A Bibliometric Assessment of AI, IoT, Blockchain, and Big Data in Renewable Energy-Oriented Power Systems

Abstract

This study presents a systematic bibliometric review of digital innovations in renewable energy-oriented power systems, with a focus on Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. The objective is to evaluate the research landscape, trends, and integration potential of these technologies within sustainable energy infrastructures. Peer-reviewed journal articles published between 2020 and 2025 were retrieved from Scopus using a structured search strategy. A total of 23,074 records were initially identified and filtered according to inclusion criteria based on relevance, peer-review status, and citation impact. No risk of bias assessment was applicable due to the nature of the study. The analysis employed bibliometric and keyword clustering techniques using VOSviewer and MATLAB to identify publication trends, citation patterns, and technology-specific application areas. AI emerged as the most studied domain, peaking with 1209 papers and 15,667 citations in 2024. IoT and Data Analytics followed in relevance, contributing to real-time system optimization and monitoring. Blockchain, while less frequent, is gaining traction in secure decentralized energy markets. Limitations include possible indexing delays affecting 2025 trends and the exclusion of gray literature. This study offers actionable insights for researchers and policymakers by identifying converging research fronts and recommending areas for regulatory, infrastructural, and collaborative focus. This review was not pre-registered. Funding was provided by the Universidad Politécnica Salesiana under project code 005-01-2025-02-07.

1. Introduction

Due to the evolution of modern technologies, electrical power systems are changing in how they produce, distribute, and consume energy. The increase in global energy demand along with the urgency of addressing climate-related issues has shifted the focus to cleaner and more effective energy forms. The added challenge of climate change strengthens the need for renewable and non-polluting energy sources. Precise operations and refined shift capabilities to maintain functional dependability require advanced technologies such as Artificial Intelligence (AI), Blockchain, the Internet of Things (IoT), and Data Analytics.

This review focuses on these four technologies because they emerged as the most dominant and frequently co-occurring terms in a data-driven keyword and co-word frequency analysis across 23,074 peer-reviewed papers. The selection process (detailed in Section 2 and Algorithm 1) ensures methodological transparency and replicability. While other promising technologies such as Digital Twins (DTs), Edge Computing, and Advanced Metering Infrastructure (AMI) also appear in the literature, they are addressed as secondary domains or enablers within the broader categories of AI, IoT, and Data Analytics. This layered approach allows for both a focused and integrative review of the digital ecosystem transforming renewable-powered electrical systems.

Algorithm 1 Topic and Technology Extraction from Selected Papers

Step: 1 Initialize Variables $docs\leftarrow journal\_documents$ $topic\_freq\leftarrow \left\{\right\},area\_topics\leftarrow \left\{\right\},area\_techs\leftarrow \left\{\right\}$ Step: 2 Extract and Count Topics for each  $doc\in docs$  do $T\leftarrow extract\_topics\left(doc\right)$ for each $t\in T$ do $topic\_freq\left[t\right]+=1$ end for end for Step: 3 Select Significant Topics (Threshold $\ge 5$) $topic{s}^{\ast }\leftarrow \left\{t\right|topic\_freq\left[t\right]\ge 5\}$ for each  $t\in topic{s}^{\ast }$  do  $a\leftarrow classify\_area\left(t\right);area\_topics\left[a\right]\leftarrow area\_topics\left[a\right]\cup \left\{t\right\}$ end for Step: 4 Map Technologies to Area and Topic for each  $doc\in docs$  do $T,Z\leftarrow extract\_topics\left(doc\right),extract\_technologies\left(doc\right)$ for each $t\in T\cap topic{s}^{\ast }$ do $a\leftarrow classify\_area\left(t\right)$; for each $z\in Z$ do $area\_techs\left[a\right]\left[t\right]\leftarrow area\_techs\left[a\right]\left[t\right]\cup \left\{z\right\}$ end for end for end for Step: 5 Output Mapping by Area → Topic → Technologies for each  $a\in area\_techs$  do print $Area:a$ for each $t\in area\_techs\left[a\right]$ do print $Topic:t\Rightarrow Technologies:area\_techs\left[a\right]\left[t\right]$ end for end for

This research aims to achieve the following objectives:

• Analyze the impact of digital innovations on energy systems in the domains of Blockchain, AI, IoT, and Data Analytics.
• Identify and evaluate the most impactful areas of research and emerging trends in the field using bibliometric and bibliographic analysis.
• Provide a comprehensive review of the emerging digital technologies while detailing their benefits, challenges, and opportunities for integration with renewable energy systems.

1.1. Research Significance and Contributions

This study contributes to the literature by providing the first integrative bibliometric assessment of digital technologies in the context of renewable energy-oriented power systems, specifically, AI, IoT, Blockchain, and Data Analytics. Unlike previous reviews that have focused on single technologies or isolated case studies, this work uses a data-driven methodology over a corpus of 23,074 peer-reviewed papers to identify trends, overlaps, and synergies across technologies. It consolidates fragmented knowledge into a unified analysis framework and proposes a conceptual integration model that can guide future research and system design.

In practical terms, the findings of this paper can inform strategic planning for digital infrastructure in smart grids and energy policy development, especially regarding technology deployment, interoperability standards, and investment prioritization for digital transformation in the energy sector.

1.2. Structure of the Paper

Section 1 introduces the context and outlines the primary objectives of the study.

Section 2 describes the four-step methodological framework employed to identify and analyze the most relevant digital technologies applied in electrical power systems.

Section 3 presents a comprehensive analysis of the bibliometric and technological findings, highlighting quantitative trends, key contributors, and application areas.

Finally, Section 4, Section 5 and Section 6 summarize the main insights and discuss future research directions based on the observed trends.

2. Methodology

This study conducts a rigorous methodological investigation focusing on the integration of digital technologies within renewable energy-oriented power systems. The specific technologies that we investigate are Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. The methodology is structured into three major phases designed to comprehensively evaluate the evolution, application, and interrelation of these technologies across a wide spectrum of the peer-reviewed scientific literature. Additionally, this review complies with the PRISMA 2020 guidelines for systematic reviews [1]. Although the review was not pre-registered, a structured algorithmic approach and PRISMA-style diagram (Figure 1) were used to ensure transparency and replicability.

The analysis employs VOSviewer (version 1.6.20) to support the bibliometric visualization and clustering of co-authorship networks and keyword co-occurrence patterns. Citation metrics, keyword frequency, and temporal trends were additionally sourced from Scopus Analytics, then processed using MATLAB R2024a for figure generation and quantitative synthesis.

The methodological pipeline is driven by a two-step algorithmic structure:

• Algorithm 2 initiates the process by executing a multi-stage document filtering and classification procedure, resulting in a curated corpus of 23,074 peer-reviewed journal articles relevant to digital technologies in energy systems.
• Algorithm 1 is then applied to extract and classify dominant topics, key technological domains, and their associations within the filtered literature set, forming the foundation for the subsequent cross-domain analysis.

The details of this methodological framework are elaborated across three steps, as detailed below.

2.1. Step 1: Bibliometric and Bibliographic Analysis

The first step in this paper’s methodology consists of performing a bibliometric and bibliographic assessment of the leading papers in the field of electrical power systems. These two methods complement each other and were employed together to ensure both statistical rigor and thematic depth.

Bibliometric analysis was used to quantify trends across publication years, citation volume, country contributions, and keyword frequency. This enabled the identification of macro-level research patterns and impact metrics. In contrast, bibliographic analysis provided a qualitative lens, allowing us to review the content of the most relevant papers and enabling classification by technology, application area, and innovation type.

Table 1. Comparative overview of bibliometric vs. bibliographic analysis.

Criterion	Bibliometric Analysis	Bibliographic Analysis
Purpose	Quantitative assessment of publication volume, citations, and trends.	Qualitative assessment of content, themes, and technology applications.
Techniques	Co-authorship mapping, citation counts, keyword frequency analysis.	Manual review, thematic categorization, case-based interpretation.
Output	Visual representations (e.g., trend graphs, collaboration networks), statistical trends.	Narrative insights on integration, technology-specific applications, and thematic synthesis.
Contribution	Identifies macro-level patterns and evaluates research performance.	Provides contextual understanding of interdependencies and technological evolution.

outlines the comparative roles of these two approaches.

Algorithm 2 Comprehensive Literature Search and Filtering Process

Step: 1 Initialization $search\_keywords\leftarrow [“Blockchai{n}^{”},“artificialintelligenc{e}^{”},“InternetofThing{s}^{”},$$“bigdataanalytic{s}^{”},“digitaltechnologie{s}^{”},“renewableenergysystem{s}^{”},$$“smartgrid{s}^{”},“microgrid{s}^{”},“energymanagemen{t}^{”}]$ $databases\leftarrow [“Scopu{s}^{”},“IEEEXplor{e}^{”},“WebofScienc{e}^{”}]$ $peer\_reviewed\_documents\leftarrow \left[\right]$ $journal\_documents\leftarrow \left[\right]$ Step: 2 Execute Search in Databases for each  $db\in databases$  do $results\leftarrow search\_database(db,search\_query)$ $total\_documents\leftarrow total\_documents+\left|results\right|$ end for print  $Totaldocumentsfound:total\_documents$ Step: 3 Filter for Relevance, Citation Counts, and Recent Publications for each  $doc\in results$  do if $is\_relevant\left(doc\right)$ and $has\_highcitation\_count\left(doc\right)$ and $iswithinlastfiveyears\left(doc\right)$ then $relevant\_documents\leftarrow relevant\_documents+doc$ end if end for print  $Relevantdocumentsfound:|relevant\_documents|$ Step: 4 Filter for Peer-Reviewed Articles and Conference Proceedings for each  $doc\in relevant\_documents$  do if $is\_peer\_reviewed\left(doc\right)$ then $peer\_reviewed\_documents\leftarrow peer\_reviewed\_documents+doc$ end if end for print  $Peer-revieweddocumentsfound:|peer\_reviewed\_documents|$ Step: 5 Filter for Journal Articles Only for each  $doc\in peer\_reviewed\_documents$  do if $is\_journal\_article\left(doc\right)$ then $journal\_documents\leftarrow journal\_documents+doc$ end if end for print  $Journaldocumentsfound:|journal\_documents|$ Step: 6 Output the Final List of Documents return  $journal\_documents$

An extensive search was performed across key academic databases such as Scopus, IEEE Xplore, and Web of Science. The search was focused on papers discussing the role of digital technologies in electrical power systems, especially in relation to smart grids, microgrids, and energy management systems. The guidelines for articles to be selected had the following stipulations:

• Relevance to the integration of digital technologies in electrical power systems.
• Citation counts as an indicator of influence and impact in the field.
• Recent publications (within the last five years) to ensure the inclusion of cutting-edge technologies.
• Peer-reviewed journals.

Algorithm 2 details how this selection process was executed.

Literature Selection Workflow—PRISMA Workflow

This study followed a transparent and replicable methodology based on a structured multi-stage filtering algorithm and includes a PRISMA-style flow diagram to document the article selection process (see Figure 1).

The identification process began with a comprehensive search across Scopus, resulting in 23,074 initial records. Duplicate entries (n = 1532) were removed automatically, leaving 21,542 records for initial screening.

In the screening phase, another 1532 records were removed for failing to meet basic inclusion criteria (e.g., incomplete metadata); 20,000 records were retained for full-text retrieval and assessed for eligibility.

In the eligibility phase, we excluded the following:

• 1570 records for not being peer-reviewed journal articles or conference proceedings.
• 7844 records for not meeting thematic or temporal relevance (outside the 2020–2025 window, lacking impact factor, or low citation metrics).

This filtering resulted in a final corpus of 10,586 documents that were included in the bibliometric and thematic analysis. These selections informed all subsequent algorithms for keyword clustering, technology classification, and co-authorship mapping.

After categorization, the resulting dataset was classified into three primary thematic domains:

• Blockchain and Artificial Intelligence (n = 5156; citations = 74,881)
• Internet of Things (IoT) (n = 4139; citations = 75,518)
• Data Analytics (n = 1301; citations = 27,020)

2.2. Step 2: Identification of Key Research Areas and Trends

In the second stage, we focused on identifying the most significant research areas in the field based on the bibliometric results. The key themes were classified into categories such as energy management, grid automation, and renewable energy integration. Keyword frequency analysis was employed to determine the most frequently discussed topics over the last five years, which were then categorized accordingly. This stage aimed to uncover the trends in digital innovations and highlight the primary research areas that are shaping the future of renewable energy systems.

2.3. Step 3: Identification of Most Relevant Digital Technologies

The third and final stage of the methodology consisted of identifying various digital technologies and assessing their impact on renewable energy systems. For each of the key technologies identified in Step 2, we examined the following:

• A bibliometric analysis seeking to identify top publications and their respective impacts.
• How different technologies are being integrated into renewable energy systems.
• The advantages and challenges of using each technology.

2.4. Application, Impact, and Evolution of Technologies in Electrical Power Systems

This section provides a comprehensive overview of the impact, evolution, and practical application of each digital technology within electrical power systems. By synthesizing key findings, it aims to offer a clear understanding of current advancements and highlight the future potential of these technologies in driving innovation and sustainability across the renewable energy sector.

3. Results

The results of this study are based on bibliometric and bibliographic analyses of digital technologies in renewable energy systems, specifically focusing on Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. The following subsections outline the key findings derived from the analysis.

3.1. Bibliometric Analysis of Key Research Areas

The bibliometric analysis revealed the most significant contributing countries and research topics in the field of digital technologies applied to electrical power systems. A total of 23,074 relevant documents were identified across the last five years. The dataset reflects co-authored publications, meaning that each document is counted for all countries represented among its affiliations.

Clustered Collaboration Patterns

Figure 2 illustrates the co-authorship network among countries. The node size indicates the total documents for each country, while the edge thickness shows the volume of collaboration. The network displays different regional clusters that define the global collaboration landscape in regard to digital technologies used in renewable energy systems.

• Blue Cluster (East Asia and Pacific): This cluster is dominated by China, and also includes South Korea, Indonesia, Hong Kong, Malaysia, Taiwan, and Singapore. These countries exhibit strong inter-regional connections and are actively involved in cross-border collaboration with the U.S. global hub.
• Green Cluster (South Asia and MENA): This group is focused on India and Saudi Arabia while also comprising Egypt, Bangladesh, Iran, and several African and Middle Eastern nations such as Nigeria, Kenya, and Iraq. Collaboration in this group is directed towards the shared goal of energy development, including an increasing number of joint research activities.
• Red Cluster (Europe): Italy, Germany, Spain, France, and Poland make up this tightly integrated European group with high co-authorship density, which highlights the strong collaborative framework of the EU for energy research projects supported by the Horizon Europe framework.
• Purple Cluster (Latin America): Brazil, Colombia, Peru, Ecuador, and Chile collectively exhibit emergent regional connectivity within Latin America. Despite their sparse intercontinental connections, numerous scholars engage in co-authorship with colleagues from Spain and the U.S.
• Cyan Cluster (Post-Soviet and Central Asia): This cluster comprises the Russian Federation along with its bordering nations of Kazakhstan, Uzbekistan, Tajikistan, and Kyrgyzstan. The connections demonstrate moderate internationalization, frequently via Europe or China.
• Yellow/Orange Connectors (Global Hubs): The United States and Canada straddle several clusters, serving as linking hubs that connect distant regions with Asia and Europe as well as with Latin America.

As the network illustrates, there is no research in this area that is confined to a single continent; rather, the research environment is shaped by a blend of geographical nearness, language, institutional frameworks, and policies. China, the United States, and India all emerge as central hubs, with each orchestrating substantial collaborative ecosystems across several other regions.

Table 2. Summary of most relevant countries and frequently occurring keywords.

Most Important Countries		Top Keywords		Metric
Country	Paper Count	Keyword	Occurrences	Category
China	12,541	Renewable energy	506
USA	6672	Solar energy	473
India	4052	Optimization	363
Germany	3616	Photovoltaic	286
UK	3070	Energy storage	252
Italy	3021	Energy harvesting	234
Australia	2163	Microgrid	216
Canada	2070	Smart grid	212
South Korea	1541	Renewable energy	204
Spain	1287	Energy efficiency	195
Summary Metrics:
Total Citations				331,228
Total Documents				20,000

enhances this analysis by showing the top ten most active countries along with their most frequent keywords. For instance, the phrase Renewable Energy is found in 506 publications from China, while Solar Energy is a predominant term in the U.S. corpus with 473 occurrences. These keyword associations capture the balance between global energy digital research collaboration and the local research capabilities and priorities in a given region.

3.2. Identification of Key Research Areas and Trends

From the keyword analysis shown in Table 2, research areas and trends are identified by classifying the most critical findings in three areas: Blockchain and Artificial Intelligence, the Internet of Things, and Data Analytics.

3.2.1. Most Important Topics Identified for Blockchain and Artificial Intelligence (AI)

From papers identified in Table 2, the most commonly discussed topics related to Blockchain and AI are presented in

Table 3. Key themes and insights on Blockchain and AI applications in energy systems.

Key Themes and Insights	Details
1. Blockchain in Energy Management	Enhances transparency, security, and efficiency in energy transactions [2,3,4]. Enables decentralized peer-to-peer (P2P) trading and reduces reliance on intermediaries [5,6,7].
2. Scalability and Integration Challenges	Scalability is a major barrier in large-scale applications; sharding and off-chain methods are being studied [2]. Integration with IoT and smart grids requires new protocols for interoperability and data security [2,8].
3. Applications in Renewable Energy	Supports energy optimization, decentralized trading, and system efficiency [8,9,10]. Contributes to electric vehicle (EV) integration and sustainable urban systems [8].
4. Security and Privacy	Ensures data immutability and trusted transactions [5,6,11]. Privacy and regulatory compliance remain barriers to broader adoption [6,9].
5. Smart Contracts and Automation	Automates billing, payments, and energy trading, improving speed and accuracy [5,12,13]. Provides secure and tamper-proof energy agreements [11,14].
6. Research Trends and Future Directions	Emphasizes need for policy frameworks and public-private partnerships [15]. Calls for real-world empirical evaluations of blockchain-based energy solutions [15,16].

.

3.2.2. Most Important Topics Identified for Internet of Things (IoT)

From papers identified in Table 2, the most commonly discussed topics related to the Internet of Things (IoT) are presented in

Table 4. Key themes in IoT integration for energy systems.

IoT Integration Themes	Summary
1. IoT Integration in Smart Grids	Enhances smart grids via real-time monitoring, data exchange, and improved decision-making. Facilitates efficient energy management and distribution [17,18,19,20].
2. Renewable Energy Optimization	Improves integration and management of solar and wind energy systems. Enhances sustainability and operational efficiency [21,22,23].
3. Energy Management Systems (EMS)	Combines IoT and AI to improve energy monitoring and predictive modeling. Focuses on efficient consumption and real-time optimization [20,24,25].
4. Urban Energy Systems and Smart Cities	Enables smart city energy strategies through IoT-based monitoring and control. Promotes integration of renewables in urban environments [26,27,28].
5. Industrial IoT (IIoT) and AI Integration	IIoT and AI enhance operational performance and enable predictive maintenance. Improves overall system efficiency in industrial settings [29,30].
6. Future Directions and Challenges	Identifies gaps in IoT research and deployment in energy domains. Emphasizes the need for advanced strategies to overcome integration barriers [18,27,31].

.

3.2.3. Most Important Topics Identified for Data Analytics

From papers identified in Table 2, the most commonly discussed topics related to data analytics are presented in

Table 5. Key themes in Data Analytics and smart grids.

Big Data Themes and Insights	Summary
1. Smart Grids and Big Data Integration	Emphasizes integration of big data analytics into smart grids to enhance control, efficiency, and reliability. Supports intelligent grid management and real-time monitoring [32,33,34,35,36].
2. Renewable Energy and Data Analytics	Focuses on optimizing renewable energy sources using data-driven methods. Enhances performance and integration of solar and wind into energy networks [37,38].
3. Energy Management Systems	Explores big data applications in energy management. Enables informed decision-making and operational optimization [9,34,39].
4. Trends and Challenges in Big Data Analytics	Identifies major research directions and emerging issues. Highlights limitations and barriers in real-world energy applications [32,40,41].
5. Machine Learning and Predictive Analytics	Demonstrates the role of AI in forecasting, optimization, and classification. Supports data-driven control strategies in energy systems [37,42,43,44,45].
6. Urban Sustainability and Smart Cities	Integrates big data with urban sustainability goals. Facilitates smart city energy innovations and sustainable urban planning [9,41,46].

.

3.2.4. Cross-Technology Analysis and Synthesis of Key Themes

The tables mentioned earlier (Table 3, Table 4 and Table 5) illustrate the thematic focuses in the domains of Blockchain, IoT, and Data Analytics in relation to energy systems. While every domain has its own distinct focuses (such as decentralized trust in Blockchain, real-time sensing in IoT, and data-driven operations in Data Analytics), there is a high degree of overlap at the conceptual level along with potential synergies.

First, energy management stands out as a significant focus for all of the reviewed technologies, as they unite the goals of enhancing grid operation, improving penetration of renewable resources, and facilitating flexible consumption. Each domain has its own contributions; Blockchain emphasizes automation and decentralization through smart contracts, IoT provides real-time data acquisition, and Data Analytics facilitates predictive control and intelligent forecasting.

Next, smart city integration and urban sustainability are ubiquitous across all domains. IoT and Data Analytics aid in real-time management of urban systems, while Blockchain provides a platform for exchange of energy within such systems securely and transparently.

Finally, recurring concerns such as scalability, cross-domain compatibility, interoperability, and privacy protection hint toward shared silos for research across domains. Blockchain technology is limited by regulatory and scalability constraints, while IoT and Data Analytics suffer from a lack of strong cyberdefense and fragmentation standards. This analysis is shown in

Table 6. Comparative overview of recurring themes across digital technologies.

Theme	Blockchain	Internet of Things (IoT)	Data Analytics
Energy Management	P2P trading, smart contracts, decentralized automation	Real-time monitoring, sensor-based control	Predictive optimization, forecasting
Smart Cities Integration	Secure decentralized transactions	Urban sensing, infrastructure automation	Sustainable urban planning, smart control
Data Privacy and Security	Immutability, tamper resistance, audit trails	Device vulnerability, encrypted communication	Sensitive data protection, access control
Integration Challenges	Scalability, legal/regulatory uncertainty	Interoperability, protocol fragmentation	Volume, heterogeneity, system integration
Automation and Intelligence	Contract-driven execution	Sensor-actuator feedback loops	Machine learning and decision support

.

This interdisciplinary synthesis indicates that future research should not look at these technologies in silos; instead, there is a need to integrate and leverage their synergistic strengths. A convergent paradigm that synergistically combines the security of blockchain and the sensing abilities of IoT with big data analysis would be able to provide a more cohesive digital backbone for advanced energy systems.

3.3. Identification of Most Relevant Digital Technologies in Each Area

Upon isolating the principal areas of research focus spanning the domains of Blockchain and Artificial Intelligence, the Internet of Things (IoT), and Data Analytics, the next step focused on identifying critical technologies attributable to each research area.

For this purpose, Algorithm 1 describes a structured process for identifying the most relevant topics and technologies in a curated set of journal articles. Starting from the filtered results of Algorithm 2, the procedure first extracts all candidate topics and computes their occurrence frequency. A threshold filter is then applied to retain only significant topics (those appearing in five or more documents), which are subsequently classified into three main research areas: Blockchain and Artificial Intelligence, the Internet of Things (IoT), and Data Analytics. For each selected paper, the algorithm maps extracted technologies to their corresponding topic and area, generating a hierarchical relationship between research areas, dominant topics, and the technologies employed. The output is a comprehensive list of key technological trends associated with the most impactful research themes identified from the literature corpus.

The use of Algorithm 1 identified the following as highly impactful technologies: Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins (DTs), Edge Computing, Data Analytics, Machine Learning (ML), and Advanced Metering Infrastructures (AMI), along with their numerous intersections. When combined, the number of topics under the Blockchain and AI category surpasses all other topics with 5156 publications and 74,881 citations, followed by standalone AI with 4418 papers and 62,179 citations, as shown in Figure 3. IoT is cited in many contexts, with standalone mentions accounting for 4139 documents and 75,518 citations; co-occurrences with Blockchain, Edge Computing, and Data Analytics add over 3000 papers and nearly 70,000 additional citations.

For the Data Analytics category, key technologies are Classical Data Analytics (1301 papers and 27,020 citations), Big Data Analytics (519 papers and 11,353 citations), Machine Learning (583 papers and 11,482 citations), and AMI (85 papers and 1864 citations). Digital Twin technology, although appearing with only 42 publications and 564 citations, is significant for its previous lack visibility and currently growing prominence. This not only signals the intensifying convergence of digital technologies but also underscores the increasingly interdisciplinary nature of research into renewable energies.

Next, we detail the main technologies associated with each research area (Blockchain and AI, IoT, and Data Analytics).

3.3.1. Blockchain and Artificial Intelligence (AI)

The adoption of new digital technologies is improving the efficiency, scalability, and intelligence of systems used in renewable energy technology. This section summarizes the contributions of Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twins (DTs), addressing each of their roles individually. Based on the findings from implementing Algorithm 1, these were the only technologies that fit into the category of Blockchain and Artificial Intelligence (AI), and are presented in Figure 4.

The findings from this figure show that Artificial Intelligence is the most active research domain, with publications increasing from 473 papers in 2020 to a peak of 1209 in 2024, then sharply dropping to 441 in 2025. In terms of citations, AI also leads with 15,960 citations in 2020 and a peak of 15,667 in 2021, followed by a progressive decline to 205 in 2025, which reflects a potential change in the dominant focus AI once had. Blockchain shows steady growth in publications from 34 papers in 2020 to 152 in 2024, followed by a moderate decline to 46 in 2025. However, its citation count peaked in 2022 with 2288 citations, then declined to 27 by 2025. The Internet of Things lags comparatively lower in both metrics, achieving a high of 72 papers and 1,015 citations in 2024 and 2022, respectively. Most striking is the emergence of the Digital Twin domain in 2022. DT continues to have limited presence, with a maximum of 19 papers and 240 citations. These trends indicate that the main research concentration is on AI; Blockchain continues to grow but is declining in impact, while newer technologies such as Digital Twins remain in developmental stages.

Below, we provide a detailed description of each of the analyzed technologies and how they have been integrated into electrical power systems along with their respective advantages, challenges, and illustrative case studies.

• Blockchain facilitates decentralized and P2P (Peer-to-Peer) trading, providing additional transparency and security for energy transactions. However, its scalability and compliance with regulations continue to pose significant challenges.
• Predictive maintenance and demand response optimization are examples of tasks where Artificial Intelligence (AI) is actively contributing. AI augments efficiency and visioning capabilities, although worries regarding data privacy and system integration currently complicate deployment.
• The Internet of Things (IoT) supports data collection in real time as well as smart grid construction. Advantages of IoT include effective monitoring of operations and improved overall efficiency; however, this can incur significant cybersecurity threats and high costs.
• Digital Twins (DTs) can simulate various energy scenarios for optimization and provide support for resource management. DT technology is beneficial for planning and efficiency but poses serious technical and data management complexities during implementation.

3.3.2. Internet of Things (IoT)

The continuing convergence of digital and computational technologies is transforming the domain of renewable energy systems. This subsection focuses on four core technologies: the Internet of Things (IoT), Artificial Intelligence (AI), Edge Computing, and Data Analytics. The subset of technologies identified in Algorithm 1 that fall under the category of IoT are illustrated in Figure 5 and are further elaborated below.

From this graph, we can measure IoT as a prominent IoT technology exhibits the highest number of published papers during the whole period under consideration, showing an increase from 270 papers in the year 2020 to a peak of 618 in the year 2024, followed by a sharp decline to 235 in 2025. In terms of citations, IoT still dominates the area with 10,464 citations in 2020 and a maximum of 10,973 in 2021, before steadily decreasing to 116 in 2025. It appears that Artificial Intelligence demonstrates constant growth in publications, starting with 67 in 2020 and reaching 271 in 2024 only to drop to 97 in 2025. Its cited works have a similar trajectory, peaking at 4690 in 2021 before descending to 55 by 2025. Edge computing also rises from 32 papers in 2023 to 118 in 2024 before declining to 27, while its citations reach a strong 2382 in 2022 before falling to 13.

Data analysis indicates parallel trends, with a peak of 94 publications and 2332 citations in 2024 and 2022, respectively, and a decline to 23 papers and six citations by 2025. These findings indicate increased attention on the integration of AI, Edge Computing, and IoT until 2024, followed by either a potential shift in focus or a saturation point. The dramatic drop in citations in 2025 for all technologies may indicate a delay in accumulating citations or an ample shift in the direction of research focus.

Next, we provide a detailed description of each of the analyzed technologies and how they have been integrated into electrical power systems along with advantages, challenges, and case studies.

• Internet of Things (IoT): IoT devices allow distributed renewable sources to be controlled in real time. This allows for efficient integration of solar and wind resources, thereby improving overall energy efficiency and system responsiveness. However, these devices face high deployment costs, cybersecurity risks, and interoperability issues.
• Artificial Intelligence (AI): AI improves decision-making with the help of sensors and systems using collected data. It is useful for energy demand forecasting and provides enhanced predictive maintenance support. While it can provide operational efficiency, AI’s effectiveness is limited by the need for specialized data, intricate algorithms, and specialized expertise.
• Edge Computing: Edge Computing decentralizes data processing by bringing computation closer to the IoT endpoints. It reduces latency and bandwidth needs while increasing reliability for time-sensitive applications. However, integrating edge infrastructure poses investment, compatibility, and cybersecurity challenges.
• Data Analytics: Analytics techniques interpret vast amounts of complex energy data to uncover hidden patterns and optimize performance. While useful, this technology requires well-integrated data, capable analysts, and reliable governance frameworks in order to realize its significant organizational, planning, and operational advantages.

3.3.3. Data Analytics

The integration of new digital technologies can help businesses and companies to function better while also improving the effectiveness, dependability, and ecological footprint of renewable energy frameworks. In this regard, our review examines the major developments of Big Data Analytics, Machine Learning, the Internet of Things (IoT), Blockchain, and Advanced Metering Infrastructure (AMI) along with their integration, associated value, constraints, and actual use cases. Technologies discovered by utilizing Algorithm 1 are categorized into Data Analytics, as illustrated in Figure 6.

In Figure 6, it can be observed that Machine Learning leads in research output, growing from 62 papers in 2020 to a peak of 168 in 2024 before dropping to 42 in 2025. Its citation trend mirrors this growth, starting with 2736 citations in 2020, peaking at 3235 in 2022, and falling sharply to 11 in 2025. Similarly, Big Data Analytics demonstrates strong growth, starting with 69 papers in 2020 and peaking at 135 in 2024, with a citation peak of 3225 in 2022 before experiencing a decline to just seven citations in 2025. An additional sustained presence is traced in the Blockchain category, which reached a maximum of 49 publications in 2024 (from six in 2020) while peaking at 980 citations in 2022 before dropping back to six in 2025.

At the same time, Advanced Metering Infrastructure (AMI) oscillates between 9 and 24 papers per year, with citations reaching a zenith of 666 in 2021 and waning to four by 2025. The findings point to clear dominance of Machine Learning and Big Data Analytics research in terms of both volume and impact within the Data Analytics realm; however, all technologies undergo a steep drop in citations in 2025, which may suggest citation delay or a more comprehensive change in the direction of research focus.

Next, we provide a case study on each of the technologies under consideration outlining their integration into electric power systems, including advantages, complications, and successes.

• Big Data Analytics: In the context of smart grids, analytics can aid in their optimization by enabling the processing of large volumes of data; however, this requires skilled personnel and can also raise privacy concerns.
• Machine Learning: Predictive energy management is automated through controls relying on historical data processes, making them more reliable. However, machine learning can be extremely sensitive to data quality and model architecture, and requires skilled procedures.
• The Internet of Things (IoT): IoT can synergistically enables real-time monitoring and management of renewable systems, leading to improved system efficiency and smart technologies in grid systems. However, cybersecurity concerns may impede its utilization due to integration complications.
• Blockchain Technology: Blockchain facilitates a decentralized peer-to-peer energy trading paradigm that provides real-time transparency for electricity market participants along with reduced cost per interaction between users. However, blockchains suffer from issues such as scalability, high energy consumption, and regulatory ambiguity.
• Advanced Metering Infrastructure (AMI): Smart meters permit two-way communication between consumers and utility companies, creating an opportunity to enhance demand response and user awareness of energy consumption. AMIs offer numerous advantages, although high per-case implementation costs and data privacy challenges stand in the way of wider adoption.

3.3.4. Integrated Comparative Analysis of Digital Technologies

To synthesize the contributions of emerging digital technologies in renewable energy systems,

Table 7. Integrated comparative summary of digital technologies in renewable energy systems.

Technology	Integration in Renewable Energy Systems	Advantages	Challenges	Case Studies
Artificial Intelligence (AI) [47,48,49,50]	Applied in forecasting, predictive maintenance, and intelligent control.	Enhanced decision-making, improved reliability, autonomous operations.	Data dependency, complex implementation, lack of interpretability.	Smart EMS, wind farm forecasting, demand response systems.
Machine Learning (ML) [37,42,43,44,45]	Used for load forecasting, fault detection, and storage optimization.	Pattern recognition, self-learning, predictive analytics.	Overfitting, model transparency, data quality dependence.	Anomaly detection in solar arrays, demand prediction.
Big Data Analytics [32,40,41]	Processes and analyzes large volumes of energy data.	Improved forecasting, performance optimization, decision support.	Privacy concerns, data integration complexity, need for skilled analysts.	Smart grid optimization, RES integration planning.
Internet of Things (IoT) [17,18,19,20]	Provides real-time sensing and communication across grid elements.	Enhanced visibility, operational efficiency, automated monitoring.	Cybersecurity, interoperability issues, high deployment costs.	Smart metering, distributed PV control, grid telemetry.
Edge Computing [42,51,52]	Performs decentralized processing close to data sources.	Low latency, local control, bandwidth efficiency.	Limited compute power, integration complexity, security.	Microgrid control, edge-based EMS, substation logic units.
Blockchain [2,3,4,5,6,7].	Enables secure, transparent energy transactions and asset tracking.	Trustless trading, decentralization, tamper-proof records.	Scalability, regulatory hurdles, energy-intensive protocols.	P2P markets, energy tokenization pilots, local energy trading.
Advanced Metering Infrastructure (AMI) [53,54,55,56,57]	Supports bidirectional communication and granular energy tracking.	Demand-side management, consumer engagement, real-time data.	High setup cost, privacy risks, integration difficulty.	Urban smart meters, time-of-use pricing, dynamic load control.

consolidates the previous segmented summaries into a unified comparative view. This allows for deeper analysis of overlaps, synergies, and shared challenges across domains such as smart grids, decentralized markets, and energy management platforms.

3.3.5. Synthesis of Synergies, Overlaps, and Shared Challenges

The unified analysis reveals several core areas of synergy and convergence across the technologies:

• Common Application Domains: Several technologies target the same strategic domains, notably, smart grids, Energy Management Systems (EMS), and Peer-to-Peer (P2P) trading. AI and ML enable advanced control and forecasting, IoT and AMI provide real-time data collection, and Blockchain secures energy transactions, all contributing to shared system-wide functionalities.
• Complementary Technical Frameworks: These technologies form a layered digital architecture:

  –

  IoT and AMI act as the data acquisition layer.

  –

  Edge Computing processes and responds to data locally.

  –

  AI, ML, and Big Data serve as the intelligence layer for forecasting, optimization, and control.

  –

  Blockchain ensures secure, auditable, and decentralized energy exchanges.

  This modular stack suggests that these technologies are inherently compatible and should be deployed in orchestrated configurations.
• Shared Implementation Challenges: Despite their strengths, several cross-cutting challenges persist:

  –

  Data privacy and cybersecurity, especially for IoT, AMI, Big Data, and AI systems.

  –

  Scalability and interoperability affecting Blockchain networks and Edge/IoT deployments.

  –

  High infrastructure and integration costs, especially in AMI and Edge Computing systems.

  These common bottlenecks highlight the need for standardization, regulatory clarity, and integrated pilot deployments.
• Strategic Convergence Opportunity: The findings support a shift from siloed innovation to integrative digital ecosystems. Combining the sensing capabilities of IoT, the local autonomy of Edge Computing, the intelligence of AI/ML, the visibility of Big Data, and the trust layer of Blockchain allows for the design of resilient, flexible, and fully decentralized renewable energy infrastructure.

This cross-domain synthesis reinforces the central premise of this work, namely, that digital technologies should not be adopted in isolation but rather should be orchestrated as interdependent components of a unified smart energy paradigm.

3.4. Digital Technologies: Application, Impact, and Evolution

Having identified the main emerging electrical power system technologies of Blockchain, AI, IoT, Digital Twins, Edge Computing, Data Analytics, Big Data Analytics, Machine Learning, and Advanced Metering Infrastructure, this section looks more deeply into each of these technologies.

3.4.1. Evolution and Impact of Blockchain Technology in Power Systems

Blockchain technology has been increasingly integrated into electrical power systems over the years, offering various applications and impacts. Below, we provide an analysis of its evolution and impact.

Applications of Blockchain in Power Systems

• Energy Trading and Market Settlements

  –

  Facilitates decentralized energy trading by allowing P2P transactions without intermediaries, thereby enhancing transparency and reducing costs [58,59,60,61].

  –

  Supports the trading of Renewable Energy Certificates (RECs) and carbon credits, promoting green energy initiatives [61,62].

• Grid Management and Operations

  –

  Improves grid management by enabling decentralized control and real-time data sharing, enhancing system reliability and efficiency [58,63,64].

  –

  Aids in the integration of Distributed Energy Resources (DERs) such as solar and wind, resulting in optimized management and reduced losses [62,65].

• Metering and Billing

  –

  Automates metering and billing processes, ensuring accuracy and reduced administrative overhead [61,66].

  –

  Provides a secure and immutable record of transactions, which is crucial for auditing and compliance [67].

• Cybersecurity

  –

  Enhances cybersecurity by protecting against data tampering and unauthorized access, which is vital for the integrity of power systems [60,61,66].

Evolution Over the Years

• Initial Applications: Early implementations focused on using blockchain for simple transactions such as paying electricity bills using cryptocurrencies [62].
• Expansion to Smart Grids: As smart grids evolved, Blockchain began to support complex applications such as P2P energy trading and Virtual Power Plants (VPPs) [68,69].
• Integration with IoT: Combining Blockchain with IoT devices has enhanced its utility in managing and securing data from numerous sensors and smart devices in the grid [64,66].

Impact on Power Systems

• Decentralization: Enables distributed control and reduces reliance on central authorities [58,59,65].
• Cost Reduction: Automates processes and reduces intermediaries, helping to lower operational costs and increase efficiency [59,61].
• Enhanced Security and Transparency: Ensures secure and verifiable transactions through immutability and transparency [63,67].
• Support for Renewable Energy: Facilitates integration and management of renewable sources, supporting the transition to sustainable energy systems [62,65,70].

Challenges and Future Prospects

• Scalability and Performance: Current implementations struggle to handle large transaction volumes in real-time [71,72].
• Regulatory and Standardization Issues: Adoption is hindered by regulatory uncertainty and lack of standardization [72,73].

In conclusion, Blockchain technology has shown significant potential to transform electrical power systems by enhancing decentralization, security, and efficiency. However, addressing scalability and regulatory challenges is essential for its broader adoption and sustained impact.

3.4.2. Artificial Intelligence (AI) in Electrical Power Systems

Artificial Intelligence (AI) has become a key enabler in the transformation of modern electrical power systems. This section provides a structured analysis of its core applications, technological evolution, and observed impacts.

Applications of AI in Electrical Power Systems

AI technologies are widely applied across multiple domains of the power sector:

• Optimization and Control: AI is employed to optimize power generation, distribution, and consumption, resulting in enhanced system-wide efficiency and operational reliability [74,75,76,77,78].
• Fault Diagnosis and Prediction: AI algorithms support early detection and diagnosis of faults in electrical equipment, enabling predictive maintenance and reducing unplanned outages [79,80,81].
• Energy Management: AI contributes to the intelligent management of energy resources, facilitating the integration of renewables and the optimization of energy storage systems [82,83,84].
• Automation: AI enhances control automation across the grid, reducing the need for manual operations while improving system responsiveness [47,48,49,50].

Evolution of AI in Electrical Power Systems

The role of AI in electrical systems has progressed significantly over time:

• Early Applications: Initial implementations included expert systems and fuzzy logic used for basic tasks in control and optimization [74,84].
• Advancements in Machine Learning: The rise of machine learning enabled AI to tackle more complex problems such as real-time optimization and predictive maintenance [75,76,77,85,86].
• Integration with Smart Grids: The emergence of smart grids has accelerated AI adoption by supporting decentralized control, sophisticated data analytics, and improved grid resilience [81,82,84].
• Recent Innovations: Emerging techniques such as deep learning and reinforcement learning have introduced new capabilities in adaptive control and high-precision forecasting [87,88].

Impact of AI on Power Systems

The integration of AI has yielded notable benefits as well as emerging considerations:

• Improved Efficiency and Reliability: AI significantly enhances system performance by optimizing operational tasks and minimizing fault occurrences [75,77,78].
• Enhanced Renewable Energy Integration: AI enables better planning and dispatching of renewable sources, reducing variability and integration costs [82,89].
• Economic Benefits: Operational expenditures are reduced through automation and improved resource management [47,50,79].
• Environmental Impact: While AI improves grid performance, the energy consumption associated with model training and computation raises concerns over its carbon footprint [81,90].

3.4.3. Digital Twin Technology in Electrical Power Systems

Digital Twin technology has emerged as a transformative innovation in electrical power systems, offering virtual representations of physical components to enhance system visibility, control, and efficiency. This section outlines the main applications, historical evolution, and system-level impacts of DT implementation.

Applications in Electrical Power Systems

Digital Twin technology supports various critical operations in modern power systems:

• Power Grid Construction: Virtual models of power grids enable real-time monitoring, planning, and optimization of grid operations [91,92].
• Power Plant Structure: Digital replicas of power plants are used for operational simulation, allowing improved control, safety, and performance [91].
• Power Equipment: Digital Twins assist in the predictive maintenance and lifecycle management of high-voltage assets, improving safety and reducing operational costs [91,93,94,95].

Evolution Over the Years

The adoption of DTs in power systems has advanced significantly over time:

• Early Development: Initial applications were limited to basic system simulation and condition monitoring [91,92].
• Integration with IoT: The convergence of IoT and DTs enabled real-time, bi-directional communication between physical infrastructure and virtual models [96,97].
• Advanced Applications: Recent developments include integration with smart grids, energy storage optimization, and renewable energy simulations [98,99,100].

Impact on Power Systems

Deployment of DTs has significantly influenced several dimensions of modern electrical networks:

• Operational Efficiency: Real-time monitoring and predictive analytics improve the reliability and efficiency of power systems [101,102].
• Renewable Energy Integration: By simulating generation profiles and grid interactions, DTs can enhance the planning and coordination of renewables [98,99].
• Lifecycle Management: Predictive maintenance and asset health diagnostics reduce failures and extend equipment lifespan [93,94,95].
• Energy Management: Applications in smart homes and buildings improve demand forecasting and consumption efficiency [97,103].

Challenges and Future Directions

Despite their promise, several challenges remain in deploying Digital Twin systems:

• Data Privacy and Security: Protecting data from breaches and ensuring cybersecurity are essential for system resilience [102].
• Integration with Legacy Systems: Compatibility issues may arise when integrating digital twins with older power infrastructure [97].
• Need for Skilled Professionals: Implementation requires domain expertise in both power systems and digital modeling [97].

Future research and development efforts should address the following priorities:

• Standardization: Establishing common frameworks to ensure system interoperability and scalability [102,104].
• Advanced Protection: Improving cybersecurity protocols to safeguard digital infrastructure [102].
• Interdisciplinary Collaboration: Encouraging cooperation among engineering, data science, and cybersecurity experts to drive innovation [97].

3.4.4. Emerging Computational Technologies in Power Systems: Edge Computing, Data Analytics, and Big Data Technologies

The convergence of Edge Computing, Data Analytics, and Big Data has reshaped the operation and optimization of electrical power systems. This section presents an overview of their roles, applications, evolution, and systemic impacts.

Edge Computing

Edge Computing refers to processing data at or near the source rather than relying on centralized servers. This architecture reduces latency, minimizes bandwidth usage, and supports real-time decision-making [105,106].

• Distributed Optimization and Control: Localized data processing enhances the performance of microgrids, distributed charging schemes, and protection systems [105].
• Smart Grid Operations: By reducing communication delays, Edge Computing improves the responsiveness and reliability of smart grids [42,51,52].
• Integration with Big Data: Edge Computing frameworks manage growing data volumes from renewable energy systems and electric vehicles, enabling scalable and resilient energy infrastructure [107,108].

Data Analytics

Data Analytics plays a pivotal role in processing high-frequency data streams from smart grids, enabling informed operational decisions [109].

• Fault Analysis and State Estimation: Analytical tool help to detect faults, estimate grid state variables, and assess system security [110].
• Energy Management: Data Analytics supports load forecasting, optimization of energy flows, and the seamless integration of renewables [111,112].

Big Data Analytics

Big Data Analytics involves the application of Machine Learning models, data mining, and forecasting, which can derive actionable insights from large datasets [113,114].

• Smart Grid Implementation: Enhances the autonomy of microgrids, coordinates electric vehicle operations, and supports real-time control of distribution networks [113].
• Electricity Markets and Theft Detection: Supports electricity market optimization and helps detect energy theft to ensure grid stability [110,113].
• Renewable Energy Integration: Enables more accurate forecasting and better integration of renewable sources to support carbon neutrality goals [112].

Evolution Over the Years

• Initial Progress: Early applications in the early 2000s focused on managing and integrating large datasets from diverse sources [114].
• Technological Advancements: The convergence of AI, IoT, and Edge Computing has significantly improved real-time analytical and control capabilities [42,108].
• Current Trends: Today’s systems leverage Edge Analytics and Big Data to manage increasing complexity and ensure sustainability [51,107,115].

Impact on Power Systems

• Enhanced Efficiency and Reliability: Real-time data analysis and localized processing improve the responsiveness and robustness of power systems [42,51].
• Improved Grid Management: These technologies have revolutionized fault detection, anomaly classification, and distributed energy resource coordination [105,108].
• Support for Renewable Energy: Big Data and Edge Computing facilitate the large-scale integration of renewables, contributing to sustainable and carbon-neutral power systems [107,112].

3.4.5. Machine Learning in Electrical Power Systems

Machine Learning (ML) has become a vital component in the modernization of electrical power systems. This section presents a structured overview of its primary applications, historical development, and systemic impacts.

Applications of Machine Learning in Electrical Power Systems

ML techniques have been deployed across a wide range of use cases in power systems:

• Load Forecasting: ML models are widely used to predict future electricity demand in order to optimize power generation and distribution planning [116,117,118].
• Predictive Maintenance: ML algorithms identify early signs of equipment degradation, reducing maintenance costs and preventing unplanned outages [116,119].
• Fault Detection and Diagnosis: ML models detect and localize faults in the power grid, improving grid reliability and minimizing service interruptions [116,120,121,122].
• Energy Management: ML optimizes the dispatch and control of energy resources, supporting efficiency improvements and renewable energy integration [116,122,123].
• Power Quality Monitoring: Advanced models monitor voltage disturbances and frequency deviations to maintain power quality [124,125].
• Security Assessment: ML assists in assessing the dynamic security of power systems, identifying vulnerabilities and mitigating risks [48,126].

Evolution Over the Years

The role of Machine Learning in power systems has expanded through several technological stages:

• Early Applications: Initial uses were focused on fundamental tasks such as fault detection and load forecasting using rule-based systems and basic classifiers [127].
• Advancements in Algorithms: The development of sophisticated models such as convolutional neural networks, support vector machines, and decision trees has enabled more accurate and scalable ML applications [116,117].
• Integration with Big Data: ML models have benefited from access to large-scale datasets, enhancing their ability to detect patterns and make reliable predictions [128,129].
• Real-Time Applications: Current ML solutions support real-time decision-making and control in smart grids and distributed energy systems [88,124].

Impact on Power Systems

Machine Learning has introduced a wide range of benefits while also presenting new challenges:

• Enhanced Efficiency: ML enables process optimization and demand-supply balancing, resulting in improved energy efficiency and cost savings [121,130].
• Improved Reliability: Predictive maintenance and advanced fault detection have contributed to higher system availability and fewer outages [120,126].
• Integration of Renewable Energy: ML assists in forecasting generation from variable renewable sources and managing their incorporation into the grid [123,131].
• Proactive Risk Management: By anticipating potential threats, ML models support better contingency planning and risk mitigation [48,126].
• Environmental Impact: Although ML improves grid performance, the computational intensity of some models contributes to increased energy consumption and carbon emissions [88].

3.4.6. Advanced Metering Infrastructure (AMI) in Electrical Power Systems

Advanced Metering Infrastructure (AMI) is a foundational technology in modern electrical power systems. It consists of smart meters, communication networks, and data management platforms that enable two-way communication between utilities and consumers. This part presents an overview of AMI’s development, practical applications, and system-wide impacts.

Overview and Evolution

AMI initially served basic metering functions such as automated meter reading and billing [53,54]. Over time, it has evolved into a comprehensive infrastructure that supports advanced functionalities including outage management, voltage monitoring, dynamic pricing, and renewable energy integration [55,56,57].

• Early Stages: These were focused on meter-to-cash functionalities and basic consumption tracking [53,54].
• Integration with Communication Technologies: The adoption of advanced communication protocols enhanced AMI’s responsiveness and scalability [132,133].
• Support for Renewable Energy: Modern AMI systems can monitor and coordinate Distributed Energy Resources (DERs), enabling effective renewable integration [55,56,134].
• Advanced Applications: AMI now supports outage detection, dynamic pricing, demand response, and grid optimization, positioning it as a core enabler of smart grids [54,57,135].

Applications in Electrical Power Systems

AMI contributes to various operational and strategic domains in power systems:

• Energy Management: Real-time data enable utilities to monitor and optimize energy flows, thereby reducing losses and enhancing distribution efficiency [54,55,136].
• Demand Response: AMI facilitates demand-side management by enabling adaptive load control during peak demand events [56,136].
• Outage Management: Utilities can promptly detect, locate, and respond to outages, thereby minimizing downtimes and improving service quality [54,57].
• Integration with Renewable Energy: AMI supports the coordination of solar, wind, and other distributed sources through accurate and granular data on consumption and generation [55,56,134].
• Dynamic Pricing: AMI enables utilities to implement time-of-use or real-time pricing structures, encouraging demand shifts and improving grid load balancing [135].

Impact on Power Systems

The deployment of AMI has led to measurable improvements in grid performance and user engagement:

• Enhanced Grid Reliability: AMI’s ability to detect and respond to faults in real time has significantly improved power system resilience [54,57].
• Improved Energy Efficiency: Access to real-time usage data allows both utilities and consumers to more effectively reduce waste and manage consumption [55,137].
• Consumer Empowerment: Through access to detailed usage analytics, consumers can make informed decisions, optimize energy use, and reduce costs [137,138].
• Support for Smart Grids: AMI serves as a digital backbone for smart grid functions, enabling electric vehicle integration, advanced control systems, and distributed energy management [56,132].

Conclusion: Advanced Metering Infrastructure has transformed power systems by enabling real-time monitoring, enhancing operational efficiency, and empowering end users. Its evolution from simple metering to a sophisticated digital framework highlights its critical role in the development of intelligent and resilient energy networks.

4. Conclusions

This study analyzed 23,074 peer-reviewed publications to investigate the integration of digital technologies in renewable energy-oriented power systems. Among these, Artificial Intelligence (AI) emerged as the most researched and cited field, peaking in 2024 with 1209 papers and 15,667 citations. This reflects AI’s central role in predictive maintenance, forecasting, and intelligent energy management. The Internet of Things (IoT) followed closely with over 10,000 citations, driven by its widespread adoption in real time monitoring, smart metering, and system responsiveness. Data analytics technologies, particularly Big Data and Machine Learning, also demonstrated high levels of academic interest and practical application. In contrast, Blockchain and Edge Computing, while less dominant in volume, have shown steady growth and are increasingly recognized for enabling decentralized, secure, and low-latency operations in smart grids.

The reviewed literature indicates that these technologies have been broadly deployed across various energy system components. AI and ML are central to optimizing operations such as demand forecasting and predictive fault detection. IoT enables continuous monitoring and control of distributed resources. Blockchain enhances trust and transparency in decentralized energy markets and peer-to-peer trading platforms. Finally, Data Analytics in combination with Edge Computing supports adaptive control and decision-making in real-time energy management systems. These technologies not only bring computational intelligence to energy systems but also foster the convergence of physical and digital infrastructures.

The observed decline in research output in 2025, particularly in citation activity, may be attributed to either indexing delays or an inflection point in technological maturity. It is possible that certain technologies have reached saturation in core knowledge while others are undergoing transitions into newer application domains. Additionally, there remains a considerable research output imbalance, with limited contributions from low- and middle-income countries. Bridging this gap through inclusive collaboration will be critical to ensuring that digital energy technologies scale equitably across diverse global regions.

The insights from this study serve a dual purpose: first, to guide researchers by identifying mature and emerging areas of digital innovation within energy systems, and second, to support policymakers and planners in aligning infrastructure and regulatory strategies with the synergistic strengths of AI, IoT, Blockchain, and Big Data. As the energy sector moves toward decarbonization and digitalization, the integration patterns and research priorities identified in this review offer a foundational roadmap for intelligent and sustainable system transformation.

5. Discussions and Practical Implications

The integration of digital technologies into renewable energy-oriented power systems presents significant implications for both technological advancement and environmental policy. The findings of this review indicate several strategic insights.

5.1. Technological Innovations and Environmental Goals

Machine Learning and Artificial Intelligence technologies facilitate predictive control, energy demand, forecasting and adaptive maintenance, which in turn allow for minimizing operational losses and increasing the assimilation of intermittent renewables. All of these developments can help to achieve increased grid precision, decreased emissions, and improved efficiency.

The Internet of Things (IoT) and Advanced Metering Infrastructure (AMI) provide real-time monitoring and decentralized visibility, allowing energy systems to shift flexibly in response to multi-dimensional consumption patterns. This aids in balancing the demand-side management of energy and better deployment of clean energies.

Although Blockchain technologies are relatively new, they provide secure systems for peer-to-peer energy trading and tracking carbon credits which can engage prosumers and foster transparency. Nevertheless, Blockchain must first overcome regulatory and energy efficiency barriers in order to allow for sustainable growth.

5.2. Policy Recommendations

Based on our bibliometric and thematic findings, we propose the following recommendations for policymakers and energy planners:

• Promote interoperability standards: Governments need to foster standardized IoT, AI integration, and data privacy frameworks to enhance interoperability across all platforms.
• Support regulatory sandboxes for Blockchain and AI: Pilot environments can support the examination of novel technologies in a controlled observational setting, showcasing their application in real-world scenarios.
• Invest in digital infrastructure for low-income regions: Our results underscore the lack participation from the global south. To address this, focused financial support can improve the equity and scalability of sustainable energy approaches.
• Prioritize digital readiness in renewable energy policies: Consideration should be given to specific objectives concerning the milestones of digital transformation concomitant with the use of renewable energy in the national strategies.
• Foster cross-disciplinary research and innovation hubs: Robust collaborative frameworks across energy, data science, and cybersecurity domains can expedite the creation of resilient and future-ready energy systems.

6. Future Work and Research Directions

Based on the outcomes of this review, a few gaps in research need to be explored in order to fully facilitate the use of digital technologies within renewable energy focused power systems:

• Improving collaboration across fields: There is no doubt that the integrated Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain security models require detailed frameworks that address separate their specific subsystems. Such integration will be required for the development of safe, self-sufficient, and expandable energy systems.
• Practical validation with case studies: Application of digital technologies in microgrids and Digital Twin modeling of hybrid energy systems is a dire need of the hour. Pilot systems and test beds are imperative for determining their technological feasibility, integration competence, and human-factor engineering aspects around usability.
• Cross-domain integration with policy and innovation systems: Future studies should incorporate patent data, regulatory documents, and policy roadmaps in order to analyze the co-evolution of digital and institutional innovations. This integrative approach can support the development of harmonized strategies linking technology deployment with regulatory frameworks and investment planning.
• Monitoring commercialization and industry trends: Further bibliometric work could be extended to include industrial white papers and technology roadmaps, which would allow for assessing the commercialization trajectories of AI, Blockchain, and IoT in renewable energy systems. Understanding market readiness and technology maturity will help to align academic research with industry needs.

These research directions aim to bridge existing knowledge gaps, inform strategic planning, and facilitate the responsible and inclusive deployment of digital technologies for global energy transition goals.