are leading and making the most significant contributions in this field of research?

4.

What is the current status of research in Morocco, and what progress has been achieved in recent years?

5.

What are the security challenges and limitations associated with AI-based approaches in smart grids?

6.

How well do blockchain-based methods mitigate the limitations of AI-based security systems in smart grids?

7.

What are the existing challenges and limitations of approaches that integrate both AI and blockchain technologies?

8.

What are the potential future research avenues in this field?

3.2. Data Source

The choice of Scopus as the primary database for this bibliometric study is justified by its extensive coverage of the scholarly literature across multiple disciplines [21], particularly African scientific publications. Indeed, a focus on Moroccan research was essential for this analysis, and, as noted by the National Center for Scientific and Technical Research (CNRST), Moroccan universities are well represented in the Scopus database [22], making the latter an appropriate resource for conducting bibliometric studies focused on Morocco. The “advanced search” feature of Scopus was used to conduct the search, with the option of searching within the documents’ titles, abstracts, and keywords. The search was conducted on 19 December 2024.

While Scopus offers strong metadata consistency and tool compatibility (e.g., VOSviewer, Bibliometrix), we acknowledge that limiting the analysis to a single database introduces potential selection bias. Other platforms, such as Web of Science or IEEE Xplore, may contain relevant publications not indexed in Scopus. Future bibliometric research could benefit from multi-database integration to improve comprehensiveness and cross-validation of findings.

3.3. Data Collection

The search queries were constructed using keywords pertinent to the research subject, in addition to the inclusion of keywords in relation to themes of AI and blockchain due to their extensive use for improving the security and performance of smart grids [23]. Publications were collected through the execution of a combination of search queries highlighted in

Table 2. Search queries and keywords employed.

Topic	Keywords
Energy (smart grids)	“Distributed Energy System* ” OR “Distributed Power Generation” OR “Electric Power Transmission Networks” OR “Renewable Energy Resources” OR “Distributed Energy Resources” OR “Smart grid*” OR “Microgrid*” OR “Renewable Energies” OR “Electric Load Flow” OR “Electric Load Balancing” OR “Green Computing”
Cybersecurity	“Cybersecurity*” OR “Network Security” OR “Security System*” OR “Security of Data” OR “Energy Security” OR “Information Management” OR “Computer Crime” OR “Electricity Theft” OR “Data Privacy” OR “Quality of Service” OR “Power Control” OR “Power Quality” OR “Fault Detection” OR “Intrusion Detection” OR “Crime” OR “Risk*”
artificial intelligence	“artificial intelligence*” OR “AI” OR “Learning System” OR “Machine Learning” OR “Deep Learning” OR “Neural Networks” OR “Reinforcement Learning” OR “Automation” OR “Learning Algorithms” OR “Big Data” OR “Intelligent System” OR “Federated Learning”
blockchain	“blockchain*” OR “Distributed Ledger” OR “Energy Trading” OR “Smart Contract” OR “Peer to Peer” OR “Digital Storage” OR “Decentralized” OR “Cryptography” OR “Ethereum” OR “Game Theory” OR “Consensus Algorithms” OR “Consensus” OR “Trading System”

Note: The asterisk (*) is used as a wildcard character to represent any number of characters in search queries.

.

Figure 1 demonstrates the search strategy employed in this research.

The process begins with the identification stage, where a total of 11,371 records were retrieved from Scopus. These records were initially refined based on specific criteria, including publication years (2014–2024), document types (conference papers, articles, reviews, book chapters, and books), and language (English). After applying these criteria, 10,070 documents were deemed suitable for further screening.

In the screening phase, the records were assessed for subject relevance, leading to the exclusion of 229 records due to irrelevancy and another 138 due to missing or unavailable metadata. After this exclusion, the set had 9703 documents for eligibility assessment.

During the eligibility stage, the availability of full-text documents was evaluated. Records lacking full-text access were excluded, resulting in the removal of 92 additional entries. Eventually, 9611 records were included in the final bibliometric review.

3.4. Research Method

The study employed several bibliometric indicators that are necessary for evaluating the impact and the productivity of scholarly publications and research entities. The used indicators are listed as follows:

• Citation count: The citation count refers to how many times a publication has been cited by other works, serving as a gauge of its influence and impact within the academic field [24].
• Publication count: Publication count represents the total number of works produced by a research entity within a given time period, serving as an indicator of the productivity and output [25].
• h-index: Defined by Hirsch in 2005 [26], the h-index quantifies both the productivity and influence of a researcher’s work. It is determined as the maximum value h, where h signifies the number of publications by a researcher that have been cited at least h times.
• g-index: As an extension of the h-index, the g-index factors in the citation counts of highly cited works. It is defined as the highest value g, where the g most-cited papers collectively have received $g^2$ or more citations.
• SJR (SCImago Journal Rank): SJR [27] is an indicator of journal prestige that evaluates the citations received by articles and the quality of the citing journals. Journals are classified into quartiles according to their SJR score.
• CiteScore: CiteScore [28] evaluates the citation impact of academic journals by calculating the average number of citations per article published within a specific journal over a period of three years.
• SNIP (Source-Normalized Impact per Paper): SNIP [29] is a journal metric that evaluates impact while accounting for varying citation practices across fields, normalizing based on the citation potential inherent to each subject area.

The analysis was performed using a collection of bibliometric tools, such as Bibliometrix R, which, along with its graphical user interface (GUI) extension, biblioshiny, was utilized for comprehensive bibliometric analysis. This R-based package offers a wide range of functionalities for exploring publication trends, authorship patterns, and citation networks [30]. In addition, Flourish was used to create our visualizations, seeing as the tool offers a range of templates and customization options for various types of charts, maps, and other types of visualizations. Lastly, VOSviewer was utilized for visualizing and analyzing bibliometric networks, particularly co-authorship and co-citation networks. This tool offers advanced network analysis capabilities, helping to identify research communities and influential publications within one or multiple fields [31].

We began the study by providing an extensive overview of the dataset, which consists of the following:

• Publication trends: Graphs and tables highlighting the evolution of research within the field and providing a comprehensive summary of dataset traits.
• Publication types: A chart showing the distribution of document types.
• Subject areas: A set of charts visualizing the distribution of publications across subject areas and the yearly volume of publications in each subject area.

The second section of our study involves employing science mapping to gain insights into the intellectual, social, and conceptual structure of research. Within this section, we focused on the following:

• Most productive and highly cited authors: A table presenting the top 10 authors based on productivity, alongside the top 10 authors with the highest number of citations.
• Leading journals: A study of the top 10 most productive and highly cited journals in AI and blockchain research for securing smart grids, including key performance metrics such as publication count, CiteScore, SJR, SNIP, and quartile rankings.
• Prominent institutions: An analysis of the 15 most productive and highly cited institutions in AI and blockchain research for securing smart grids, along with a visualization of the co-authorship network among key organizations.
• Global research trends: An analysis that highlights the leading countries in AI and blockchain research for smart grids, along with international collaboration patterns and visualization of key co-authorship networks.
• Most cited papers: A study of the top 10 most cited papers on the application of AI and blockchain for securing smart grids.
• Keywords Analysis: A visualization and examination of the most frequently occurring keywords in AI and blockchain studies focused on smart grids.
• Moroccan research overview: An analysis of Morocco’s performance in AI and blockchain research for smart grids, including key metrics (publication count, h-index, and international collaboration rate), institutional contributions, and its ranking at the African, Arab, and international levels.

The third stage of our study applies network analysis to explore relationships among authors, institutions, and research topics. This involves a factorial approach based on correspondence analysis (CA), a statistical method that transforms categorical data into a low-dimensional space, revealing topic associations and clustering patterns.

To enhance our insights, we use the following visual tools:

• Thematic Map: Categorize research themes based on centrality (importance) and density (development stage), helping identify emerging and well-established topics.
• Conceptual Structure Map: Display topic relationships, showing how research themes are connected.
• Topic Dendrogram: Provide hierarchical clustering of topics, highlighting their similarities and proximity.

4. Results

4.1. Overview

4.1.1. Publication Trends

Figure 2 illustrates the evolution of research relating to the use of AI and blockchain for smart grid security since the first paper’s appearance in 1991. The number of publications was growing at a slow and consistent rate between the period of 1991 and 2013, representing only 7.67% of all publications (798 documents). The yearly publication volume had an important increase starting from the year 2014, representing 92.33% of all publications (9611 documents). Therefore, we chose to focus on this period between 2014 and 2024 as our timeframe for the bibliometric study.

As shown in Figure 3, between 2014 and 2024, the publication count was at a constant and steady growth, with the most productive year being 2023, with 1725 documents. This corpus of documents accumulated 162,283 citations, with the annual citation count growing at a steady rate, reaching its peak during 2019 at 27,244 citations and continuing to decrease at a constant rate. The yearly average citations per article remains consistent between 35 to 23 citations up until 2020, when it starts to decrease to as low as 1.83 citations in 2023, most likely due to the low number of citations in accordance with the amount of publications (2884 publications and 1577 citations).

A comprehensive summary of research publications and citation metrics spanning the period from 2014 to 2024 is provided in

Table 3. Descriptive statistics of the retrieved publications (2014–2024).

Metric	Value
Publication Years	2014–2024
Total Publications	9611
Citable Year	11
Number of Contributing Authors	39,482
Number of Cited Papers	7466
Total Citations	162,283
Citation per Paper	16.89
Citation per Cited Paper	21.74
Citation per Year	16,228.30
Citation per Author	4.11
Author per Paper	4.11
Citation Sum within h-Core	42,411
h-index	124
g-index	124
m-index	11.273

. The data encapsulates the collective scholarly output and impact within the field of smart grid security.

4.1.2. Publication Types

Figure 4 presents a chart illustrating the distribution of publication types. Conference papers account for 46.8% of all publications, followed closely by journal articles at 45.2% and others (books, reviews, book chapters).

4.1.3. Subject Areas

The dataset covers 23 subject areas on Scopus, as shown by Figure 5. The most dominant subject is “Engineering” with 6570 publications (66.77% of all publications), with the second most dominant subject being “Computer Science” with 5643 publications (57.35% of all publications), followed by “Energy” with 3953 publications (40.17% of all publications). Publications in Scopus may be indexed under multiple subject areas, which is why percentages can exceed 100%. This multidimensional classification helps capture the interdisciplinary nature of smart grid research.

Within the dataset, 88.7% of all publications are concentrated in the top six subject areas, prompting an analysis of their yearly evolution, as depicted in Figure 6. The publication volume in each of these top six subject areas has exhibited consistent and rapid growth since 2014. Throughout the majority of this period, ’Engineering’ has remained the dominant field, with the exception of 2017, when it was surpassed by ’Computer Science’. ’Computer Science’ has generally occupied the second position, followed by ’Energy’ and ’Mathematics’. ’Decision Sciences’ and ’Physics and Astronomy’ have exhibited similar trends, with their positions fluctuating throughout the period.

4.2. Science Mapping

4.2.1. Most Productive and Highly Cited Authors

Table 4. Analysis of the most relevant authors in AI and blockchain research for securing smart grids (2014–2024).

Rank	Most Productive Authors		Highly Cited Authors		Authors with Highest h-Index
	Author	Count	Author	Citations	Author	h-Index
1	Javaid, Nadeem	47	Zhang, Yan	2565	Javaid, Nadeem	21
2	Tanwar, Sudeep	28	Kumar, Neeraj	2402	Kumar, Neeraj	17
3	Mahmoud, Mohamed	27	Yu, Rong	1976	Tanwar, Sudeep	15
4	Sun, Hongbin	27	Kang, Jiawen	1971	Ismail, Muhammed	14
5	Kumar, Neeraj	25	Tanwar, Sudeep	1817	Kumari, Aparna	14
6	Fouda, Mostafa M.	24	Choo, Kim-Kwang Raymond	1595	Mahmoud, Mohamed	14
7	Ismail, Muhammed	24	Javaid, Nadeem	1526	Alasmary, Waleed	13
8	Dong, Zhao Yang	22	Maharajan, Sabita	1409	Gooi, Hoay Beng	13
9	Guerrero, Josep M.	21	Dong, Zhao Yang	1368	Ibrahem, Mohamed I.	13
10	Ibrahem, Mohamed I.	21	Yu, Wei	1288	Dragicevic, Tomislav	12

presents the top 10 authors in the research field, which provides an overview of their research output, impact, and scholarly contributions. Among these authors, Nadeem Javaid, Yan Zhang, and Neeraj Kumar emerge as the most prolific and influential figures in the field. Yan Zhang ranks as the most cited author, with a total of 2565 citations. Nadeem Javaid is the most productive author, with 47 publications, and also boasts the highest h-index (21) among the rest. Neeraj Kumar, with 2402 citations and an h-index of 17, ranks second in citation impact with 25 publications.

4.2.2. Leading Journals

Figure 7 illustrates the distribution of source types within the dataset. Journals represent the predominant source type, accounting for 50.2% of all sources, followed by conference proceedings at 40.2%, and book series, which constitute 6.4%. Given the substantial scholarly impact of journals in the research field, the analysis will primarily focus on this source type. Specifically, attention will be given to evaluating journals based on their SJR and associated performance metrics.

As shown in Figure 8 and

Table 5. Distribution of journal publications per SJR quartile (2014–2024).

Quartile	Total Publications
Q1 (Top 25%)	2442 (63%)
Q2 (Top 26% to 50%)	709 (18%)
Q3 (Top 51% to 75%)	321 (8%)
Q4 (Top 76% to 100%)	406 (10%)

, 63% of the total publications are indexed in Q1 journals, 18% in Q2 journals, 10% in Q4 journals, and 8% in Q3 journals. This distribution underscores the growing prominence of high-quality research, with the increasing proportion of publications in Q1-indexed journals relative to the other quartiles.

Table 6. Analysis of the top 10 journals in AI and blockchain research for securing smart grids (2014–2024).

Journal	${\mathrm{N}}^{\circ }$ of Publications	Total Citations	CiteScore (2022)	SJR (2022)	SNIP (2022)	Quartile
IEEE Access	339	12,988	9.0	0.926	1.422	Q1
Energies	275	6745	5.5	0.632	1.025	Q2
IEEE Transactions on Smart Grid	180	11,770	23.6	5.118	3.197	Q1
International Journal of Electrical Power and Energy Systems	115	3424	10.8	1.533	1.598	Q1
Applied Energy	111	5088	6.6	1.099	1.373	Q1
Electric Power Systems Research	106	2324	21.1	2.907	2.758	Q1
IEEE Transactions on Industrial Informatics	98	7868	22.4	4.002	3.394	Q1
IEEE Transactions on Power Systems	80	3322	7.0	0.975	1.286	Q1
IEEE Internet of Things Journal	80	5498	17.4	3.747	2.844	Q1
Energy Reports	74	1304	5.6	0.973	1.542	Q2

presents a comprehensive analysis of the top 10 most productive journals within the field. The journal IEEE Access leads in terms of productivity with 339 publications, followed by Energies with 275 publications, and IEEE Transactions on Smart Grid with 180 publications. In terms of journal performance, IEEE Transactions on Smart Grid stands out as the highest-performing journal, with the highest CiteScore (23.6) and SJR score (5.118) among the top 10. It ranks second to IEEE Transactions on Industrial Informatics in SNIP score, with a value of 3.197 compared to 3.394, respectively. While most of the journals in the list exhibit relatively high performance metrics, exceptions are found in some Q2 journals and a few less productive Q1 journals.

4.2.3. Prominent Institutions

The institution analysis highlighted the most productive institutions as well as their key citation metrics, such as the total citation count, the h-index, and the g-index, as shown in

Table 7. Analysis of the top 15 institutions in AI and blockchain research for securing smart grids (2014–2024).

Institution	Country	Total Publications	Total Citations	h-Index	g-Index
North China Electric Power University	China	161	3144	23	42
Tsinghua University	China	131	3204	17	40
Zhejiang University	China	120	1755	17	30
China Electric Power Research Institute	China	114	1554	11	27
Nanyang Technological University	Singapore	98	3523	23	51
Southeast University	Bangladesh	88	1128	13	25
Aalborg University	Denmark	83	3439	22	43
National Institute Of Technology	India	82	934	9	17
Shanghai Jiao Tong University	China	74	1333	11	28
Xi’an Jiaotong University	China	71	2714	15	41
COMSATS University Islamabad	Pakistan	70	1939	22	43
Vellore Institute Of Technology	Indonesia	69	1347	18	36
Electric Power Research Institute	United States	67	645	10	24
Islamic Azad University	Iran	67	1853	20	36
University Of Electronic Science And Technology Of China	China	66	1534	15	32

. The most productive institution is North China Electric Power University (China), with 161 total publications, followed by Tsinghua University (China) with 131 publications, and Zhejiang University (China) with 120 publications. Chinese institutions dominate in terms of publication productivity compared to other institutions. Regarding citation count, Nanyang Technological University (Singapore) is first with 3523 citations, then Aalborg University (Denmark) with 3439 citations, and Tsinghua University (China) with 3204 citations. The high citation numbers for Nanyang Technological University and Aalborg University reflect the quality of their research, as their publication volumes are almost half of the top three highest-producing institutions.

To further explore the nature of international collaborations among institutions, a co-authorship network was created, as illustrated in Figure 9.

Analyzing co-authorship and institutional collaboration networks provides critical insights into the structure and dynamics of scientific research communities. In emerging interdisciplinary fields like AI and blockchain for smart grid security, collaboration patterns reflect knowledge exchange, innovation, and potential, which in turn drive research quality and impact. For countries and institutions striving to enhance their influence, understanding these patterns helps identify strategic partnerships and gaps. Therefore, this network analysis is instrumental in assessing the development and diffusion of AI and blockchain technologies within the global smart grid research ecosystem.

The network was generated using VOSviewer, employing a specified thesaurus to eliminate anomalies and duplicate affiliations. A minimum publication count of five papers was set, resulting in 43 institutions being included and grouped into 11 clusters.

In the network, the node size reflects the publication volume for each institution, while the connections represent collaboration links. The analysis confirms that North China Electric Power University is not only the most productive institution but also the most collaborative, with 12 distinct partnerships.

4.2.4. Global Research Trends

Table 8. Analysis of the most relevant countries in AI and blockchain research for securing smart grids (2014–2024).

Most Productive Countries			Most Cited Countries			Countries with Highest h-Index
Country	Count	Rank	Country	Count	Rank	Country	h-Index	Rank
China	3202	1	China	52,463	1	China	75	1
India	1786	2	United States	39,671	2	United States	58	2
United States	1546	3	India	21,493	3	India	51	3
United Kingdom	386	4	Australia	13,662	4	Australia	49	4
Canada	380	5	Canada	12,210	5	Saudi Arabia	40	5
Saudi Arabia	339	6	United Kingdom	11,329	6	Pakistan	39	6
Australia	326	7	Pakistan	7017	7	United Kingdom	35	7
Iran	306	8	Saudi Arabia	6901	8	South Korea	33	8
Pakistan	251	9	South Korea	6382	9	Canada	31	9
South Korea	200	10	Iran	5964	10	Iran	30	10
Italy	190	11	Singapore	5333	11	Singapore	29	11
Germany	169	12	Denmark	4016	12	Denmark	27	12
Singapore	167	13	Italy	3812	13	Turkey	25	13
Turkey	166	14	Norway	3634	14	Qatar	24	14
Brazil	153	15	Turkey	3590	15	Hong Kong	23	15
Case of Morocco
Morocco	65	33	Morocco	546	47	Morocco	12	37

and Figure 10 present an assessment of the leading countries in terms of publication volume and influence within the field of research. China leads in production volume, contributing 33.31% of all publications with a total of 3202 papers. It is also the most influential country, achieving the highest citation count (52,463) and an h-index of 75. India ranks as the second most productive country with 1786 publications and is third in terms of citation count (21,493) and h-index (51). The United States follows as the third most productive country with 1546 publications, but ranks second in citation count (39,671).

Regarding international collaborations,

Table 9. Degree of international collaboration of the most productive countries in AI and blockchain research to secure smart grids (2014–2024).

Country	Total Publications	International Collaboration (%)
China	3202	19.45
India	1786	18.71
United States	1546	37.54
United Kingdom	386	53.22
Canada	380	65.55
Saudi Arabia	339	61.43
Australia	326	79.69
Iran	306	50.39
Pakistan	251	78.28
South Korea	200	37.02
Italy	190	50.28
Germany	169	58.86
Singapore	167	63.19
Turkey	166	34.27
Brazil	153	41.04
Spain	143	56.00
Denmark	137	62.10
France	132	77.50
Egypt	125	66.35
Malaysia	118	64.36
Case of Morocco
Morocco	65	58.46

presents the percentage of international collaborations among the top 20 most productive countries. The data indicates that the most productive countries tend to exhibit lower collaboration rates, with percentages such as 19.45% for China, 18.71% for India, and 37.54% for the United States. In contrast, countries with the highest collaboration rates include Saudi Arabia (79.96%), Pakistan (78.28%), Denmark (77.50%), Egypt (66.35%), and the United Kingdom (65.55%).

A collaboration network between these countries is depicted in Figure 11. In this network, the links represent international collaborations, and each link’s width corresponds to the volume of co-authored documents. The analysis covers the 20 most productive countries and 167 collaboration links among them. Notably, the strongest links are observed between the United States and China (882 publications), Saudi Arabia and Pakistan (290 publications), and Australia and China (287 publications).

4.2.5. Most Cited Papers

Table 10 reveals the top 10 most cited papers on the use of AI and blockchain for securing smart grids. We focus on technical approaches that utilize blockchain, AI, or both technologies to emphasize the nature of implementations within the field, highlighting their key similarities and differences. The majority of the top-cited papers center on security, privacy, and efficiency improvements in decentralized energy trading and smart grid management.

Aitzhan et al. [32] is the most cited paper, published in IEEE Transactions on Dependable and Secure Computing in 2016, totaling 1017 citations (145.29/year). The study addresses privacy and security in decentralized trading using multi-signatures and anonymous messaging. However, it does not tackle scalability when integrating with existing grid infrastructures, highlighting a need for lightweight consensus mechanisms and interoperability protocols to support real-world deployment.

Kang et al. [11], published in IEEE Transactions on Industrial Informatics in 2017, achieved 1013 citations (126.63/year). The authors demonstrate a consortium blockchain for peer-to-peer energy trading among electric vehicles, improving transaction transparency and trust. Yet, the proposed consensus and communication overheads pose challenges for real-time applications, indicating a gap in designing low-latency, resource-efficient blockchain frameworks for mobile grid nodes.

Table 10. Top 10 most cited papers on the use of AI and blockchain for securing smart grids (2014–2024).

Rank	Authors	Title	Journal	Year
1	Aitzhan N.Z.; Svetinovic D.	Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, blockchain and Anonymous Messaging Streams [32]	IEEE Transactions on Dependable and Secure Computing	2016
2	Kang J.; Yu R.; Huang X.; Maharjan S.; Zhang Y.; Hossain E.	Enabling Localized Peer-to-Peer Electricity Trading among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains [11]	IEEE Transactions on Industrial Informatics	2017
3	Li Z.; Kang J.; Yu R.; Ye D.; Deng Q.; Zhang Y.	Consortium blockchain for secure energy trading in industrial internet of things [33]	IEEE Transactions on Industrial Informatics	2017
4	Qing X.; Niu Y.	Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM [34]	Energy	2018
5	He Y.; Mendis G.J.; Wei J.	Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism [35]	IEEE Transactions on Smart Grid	2017
6	Zheng Z.; Yang Y.; Niu X.; Dai H.-N.; Zhou Y.	Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure smart grids [36]	IEEE Transactions on Industrial Informatics	2017
7	Gai K.; Wu Y.; Zhu L.; Qiu M.; Shen M.	Privacy-preserving energy trading using consortium blockchain in smart grid [37]	IEEE Transactions on Industrial Informatics	2019
8	Esmalifalak M.; Liu L.; Nguyen N.; Zheng R.; Han Z.	Detecting stealthy false data injection using machine learning in smart grid [38]	IEEE Systems Journal	2014
9	Morstyn T.; Teytelboym A.; McCulloch M.D.	Bilateral contract networks for peer-to-peer energy trading [39]	IEEE Transactions on Smart Grid	2017
10	Gai K.; Wu Y.; Zhu L.; Xu L.; Zhang Y.	Permissioned blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks [40]	IEEE Internet of Things Journal	2019

Table 10. Top 10 most cited papers on the use of AI and blockchain for securing smart grids (2014–2024).

Rank	Authors	Title	Journal	Year
1	Aitzhan N.Z.; Svetinovic D.	Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, blockchain and Anonymous Messaging Streams [32]	IEEE Transactions on Dependable and Secure Computing	2016
2	Kang J.; Yu R.; Huang X.; Maharjan S.; Zhang Y.; Hossain E.	Enabling Localized Peer-to-Peer Electricity Trading among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains [11]	IEEE Transactions on Industrial Informatics	2017
3	Li Z.; Kang J.; Yu R.; Ye D.; Deng Q.; Zhang Y.	Consortium blockchain for secure energy trading in industrial internet of things [33]	IEEE Transactions on Industrial Informatics	2017
4	Qing X.; Niu Y.	Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM [34]	Energy	2018
5	He Y.; Mendis G.J.; Wei J.	Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism [35]	IEEE Transactions on Smart Grid	2017
6	Zheng Z.; Yang Y.; Niu X.; Dai H.-N.; Zhou Y.	Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure smart grids [36]	IEEE Transactions on Industrial Informatics	2017
7	Gai K.; Wu Y.; Zhu L.; Qiu M.; Shen M.	Privacy-preserving energy trading using consortium blockchain in smart grid [37]	IEEE Transactions on Industrial Informatics	2019
8	Esmalifalak M.; Liu L.; Nguyen N.; Zheng R.; Han Z.	Detecting stealthy false data injection using machine learning in smart grid [38]	IEEE Systems Journal	2014
9	Morstyn T.; Teytelboym A.; McCulloch M.D.	Bilateral contract networks for peer-to-peer energy trading [39]	IEEE Transactions on Smart Grid	2017
10	Gai K.; Wu Y.; Zhu L.; Xu L.; Zhang Y.	Permissioned blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks [40]	IEEE Internet of Things Journal	2019

Li et al. [33], also in IEEE Transactions on Industrial Informatics (2017), garnered 958 citations (136.86/year). Their consortium blockchain model secures IoT-based energy trading in industrial settings, emphasizing data integrity and access control. Despite these advances, the work leaves the interoperability with heterogeneous IoT devices underexplored, suggesting future research should focus on adaptive middleware and standardization to bridge protocol disparities.

IEEE Transactions on Industrial Informatics is the journal with the highest number of cited papers in our analysis, hosting four of the top 10 most cited papers, indicating its high citation impact, as shown in Table 6.

4.2.6. Keywords Analysis

For our keyword analysis, we generated a word cloud based on the data obtained from the papers in the dataset. This allowed us to highlight the most frequently used terms and identify key themes and disciplines pertaining to applications of AI and blockchain in securing smart grids. The top 160 keywords are visually represented in Figure 12. Among the most prominent keywords are “Smart Grid”, “Renewable Energy Resources”, “Power Quality”, “Microgrid”, “Electric Power System Control”, “Smart Power Grids”, “Electric Power System Protection,” “Energy Management”, and “Electric Power Transmission Networks”, among others.

4.2.7. Moroccan Research Overview

Morocco’s strategic location in Northwest Africa, granting access to both the Mediterranean Sea and the Atlantic Ocean, combined with its diverse climatic conditions and growing energy demands [41], positions it as an ideal candidate for the development and integration of smart grid technologies. These advanced grids, augmented by AI and blockchain, offer transformative potential for optimizing and securing energy distribution. Morocco’s commitment to renewable energy is exemplified by its significant investments in solar and wind capacity [42]. Consequently, integrating smart grids into its national energy infrastructure presents an opportunity to enhance energy efficiency, ensure sustainable development, and meet the country’s evolving energy needs.

Our investigation of Moroccan research in the application of AI and blockchain to enhance smart grid security, as summarized in

Table 11. Descriptive statistics of Moroccan research on the use of AI and blockchain to secure smart grids (2016–2024).

Metric	Value
Publication Years	2016–2024
Total Publications	65
Citable Year	9
Number of Contributing Authors	293
Number of Cited Papers	48
Total Citations	551
Citation per Paper	8.48
Citation per Cited Paper	11.48
Citation per Year	68.88
Citation per Author	1.88
Author per Paper	4.51
Citation Sum within h-Core	510
h-index	14
g-index	21
m-index	1.556

and

Table 12. Distribution of Moroccan research on the use of AI and blockchain to secure smart grids per SJR quartile (2016–2024).

Quartile	Total Publications
Q1 (Top 25%)	19 (29.23%)
Q2 (Top 26% to 50%)	7 (10.77%)
Q3 (Top 51% to 75%)	8 (12.31%)
Q4 (Top 76% to 100%)	11 (16.92%)

, reveals that this field is still emerging. The first paper was published in 2016 [43], with a total of 65 publications to date, 66.15% of which were published in the last three years. The corpus has received 551 citations, achieving an h-index of 14 and a g-index of 21. Notably, 29.23% of these publications are indexed in Q1 sources, while 16.92% are in Q4, reflecting the varying quality of Moroccan research in this domain. A closer examination of the 65 articles reveals that 45% focus on AI-based load forecasting or anomaly detection, 30% address blockchain applications for peer-to-peer energy trading, and 25% explore cybersecurity architectures in microgrid contexts. Major contributions originate from Mohammed V University in Rabat, as highlighted by

Table 13. Institutional analysis of Moroccan research on the use of AI and blockchain for securing smart grids (2016–2024).

Institution	Total Publications	Total Citations	h-Index	g-Index
Mohammed V University in Rabat	5	70	3	5
International University of Rabat	2	56	2	2
University Hassan II of Casablanca	2	6	1	2
Mohammed VI Polytechnic University	2	24	1	2
Sidi Mohammed Ben Abdellah University	2	15	1	2
Hassania School of Public Works	1	0	0	0
Hassan First University of Settat (UH1)	1	24	1	1
Moroccan Agency for Sustainable Energy	1	10	1	1
National Higher School for Computer Science and Systems Analysis	1	2	1	1
Private University of Fez (UPF)	1	0	0	0
Al Akhawayn University in Ifrane	1	1	1	1

.

In terms of global rankings, Morocco ranks 36th with 65 publications, as detailed in Section 4.2.4. Within Africa, Morocco is 3rd, following South Africa (83 publications) and Algeria (69 publications). At the Arab regional level, Morocco ranks 7th, trailing Algeria and Iraq (72 publications).

Morocco’s international collaborations are highlighted in Figure 13, which reveals that Morocco’s primary collaboration partners include France, India, the United Kingdom, and the United States. Moreover, Morocco’s degree of international collaboration stands at 58.46%, which showcases its high collaborative nature and interest in the subject of research.

Table 13 highlights the most prominent Moroccan institutions in this area of research. The most productive institution is Mohammed V University in Rabat, with five publications and a citation count of 70. It is followed by the International University of Rabat, which has two publications and 56 citations.

4.3. Network Analysis

4.3.1. Thematic Map

The thematic map highlighted in Figure 14 organizes research topics in AI and blockchain for smart grids into four categories based on their relevance (centrality) and development (density).

Figure 14 depicts four distinct clusters of research topics. In the upper-right quadrant, often called the motor themes, highly relevant and well-developed areas emerge. Here, topics such as “electric power transmission networks”, “deep learning”, and “learning systems” stand out, indicating a mature intersection of AI methods and power grid optimization. In contrast, the upper-left quadrant comprises niche themes, characterized by strong internal cohesion but lower integration with the broader research landscape. Terms like “electric load flow”, “power control”, and “distributed power generation” suggest specialized areas in power engineering and system modeling that have not fully converged with more mainstream AI or blockchain approaches. The lower-left quadrant includes emerging or declining themes such as “smart power grids” and “network security”. While these might be foundational or previously established concepts, their current lower centrality implies either a transition to more specialized research or the need for renewed focus and innovation. Finally, the lower-right quadrant displays basic themes with high relevance but less development, including “electric power system control” and “microgrid”. These topics lie at the core of smart grid research yet offer extensive room for deeper inquiry, especially regarding blockchain and AI integration. Near the center of the map, concepts like “blockchain”, “automation”, and the “internet of things” act as bridges between multiple clusters. Their position indicates moderate maturity and significant relevance, suggesting that targeted research in these areas can further unify the field.

4.3.2. Conceptual Structure

Figure 15 depicts the conceptual structure map derived from a Correspondence Analysis of the most frequent author keywords in our dataset. The two axes (Dim 1 and Dim 2) explain a substantial portion of the variance (4.2% and 4.15%, respectively). Overall, the map reveals several distinct keyword clusters, each representing a specific thematic focus. In the bottom-left quadrant (shown in red), a large cluster emerges around renewable energy, cybersecurity, blockchain, and smart grid-oriented terms. The proximity of these keywords suggests a strong research interest in integrating distributed energy resources and secure data architectures (e.g., blockchain) to enhance smart grid operations. Toward the center quadrant (in green), keywords like distributed network and fault detection appear, indicating a specialized focus on power flow management and the deployment of decentralized energy assets. In contrast, the orange clusters (top-center and right) center on advanced computational approaches, featuring terms such as artificial neural network and fault diagnosis. Their positioning suggests the application of AI-driven methods for predictive maintenance, anomaly detection, and early fault identification in smart grid components. Taken together, these clusters illustrate the diverse nature of research on AI and blockchain for securing smart grids. On one side, there is a strong interest in decentralized generation and secure architectures; on the other, advanced data-driven methods and diagnostic tools address emerging cybersecurity and reliability challenges.

4.3.3. Topic Dendrogram

Figure 16 illustrates the hierarchical clustering of research topics, revealing structural relationships and thematic proximities. The dendrogram groups topics based on their co-occurrence patterns, where shorter branch heights indicate closely related themes, while longer branches suggest broader distinctions between topic clusters.

Several key insights emerge from the analysis:

• Core AI and blockchain Themes: Keywords such as blockchain, deep learning, and machine learning appear in closely connected clusters, indicating a strong focus on leveraging AI techniques for smart grid security and optimization.
• Smart Grid Infrastructure and Security: Another major grouping includes terms like cybersecurity, fault detection, and distributed energy resources, reflecting research efforts toward enhancing grid resilience through anomaly detection and secure data architectures.
• Energy Management and Optimization: The dendrogram also reveals subclusters focusing on energy efficiency, demand response, and power system stability, highlighting studies on optimizing grid performance using AI-driven methodologies.
• Emerging Research Frontiers: More distantly connected terms such as peer-to-peer energy trading and microgrids suggest evolving research areas where blockchain and AI intersect with decentralized energy systems.

5. Discussion

5.1. Global Bibliometric Trends and Research Evolution

The analysis of 9611 publications from 2014 to 2024 reveals a transformative shift in smart grid research, driven by the evolution of AI and blockchain technologies. The exponential growth in publications (92.33% post-2014) aligns with global urgency to address cybersecurity threats and decarbonize energy systems [6]. China’s dominance (33.31% of publications) reflects its strategic prioritization of smart grid modernization, while the high citation impact of institutions in Singapore and Denmark underscores the value of interdisciplinary collaboration and quality-focused research. The predominance of “Engineering” (66.77%) and “Computer Science” (57.35%) in subject areas (Figure 5) highlights the field’s technical focus, though the rising trajectory of “Energy” (40.17%) signals growing emphasis on sustainability. Journals such as IEEE Transactions on Smart Grid and IEEE Transactions on Industrial Informatics dominate citations, serving as hubs for integrating AI-driven analytics with grid resilience frameworks. Keyword clusters like “deep learning” and “microgrid” (Figure 12) further emphasize the convergence of computational intelligence and decentralized energy systems.

5.2. Limitations and Modern Solutions for AI and Blockchain Integration

The bibliometric indicators reveal critical limitations and promising directions in AI and blockchain research for smart grid security. Despite the large volume of AI-related studies, their average citation count remains modest (16.89 citations per article), indicating fragmented maturity and the need for further consolidation in methodologies. High-impact models like LSTM have demonstrated value in load forecasting [9], but their practical deployment is hindered by resource demands—especially in settings where edge computation is essential. This is further complicated by adversarial attack vectors (e.g., False Data Injection) identified in [38], prompting the use of Homomorphic Encryption to secure model inputs and outputs [44]. Recent trends show growing interest in lightweight and privacy-preserving alternatives, such as tinyML for ultra-low-power devices [45] and Federated Learning frameworks [46], which align with the needs of distributed and constrained grid environments.

On the blockchain side, analysis shows a decline in citation momentum for early Proof-of-Work–based systems, paralleling the emergence of more energy-efficient consensus protocols. Notably, high-SJR journals now favor studies proposing hybrid DAG–Proof-of-Stake variants that reduce energy use by up to 60% [47], and sharding techniques that parallelize transactions in microgrids [40]. While citation impact remains high (with a g-index of 124), thematic clustering indicates these contributions are still confined to a few subdomains. Innovations such as zero-knowledge proofs for privacy-preserving energy trading [47] are gradually making blockchain more adaptable to smart grid contexts.

5.3. Integration of AI and Blockchain in Smart Grids

The convergence of AI and blockchain technologies in smart grids, though currently limited, exhibits early signs of strategic importance. Our bibliometric analysis reveals the co-occurrence of keywords such as “reinforcement learning” and “smart contracts”, indicating nascent integration efforts. For instance, some studies utilize blockchain audit trails to train fraud-detection models [36], while others apply adaptive AI to tune consensus parameters under variable load conditions [17]. Despite these developments, co-authorship networks and sparse thematic quadrants suggest a need for stronger multidisciplinary collaboration and interoperable architectures. The limited intersection of AI and blockchain research in smart grids underscores the potential for future studies to bridge legacy grid constraints and enable reliable AI–blockchain synergy. By fostering interdisciplinary research and developing integrated frameworks, smart grids can better leverage the complementary strengths of AI and blockchain to enhance grid security, efficiency, and resilience.

5.4. Morocco’s Research Landscape and Strategic Opportunities

Morocco’s research trajectory mirrors global trends but with unique regional challenges. Ranking 3rd in Africa and 36th globally, its 65 publications since 2016—66.15% in the last three years—demonstrate accelerating engagement. When compared to neighboring North African countries, Morocco’s 65 publications and h-index 12 (Table 8) trail Egypt (125 papers, h-index 29) and is ahead of Tunisia (30 papers, h-index 10). Tunisian researchers often collaborate with European partners, whereas Moroccan collaborators predominantly partner with French and Gulf institutions. Unlike Algeria, whose energy reforms post-2018 spurred higher outputs (72 papers, h-index 15), Morocco’s research steadily increased post-2016 due to proactive government policies.

The focus on AI-driven microgrid optimization and blockchain-based peer-to-peer trading [43] aligns with national goals under the Digital Morocco 2030 Strategy [48]. This strategic vision prioritizes smart grid modernization and renewable energy integration to enhance national sustainability and resilience. However, reliance on international collaborations (58.46%) for advanced AI/blockchain expertise (Figure 13) reveals gaps in local capacity. For instance, while partnerships with French institutions have advanced federated learning applications [22], few studies address Morocco’s rural grid challenges, such as intermittent connectivity and low-density energy demand.

To address these gaps, Moroccan research could pioneer edge AI solutions tailored for offline rural microgrids, combined with lightweight blockchain forks (e.g., IOTA Tangle [49]) for low-bandwidth transaction logging. Additionally, integrating digital twins [50] could simulate cyberattacks on Morocco’s hybrid grids, providing actionable insights for hardening infrastructure. Prioritizing such context-specific innovations will bolster Morocco’s role as a regional leader while addressing disparities in grid access and resilience.

6. Conclusions and Future Work

In conclusion, this paper presented a comprehensive bibliometric analysis on the use of AI and blockchain technologies to secure smart grids, covering 9611 documents published between 2014 and 2024, and accumulating a total of 162,283 citations. By examining data retrieved from the Scopus database, we evaluated global research efforts related to smart grid security, highlighting diverse implementations designed to enhance the resilience of smart grids. The leading contributors in this field were China (3202 publications), India (1786 publications), and the United States (1546 publications). Despite their notable production levels, these countries show limited collaboration rates below 50%, indicating potential for future international partnerships. Among the most influential publication sources are IEEE Transactions on Smart Grid and IEEE Access, which stand out for their significant citation metrics.

Our findings emphasize the growing significance of AI and blockchain in addressing the escalating threats faced by modern energy infrastructures. The evolution of research in this domain reveals a shift from fundamental threat detection algorithms toward innovative solutions such as distributed networks, peer-to-peer energy trading, and advanced load forecasting. These developments illustrate the potential for future technologies to redefine the operational and security frameworks of smart grids.

For Morocco, these insights underscore a unique opportunity to align national research efforts with its renewable energy ambitions. By adopting advanced AI and blockchain technologies, Morocco can enhance its smart grid infrastructure to meet increasing energy demands while addressing specific cybersecurity challenges. Collaborative efforts between Moroccan researchers and global institutions can provide innovative solutions that not only benefit the nation but also contribute significantly to the global advancement of secure and sustainable energy systems.

Looking ahead, future research should not only prioritize the merging of AI and blockchain technologies to address evolving cyberthreats and vulnerabilities in distributed energy systems but also explore several promising emerging approaches. For example, integrating advanced Graph Neural Networks (GNNs) with transformer-based architectures could further enhance anomaly detection accuracy and robustness. In addition, developing blockchain-based federated learning frameworks that incorporate privacy-preserving techniques such as swarm learning can enable decentralized model training with improved convergence rates and efficiency.

This bibliometric analysis provides a valuable foundation for further studies and collaborative initiatives aimed at securing smart grid systems and critical energy infrastructure as a whole through technological innovation.