From Technology to Strategy: The Evolving Role of Smart Grids and Microgrids in Sustainable Energy Management

Abstract

This study presents a comprehensive bibliometric review of 136 academic publications on smart grids, microgrids, and semiconductor technologies in the context of sustainable energy management. Data were collected from the Web of Science Core Collection and analyzed using VOSviewer to identify intellectual structures, thematic clusters, and research trajectories. The results demonstrate the increasing prominence of semiconductor-enabled solutions in advancing renewable energy integration, grid optimization, and energy storage systems. Five major research themes are identified: renewable energy and smart grid integration; distributed microgrid systems; optimization models; control strategies; and system-level resilience and cybersecurity. The analysis reveals a temporal evolution from foundational engineering (2020–2021) to intelligent, digitally enhanced energy systems (2022–2025), with a growing emphasis on electric mobility, digital twins, and advanced energy management techniques, such as convex optimization. Beyond mapping trends, this study underscores critical research gaps in the non-English literature, multi-database integration, and practical deployment. The findings provide actionable insights for researchers, policymakers, and industry leaders by highlighting technological maturity, real-world applications, and strategic implications for energy transition. By aligning digital intelligence, semiconductor innovation, and sustainable energy goals, this review advances a forward-looking agenda for resilient and equitable energy systems.

1. Introduction

The global push toward sustainable and resilient energy systems has led to significant technological, institutional, and behavioral shifts in the ways in which energy is produced, distributed, and consumed [1]. In this evolving landscape, smart grids and microgrids have emerged as central components of a decentralized and digitalized energy infrastructure [2]. While initially conceptualized as engineering solutions to modernize aging power grids, their roles have expanded considerably, encompassing not only technical innovation but strategic functions related to governance, energy policy, risk management, and organizational change [3].

Smart grids refer to electricity networks that leverage advanced communication, automation, and real-time data processing to optimize generation, distribution, and consumption [4]. By enabling two-way communication between suppliers and consumers, these systems facilitate the integration of renewable energy sources, enhance demand-side management, and increase overall system flexibility [5]. In parallel, microgrids, localized energy systems that can operate either independently or in conjunction with the main grid, contribute to energy reliability, disaster resilience, and rural electrification [6]. Both technologies are considered enablers of the energy transition, supporting the shift away from fossil fuel dependence toward low-carbon, distributed, and intelligent energy systems [7]

However, the rise of smart grids and microgrids presents more than just technological advancements [8]. Their implementation and scalability depend heavily on multi-level coordination among stakeholders, including utility companies, regulators, policymakers, technology developers, and end users [9]. The success of these systems increasingly hinges on factors such as regulatory frameworks, financial models, public–private partnerships, user acceptance, and interdisciplinary planning [10]. In this regard, the development of smart grids and microgrids must be understood not only as a matter of engineering excellence but as a strategic management challenge, one that requires innovation in governance, market design, and energy system thinking [10].

A critical but often underexplored element in this transition is the role of semiconductor technologies [11]. Semiconductors underpin the advances in power electronics, grid intelligence, and storage integration, enabling more efficient energy conversion, predictive analytics, and secure digital infrastructure [12]. Recent studies have highlighted their importance in improving inverter efficiency, facilitating AI-driven energy management, and supporting electric mobility [13]. As such, semiconductors form a technological bridge between digital intelligence and sustainable energy systems, yet their role is fragmented in the current literature.

Despite the critical importance of this dual technological–strategic evolution, the existing body of literature is vast, fragmented, and multidisciplinary, making it difficult for scholars and practitioners to form a cohesive view of the field’s intellectual structure and thematic priorities. Previous narrative reviews have provided important insights [11,12,13], but they often remain descriptive and lack a systematic mapping of intellectual linkages. Furthermore, research gaps persist regarding the integration of semiconductor innovation with sustainable energy management, the extent of cross-regional contributions, and the policy and industry implications of emerging technologies [14,15].

To address these gaps, this study employs a bibliometric analysis to systematically examine global research on smart grids, microgrids, and semiconductor-enabled sustainable energy solutions over the period 2009–2025. A bibliometric analysis provides a robust methodological framework to identify patterns of scientific productivity, influential publications, key authors and institutions, collaborative networks, and emerging thematic clusters [16,17]. Unlike traditional literature reviews, bibliometric approaches offer a quantitative and visual understanding of the evolution and current state of a research field, enabling a more comprehensive and objective assessment.

Through this analysis, this study aims to uncover how the discourse surrounding smart grids and microgrids has transitioned from a primarily technological and engineering-focused orientation to a broader strategic one that incorporates energy management, semiconductor innovation, resilience, and socio-economic considerations. Specific attention is given to technological maturity, real-world applications, and strategic implications, thereby offering forward-looking insights for researchers, policymakers, and industry leaders.

The insights generated from this research are expected to serve multiple audiences. For researchers, it provides a consolidated view of key themes, gaps, and influential works in the field. For policymakers and energy managers, it offers evidence-based guidance on the strategic dimensions of smart grid and microgrid deployment, including lessons learned and emerging global practices. For technologists and system designers, it emphasizes the importance of aligning engineering solutions with institutional and societal contexts, while highlighting the enabling role of semiconductor technologies in fostering innovation.

Therefore, this study contributes a timely and comprehensive bibliometric review of the evolving role of smart grids, microgrids, and semiconductors within sustainable energy management. By blending bibliometric methods with strategic analysis, it advances an integrated understanding of how these technologies are shaping, and being shaped by, the broader quest for a clean, reliable, and equitable energy future.

2. Literature Review

The global energy landscape has undergone a profound transformation in recent years, driven by escalating environmental concerns, technological advancements, and policy commitments to decarbonization [18]. In this context, smart grids and microgrids have emerged as central pillars in the pursuit of energy sustainability and resilience [19]. Smart grids integrate digital communication, automation, and control systems into traditional electricity networks to enhance efficiency, flexibility, and real-time responsiveness [11]. Microgrids, by contrast, are localized energy systems capable of operating autonomously or in concert with the main grid, offering tailored solutions to specific communities or industrial zones [20]. The integration of these technologies marks a paradigm shift from centralized energy control to distributed energy governance [21].

The literature emphasizes that these technologies are not merely engineering innovations but essential enablers of broader strategic goals, including energy equity, security, and environmental protection [22]. With global commitments to net-zero emissions accelerating, renewable energy integration within smart grid and microgrid systems has become increasingly prominent. Microgrids powered by solar PV, wind, biomass, and hydro play a pivotal role in achieving energy autonomy, particularly in underserved regions [19,23]. These systems support energy democratization by allowing communities and institutions to generate, store, and manage their own energy, thus reducing dependence on centralized infrastructures and fossil fuels [24]. At the same time, semiconductor technologies underpin advanced inverters, storage integration, and real-time analytics, ensuring reliable and efficient operation of these distributed systems [12,13].

Strategically, smart grids enable advanced demand response, predictive maintenance, real-time monitoring, and consumer engagement through data-driven platforms and IoT technologies [21,25]. These innovations are reshaping the role of energy consumers into active participants, “prosumers”, who contribute to grid stability and sustainability. Biswas et al. [22] discussed how smart grids, when equipped with AI and machine learning, can forecast load patterns, optimize energy flows, and facilitate peer-to-peer energy trading models. Such capabilities are essential for the operational complexity introduced by intermittent renewables and growing electrification of sectors such as transportation and heating [26]. Recent work in IEEE Transactions on Smart Grids has demonstrated progress in advanced demand response algorithms [27], resilient grid operation under cyber threats [28], and the integration of electric vehicles into distribution networks [29]. These contributions highlight both technological maturity and ongoing challenges in system optimization, digital security, and interoperability.

From a policy and governance standpoint, the transition toward smart and microgrid-based energy systems is heavily influenced by regulatory frameworks, public–private partnerships, and national energy strategies, underlining the necessity of aligning technical deployments with supportive institutional environments, including subsidy schemes, tariff structures, and standards for grid interconnection. Moreover, the International Energy Agency underscores the critical role of multi-level coordination between governments, utilities, and consumers to overcome deployment barriers and to achieve cost-effective scaling. In emerging economies, challenges such as policy fragmentation, financial constraints, and limited technical capacity hinder broader adoption despite the high potential for impact [30]. This indicates a gap between technical feasibility and policy readiness that requires interdisciplinary coordination.

Resilience and adaptability also form a growing strand in the literature, particularly in light of increasing climate-related disruptions [31]. Smart grids and microgrids offer modular and robust configurations that can isolate disturbances and restore power more swiftly than conventional grids. Younesi et al. [26] showed how disaster-prone regions can significantly benefit from microgrids equipped with backup storage and islanding capabilities, which are vital for hospitals, military bases, and critical infrastructure. This resilience aspect further strengthens the case for integrating these technologies into national disaster preparedness and urban planning policies [32]. Advanced energy management methods, including convex optimization for hybrid AC/DC microgrids, have been shown to enhance efficiency under high renewable penetration [33]. However, these approaches remain underexplored in broader strategic discussions, suggesting a gap between technical advances and their system-level deployment.

The role of storage technologies, especially battery storage and emerging options like green hydrogen, is another central theme [13]. Energy storage not only ensures supply continuity but enables time-shifting of renewable energy generation, which is crucial for grid balancing. As discussed by Elazab et al., the pairing of microgrids with energy storage creates a dynamic system capable of managing variability while reducing grid congestion and operational costs. Furthermore, energy storage allows for strategic arbitrage opportunities in deregulated energy markets, making smart grid investments more financially viable for private stakeholders [34]. Yet, questions remain about lifecycle impacts, cost optimization, and semiconductor-enabled improvements in efficiency. Addressing these will be crucial for scaling up deployment.

Another important dimension explored in recent studies is the socio-economic and behavioral integration of these systems. Public acceptance, digital literacy, and consumer engagement significantly influence the successful deployment and utilization of smart energy systems [35]. Behavioral energy management programs, when integrated into smart grid infrastructures, can yield significant efficiency gains and foster more sustainable consumption patterns [21]. This human-centered approach is increasingly critical in designing inclusive and equitable energy transitions that align with both environmental goals and social justice imperatives [36]. Nevertheless, studies indicate persistent gaps in inclusivity, affordability, and equity of access, particularly in developing regions

In summary, the literature illustrates a trajectory from discrete engineering solutions toward integrated systems that combine technical, managerial, social, and environmental dimensions. However, the following key gaps remain: (1) limited integration of semiconductor research into the sustainable energy literature; (2) underrepresentation of non-English and non-WoS sources, constraining global coverage; (3) insufficient cross-linkages between technical optimization (e.g., convex dispatch, AI forecasting) and strategic policy implications; (4) fragmentation in socio-economic analyses, especially regarding equity and inclusivity.

These gaps justify the present bibliometric study, which systematically maps the intellectual structure, thematic evolution, and research frontiers of smart grids, microgrids, and semiconductors within sustainable energy management.

3. Research Methodology

This study adopts a bibliometric approach to investigate the evolution and strategic transformation of research on smart grids, microgrids, and semiconductor-enabled sustainable energy management. Bibliometric analysis, as a quantitative method, facilitates the systematic evaluation of scientific outputs, uncovering patterns in authorship, institutional collaboration, thematic development, and citation structures [14]. By analyzing publication trends and intellectual linkages, this method allows researchers to assess the maturity of the field and to identify key knowledge domains and emerging research trajectories [15].

3.1. Data Collection

The dataset was retrieved from the Web of Science (WoS) Core Collection, chosen for its rigorous indexing standards and comprehensive coverage of high-impact journals. The search strategy employed Boolean operators, such as (“smart grid” OR “microgrid”) AND (“sustainable energy management” OR “energy strategy”). This produced four effective keyword combinations, ensuring coverage of both technological and managerial dimensions.

The time frame was restricted to 1 January 2009–30 June 2025, with the search limited to publications in English. The restriction to English was applied to ensure consistency in metadata quality; however, we acknowledge that this may exclude important non-English contributions. In line with reviewer recommendations, future research should expand to include Scopus, IEEE Xplore, and non-English sources to provide more comprehensive coverage.

3.2. Data Screening and Processing

The initial search yielded 179 records. After removing duplicates, irrelevant items (e.g., conference abstracts, editorials), and documents outside the defined subject areas, a final dataset of 136 records was retained. Each record contained detailed bibliographic information (titles, abstracts, keywords, author affiliations, countries, funding sources, and citation metrics).

3.3. Analytical Tools and Techniques

Two principal bibliometric software tools were employed VOSviewer (v1.6.20), used to visualize co-occurrence networks, bibliographic coupling, and cluster evolution, and Microsoft Excel, applied to clean the data, to identify publication and citation trends, and to perform descriptive statistical analyses.

Performance analysis was applied to assess publication productivity, citation impact, and the most influential authors, journals, and institutions. Keyword co-occurrence analysis was conducted to identify major thematic clusters and their temporal evolution [37].

3.4. Temporal Segmentation

A temporal segmentation was applied to examine how the research focus has shifted across three distinct periods: 2020–2021, 2022–2023, and 2024–2025. This division allowed for a longitudinal perspective on the development of the literature. Furthermore, a cluster analysis using VOSviewer’s modularity-based algorithm was performed to group keywords into thematic clusters, offering insights into dominant and emerging research areas [17]. This segmentation facilitated a longitudinal analysis of how the research focus has evolved across time. The inclusion of partial data for 2025 is explicitly noted; to avoid misinterpretation, figures and captions have been updated to clarify that 2025 results represent only half a year of output.

While the bibliometric approach ensures objectivity and systematic mapping, it is not without limitations. First, reliance on WoS and English-language publications constrains global inclusivity. Second, keyword-based searches may omit relevant studies that employ alternative terminology. These limitations have been addressed by suggesting future integration of multiple databases (e.g., Scopus, IEEE Xplore) and broader linguistic coverage using AI-assisted translation tools.

4. Results and Discussions

4.1. Publication Trends and Growth Patterns

The temporal distribution of publications on the topic of smart grids and microgrids in sustainable energy management reveals a dynamic and accelerating research interest, particularly in recent years (Figure 1). Of the 136 total documents analyzed, the period from 2020 to 2025 accounts for a remarkable 78.26%, reflecting a clear surge in scholarly attention.

The most productive years were 2024 (34 publications, 25%) and 2025 (31 publications, 22.79%), demonstrating an intense focus on the subject in the past two years. This surge aligns with the growing global prioritization of decarbonization, renewable energy integration, and intelligent energy infrastructure. Policy shifts, energy crises, and technological advancements during this time have likely influenced the spike, as governments and industries seek scalable solutions to enhance energy resilience and sustainability.

By comparison, earlier years (2009–2019) witnessed only moderate and sporadic research output. From 2009 to 2019, the number of publications per year remained low, often not exceeding seven records annually. These earlier studies likely laid foundational theories and technological frameworks, yet lacked the momentum observed in more recent publications.

The shift after 2020 likely indicates that smart grids and microgrids transitioned from conceptual exploration to practical deployment and strategic integration. The years 2022 and 2023 also show notable activity with 20 (14.71%) and 12 (8.82%) publications, respectively, suggesting that the upward trajectory began gradually before sharply rising. These findings confirm that the research field has evolved significantly, mirroring the broader energy sector’s movement toward smarter, decentralized, and more sustainable systems.

The sharp growth after 2020 mirrors trends in related fields, such as renewable energy optimization and AI applications in energy, which also experienced acceleration during the COVID-19 era due to heightened attention on resilience, digitalization, and energy security [27]. This suggests that smart grid and microgrid research is not evolving in isolation but as part of a broader interdisciplinary push toward sustainable energy transitions.

The temporal imbalance also reveals an important structural insight: although the field has matured rapidly in recent years, the limited number of studies before 2020 highlights a relatively short research history compared to other domains, such as general renewable energy systems or climate change policy [38]. This underscores the “emerging” nature of the field, and indicates opportunities for more longitudinal and historical analyses.

Furthermore, the surge in 2024–2025 coincides with major international initiatives, including the UN’s Global Stocktake of climate progress (2023) and expanded national energy transition plans in Asia, Europe, and the Middle East. These events likely stimulated both academic and policy-driven research agendas, explaining the exceptional output during this period [39].

Overall, the trend demonstrates that research on smart grids and microgrids has moved from a niche area into the mainstream of sustainable energy discourse. However, the concentration of publications in just a few recent years may also suggest risks of thematic fragmentation or duplication, which future bibliometric studies should monitor.

4.2. Subject Categories

The distribution of subject categories demonstrates the interdisciplinary nature of the research on smart grids, microgrids, and sustainable energy management (Figure 2). The leading categories include Energy Fuels (58 records; 42.65%), Engineering Electrical Electronic (51 records; 37.50%), and Green Sustainable Science Technology (39 records; 28.67%). This distribution reflects the field’s strong technological orientation, as well as its strategic alignment with global sustainability goals.

The dominance of “Energy Fuels” and “Electrical and Electronic Engineering” confirms that much of the research remains anchored in core technical domains, such as power systems, energy storage, and renewable integration. This indicates a high degree of technological maturity in operational aspects, including optimization models, inverter design, and energy management systems.

At the same time, the prominence of “Green and Sustainable Science Technology” suggests a growing shift from purely engineering concerns toward interdisciplinary sustainability approaches, connecting energy research with environmental policy, economics, and long-term resilience. This reflects how smart grid and microgrid systems are increasingly framed not only as technical solutions but as instruments for advancing the global climate and decarbonization agenda.

Other categories, such as Computer Science Information Systems (22 records; 16.17%) and Automation Control Systems (17 records; 12.50%), highlight the integration of digital technologies and advanced control mechanisms. These clusters confirm the growing importance of artificial intelligence, optimization algorithms, and digital twins in modern energy management, supporting earlier insights from recent IEEE Transactions on Smart Grid studies (Ali et al., 2022; Munir et al., 2023) [27,28].

Conversely, categories such as “Social Sciences Interdisciplinary” (eight records; 5.88%) and “Economics” (six records; 4.41%) remain marginal. This underrepresentation suggests that, while the technical feasibility of smart grids and microgrids has been well established, socio-economic, behavioral, and governance aspects are still underexplored. Bridging this gap is critical, as energy transitions cannot succeed without parallel advances in policy frameworks, financing mechanisms, and consumer engagement.

In a comparative perspective, this imbalance mirrors the findings in renewable energy bibliometric studies, where technological clusters dominate while social and economic analyses lag behind [14]. Such asymmetry underscores the need for more interdisciplinary collaboration between engineers, economists, and policy scholars to ensure that innovations are both technically effective and socially equitable [40].

Overall, the subject category distribution underscores the technological maturity of the field, while simultaneously pointing to persistent gaps in socio-economic and governance research. Addressing these gaps will be essential to transform smart grids and microgrids from technical prototypes into fully integrated pillars of sustainable energy systems worldwide.

4.3. Leading Journals

The analysis of source titles highlights the journals that serve as primary outlets for research on smart grids, microgrids, and sustainable energy management. The top three journals—Energies (22 publications; 16.18%), IEEE Access (18; 13.24%), and Sustainability (15; 11.03%)—together account for over 40% of the total output. This indicates that both open access interdisciplinary platforms and IEEE-affiliated outlets play central roles in disseminating knowledge.

Notably, while Energies and Sustainability emphasize broad multidisciplinary coverage, IEEE Access provides more technically focused contributions, aligning with the engineering-driven nature of the field. However, the relatively modest representation of IEEE Transactions on Smart Grid, despite its recognized prestige, suggests that some of the highest impact work remains underrepresented in the current dataset. This contrasts with bibliometric studies in adjacent fields, such as renewable energy forecasting, where IEEE Transactions dominate the landscape [33].

Another point of comparison lies in citation performance: while Sustainability and Energies produce a high volume of papers, the IEEE journals often yield higher average citations per article [29]. This distinction reflects the dual pathway of the field—balancing accessible, broad-coverage dissemination with highly specialized, technically rigorous outlets.

Overall, the results suggest that, although multidisciplinary and open access journals dominate in volume, IEEE’s flagship outlets remain essential for high-impact contributions. Future bibliometric reviews should therefore pay particular attention to IEEE Transactions and similar premier venues to fully capture the intellectual structure of the field (Figure 3).

4.4. Mapping the Contributions to the Sustainable Development Goals (SDGs)

The mapping of the dataset to the United Nations Sustainable Development Goals (SDGs) demonstrates how research on smart grids and microgrids contributes to the broader sustainability agenda (Figure 4). The strongest connections are with SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), which together account for more than half of the mapped publications. This reflects the field’s direct role in enabling renewable integration, decarbonization, and climate resilience.

SDG 9 (Industry, Innovation, and Infrastructure) also emerges prominently, underscoring the technological orientation of the field, where smart grids and microgrids are framed as infrastructural enablers of innovation-driven energy transitions. Similarly, links with SDG 11 (Sustainable Cities and Communities) illustrate the increasing focus on urban resilience, smart mobility, and community-level energy autonomy.

By contrast, connections to SDG 1 (No Poverty), SDG 5 (Gender Equality), and SDG 10 (Reduced Inequalities) remain marginal. This imbalance suggests that, while the technological and environmental impacts of smart grid and microgrid systems are well recognized, their socio-economic and equity dimensions are comparatively underexplored. This mirrors the trends in broader sustainability bibliometrics, where environmental and technological SDGs dominate, while social justice-oriented goals are less visible [12,41].

Comparatively, renewable energy bibliometric reviews (e.g., wind and solar integration) also show a concentration on SDGs 7, 9, and 13, but often report stronger contributions to SDG 12 (Responsible Consumption and Production) than found here. This highlights a relative gap in addressing lifecycle considerations and consumption-side dynamics within smart grid and microgrid research [42].

Overall, the SDG mapping indicates that the field is well positioned to drive progress in clean energy, climate action, and infrastructural innovation. However, a more deliberate integration of social inclusion and equity concerns will be required to align the research more fully with the holistic sustainability agenda envisioned by the UN.

4.5. Country-Level Contributions

The geographic distribution of publications in Figure 5 reveals a clear concentration of scholarly interest in a few key countries, particularly in Asia. India leads the publication output with 27 records, accounting for approximately 19.85% of the 136 publications analyzed. China follows closely with 26 records (19.12%). According to the International Energy Agency (2023), this dominance by India and China aligns with their ambitious national strategies focusing on sustainable energy development, climate action, and industrial innovation. India’s National Solar Mission and China’s extensive investment in renewable technologies likely drive academic research in clean energy, infrastructure, and sustainability themes.

Saudi Arabia emerges as the third most productive country with 14 publications (10.29%). This reflects the kingdom’s recent strategic pivot towards sustainability, particularly under its Vision 2030 framework, which emphasizes reducing reliance on oil through diversification into clean energy and green technologies [43]. Malaysia and the United States each contributed nine articles (6.62%), indicating moderate but consistent engagement with the topic. While Malaysia is advancing its green technology master plan, the U.S. research contribution remains relevant due to its global leadership in clean tech innovation and climate change policies.

China’s leadership reflects strong state-led investment in energy transition technologies, particularly renewable integration and microgrid demonstrations. The United States shows a more diverse landscape, with contributions from both academic institutions and industry-driven projects, while India’s growing share highlights its focus on decentralized energy solutions to address rural electrification and resilience challenges.

Other notable contributors include Morocco (eight records), Australia and Iran (seven records each), and Bangladesh (six records). Morocco’s emerging role in Africa’s solar energy transition, especially through the Noor Solar Project, the world’s largest concentrated solar power plant, has drawn international research attention [44]. Similarly, Australia’s record reflects the country’s interest in renewable energy policies and sustainable urban development. Iran’s academic output, despite geopolitical constraints, suggests an internal focus on optimizing energy efficiency and environmental resilience. Bangladesh’s growing presence in this field is likely driven by its vulnerability to climate change and its proactive engagement in adaptation strategies.

The data underscore a regional clustering of academic output in Asia and parts of the Middle East. These patterns reflect a broader geopolitical shift where emerging economies are increasingly contributing to the global discourse on sustainability. The high levels of publication from these countries highlight both the urgency of environmental issues in these regions and their growing capacity for academic and technological innovation.

A closer inspection reveals that, in many of these cases, publications are concentrated within one or two research groups, indicating localized expertise rather than a broad national research base. This suggests that, while innovative work is being conducted, it cannot yet be interpreted as a widespread adoption of the topic at the national level.

The collaborative dimension also reveals disparities. Countries like the UK and Germany often appear as co-authors in cross-national projects, whereas countries with smaller outputs (e.g., Bangladesh, Malaysia) tend to publish more in isolation. This imbalance underscores the importance of international collaboration networks in amplifying research visibility and impact.

Overall, the country-level distribution demonstrates both the global relevance of smart grid and microgrid research and the concentration of influence in a few technologically advanced nations. For emerging economies, fostering international partnerships and capacity building will be key to broadening the research base and ensuring that smart grid and microgrid solutions address diverse local contexts.

4.6. Thematic Clustering

The co-occurrence network visualization generated in

Table 1. Research Clusters and Trends.

Cluster	Focus Area	Keywords (Selected)	Key Themes
Red	Renewable Energy and Smart Grid	Renewable energy, electric vehicles, energy efficiency	Integration and load management
Green	Microgrids and Distributed Energy Systems	Microgrids, energy storage, peer-to-peer, trading	Decentralization and resilience
Blue	Optimization Models and Techniques	Optimization, model, generation, placement	Modeling and simulation
Yellow	Control Systems and Algorithms	EMS, economic dispatch, algorithm, strategy	Control strategies and energy balance
Purple	Cybersecurity and System Resilience	Security, impact, smart grids, operation	Safety, reliability, digital risk

and Figure 6 reveals five distinct thematic clusters related to smart grid and energy research. Each color-coded cluster groups keywords based on their co-occurrence patterns, allowing us to infer dominant research themes and evolving trends within this interdisciplinary domain.

Red Cluster—Renewable Energy and Load Management Focus

The red cluster is centered around “renewable energy”, “energy efficiency”, and “smart grid”, indicating a significant body of the literature focused on integrating renewable energy sources into smart grids. Keywords like “consumption”, “load”, “demand”, and “prediction” suggest an emphasis on demand-side management and forecasting in the context of variable renewable energy sources. The presence of terms such as “electric vehicles”, “market”, and “security” points to emerging interests in the secure and market-based integration of electric mobility solutions into smart grid systems. This cluster highlights the growing concern with balancing supply and demand in a decarbonized grid, with a strong reliance on predictive modeling and efficiency-driven solutions [45,46]. Compared to earlier bibliometric reviews in renewable energy [47], this cluster demonstrates greater maturity, as research has moved from theoretical formulations toward pilot projects and practical applications in load forecasting and grid stability. This indicates a readiness for large-scale deployment in both urban and industrial settings.

Green Cluster—Distributed Systems and Energy Trading

The green cluster revolves around “microgrids”, “distributed generation”, and “energy storage”, reflecting an increasing scholarly focus on decentralized energy systems. The co-occurrence of “peer-to-peer energy trading”, “cost optimization”, “batteries”, and “distribution systems” suggests a trend toward local, autonomous grids that leverage energy trading mechanisms and storage technologies. The appearance of keywords like “uncertainty”, “energy strategy”, and “sustainability” indicates that research in this cluster is also concerned with the planning, resilience, and long-term environmental implications of distributed energy systems. This thematic group represents a paradigm shift from centralized to distributed control in energy systems, enabled by digital platforms and market innovations. This thematic group represents a paradigm shift from centralized to distributed control, enabled by digital platforms and market innovations. Compared with the previous bibliometric studies that have emphasized technical feasibility, this cluster highlights growing commercial viability and policy relevance, especially in the contexts of community energy resilience and transactive energy systems [48,49].

Blue Cluster—Optimization Techniques and Modeling

The blue cluster is dominated by the term “optimization”, accompanied by related concepts, such as “model”, “generation”, “efficiency”, and “strategies”. This indicates a strong methodological trend in the literature focusing on the algorithmic and model-based optimization of energy systems. The inclusion of “integration”, “battery”, and “placement” implies that specific optimization applications target the spatial and technical integration of renewable energy and storage assets. This cluster reflects the foundation of smart energy research, where decision-support systems, modeling frameworks, and optimization algorithms are developed to ensure system reliability, efficiency, and economic viability. While earlier reviews often treated optimization as a supporting tool, this cluster shows it has evolved into a central research pillar with direct implications for real-world system design, siting, and operation, particularly in hybrid storage and multi-energy systems [50].

Yellow Cluster—Control Systems and Algorithm Development

The yellow cluster centers around “algorithm”, “energy management system”, and “economic dispatch”. This thematic area overlaps with engineering and computer science disciplines, as it focuses on the control and management of energy flows within smart grids and microgrids. Keywords like “strategy”, “optimization algorithm”, and “sustainable energy” point to the development of intelligent systems that manage real-time energy distribution based on economic and environmental goals. This cluster reflects a sustained interest in operational control, often through the use of advanced optimization and artificial intelligence techniques to enhance energy autonomy and efficiency. Recent advancements have confirmed the strategic role of AI-based algorithms in enabling self-adaptive, resilient systems that can operate under uncertainty, marking a progression beyond classical economic dispatch toward intelligent grid autonomy [51].

Purple Cluster—System-Level Integration and Security

The purple cluster includes keywords such as “smart grids”, “impact”, “energy optimization”, and “security”. This suggests a high-level, systemic view of energy infrastructure that encompasses not only technical optimization but broader considerations, like cybersecurity, regulatory impact, and system-wide integration. The overlap with the red and blue clusters points to interdisciplinary approaches, where technical, economic, and societal dimensions intersect. Notably, the inclusion of “operation” and “electric vehicles” also signals a trend toward electrification and its implications on smart grid operation and security. Compared to previous studies, this cluster shows increasing maturity in cyber–physical security and regulatory frameworks, which are critical for the safe scaling of smart grids and their integration into national infrastructures [52].

Emerging Trends

The thematic clusters reveal that current research is expanding from foundational optimization and control systems toward more holistic themes, such as decentralization, electrification, security, and sustainability. The convergence of keywords like “smart grid,” “optimization”, “microgrids”, and “renewable energy” at the center of the map suggests that these topics remain central to the field. The presence of “peer-to-peer trading”, “batteries”, and “energy strategy” in peripheral but increasingly connected clusters indicates the emergence of new directions driven by policy, market, and technological developments. Thus, the bibliometric landscape illustrates both the maturity of core areas and the dynamism of emerging subfields in smart grid and energy systems research. Compared to earlier bibliometric landscapes [53,54], the field is characterized not only by maturity in optimization and control but by diversification into socio-technical themes, such as market-based trading, resilience, and policy integration. This suggests that future research will require more interdisciplinary approaches, bridging engineering, economics, and governance.

4.7. Overlay Visualization

Based on the overlay visualization map, several notable temporal patterns in keyword co-occurrence within the field of sustainable energy and smart grid research can be identified (

Table 2. Temporal patterns in keyword co-occurrence.

Period	Color	Prominent Keywords	Research Focus
2020–2021	Dark Blue/Purple	Smart grid, Deep learning, Blockchain, Sustainability	Foundational technologies and early integration of AI and digital tools in energy systems
2022–2023	Green	Energy management, Renewable energy, Sustainable energy, Microgrid	Application-oriented research, energy distribution optimization, and integration of renewables
2024–2025	Yellow	Optimization, Energy storage, Electric vehicles, Resilience, Artificial intelligence	Emerging trends focused on intelligent systems, sustainability, resilience, and electric mobility.

, Figure 7). The color gradient, ranging from dark blue (2020) to yellow (2025), reflects the average publication year of the documents associated with each keyword, offering insight into the evolution of research priorities over time.

In earlier years (2020–2021), the focus of research was predominantly on foundational technologies and methodologies. Keywords such as “smart grid”, “deep learning”, “blockchain”, and “sustainability” are shaded in dark blue and purple tones, indicating their emergence and consolidation during this period. This suggests that, during the initial phase, scholars concentrated on integrating artificial intelligence and digital technologies into energy systems, particularly in the development of smart grids and decentralized infrastructures. Similar early-stage emphasis was reported in previous bibliometric studies on renewable energy (Donthu et al., 2021) [14], confirming that the period marked a conceptual foundation rather than wide-scale deployment.

As the field progressed into 2022 and 2023 (represented by green tones), a noticeable shift toward applied research and optimization became apparent. Keywords such as “energy management”, “renewable energy”, “sustainable energy”, and “microgrid” became more prominent. This transition highlights the growing academic attention to efficient energy distribution, local energy generation, and the management of renewable sources in decentralized networks. The frequent co-occurrence of these terms also reflects the increased efforts to integrate renewable energy into grid systems and to optimize energy usage. Compared with earlier bibliometrics in energy policy [55], the present results suggest a stronger technical and operational orientation, signaling the field’s progression from theory to practical applications.

The most recent developments, illustrated by the yellow tones (2024–2025), reveal a pivot toward emerging and forward-looking topics. Terms like “optimization”, “energy storage”, “electric vehicles”, “resilience”, and “artificial intelligence” are colored in lighter shades, indicating that they are at the forefront of current research efforts. These keywords point to a growing emphasis on system efficiency, sustainability under stress conditions, and the incorporation of electric vehicles into the broader energy ecosystem. The presence of energy optimization and sustainable energy management further underscores a contemporary focus on refining energy strategies for smarter and greener outcomes. In line with Arévalo and Jurado [12], the rise of AI and resilience research indicates a maturing field that is not only advancing technically but aligning with pressing global policy priorities, including the Sustainable Development Goals (SDGs) and climate resilience strategies.

Overall, the overlay visualization shows a clear temporal progression: from foundational digital integration (2020–2021), to applied system optimization (2022–2023), and finally to future-oriented trends (2024–2025) emphasizing resilience, electrification, and AI-enabled systems. This forward-looking trajectory reflects not only academic momentum but strategic imperatives for policymakers and industry leaders seeking robust, sustainable, and intelligent energy infrastructures.

4.8. Citation Trend Analysis (2009–2025)

The citation trend from 2009 to 2025 reveals a dynamic progression in scholarly attention toward the field of study, indicating an evolving and increasingly significant research domain (Figure 8). During the initial phase, from 2009 to 2015, the number of citations remained relatively low, with annual citations averaging fewer than ten. This limited engagement suggests that the topic had not yet gained widespread academic traction, and was likely considered a niche area of inquiry.

However, from 2016 to 2019, the trend began to shift, with citations demonstrating a modest yet consistent increase. This period likely marked the emergence of foundational studies on smart grid concepts and the early development of decentralized energy systems. Similar patterns of slow but steady growth have been reported in bibliometric reviews of renewable energy technologies, where early interest preceded later exponential growth [56].

A substantial transformation occurred between 2020 and 2023, during which the number of citations rose sharply. Notably, the citation count tripled from 2019 to 2020, a turning point that may have been influenced by global shifts in research priorities triggered by the COVID-19 pandemic, which accelerated digitalization, energy resilience debates, and the integration of renewables into recovery policies. This phase corresponds with the rise of applied research on energy storage, artificial intelligence, and optimization, reflecting the alignment of academic inquiry with urgent societal needs.

By 2023, the number of citations exceeded 250, and the upward trajectory peaked in 2024 with approximately 375 citations. This substantial growth underscores the widespread recognition of the field’s importance and its integration into broader scientific and policy discussions. This surge also coincides with international policy milestones, such as strengthened commitments to the Paris Agreement and growing momentum around the United Nations’ Sustainable Development Goals (SDGs), which further embedded smart grids and microgrids in the global sustainability agenda [57].

Although 2025 has shown a slight decline in citations (approximately 350), this is best interpreted as a result of incomplete data collection for the current year rather than a genuine drop in scholarly engagement. Such partial-year effects are common in bibliometric analyses [58] and do not indicate a downward trajectory.

Overall, the citation trend reflects a clear transition from marginal to mainstream academic discourse. The field has matured from conceptual explorations (2009–2015), to foundational research and systematization (2016–2019), to widespread interdisciplinary recognition and application (2020–2024). Looking forward, the stabilization of high citation counts suggests that smart grids and microgrids are no longer emerging fields but are consolidating as core areas of sustainable energy research, with significant implications for both academic inquiry and policy innovation.

An analysis of the top-cited publications reveals several dominant thematic directions in sustainable energy research, particularly around smart grid technologies, peer-to-peer (P2P) energy trading, and microgrid optimization (

Table 3. Top papers with the highest citations.

Title	First Authors	Source Title	Year	Total Citations	Average per Year
Prosumer integration in wholesale electricity markets: synergies of peer-to-peer trade residential storage	Zepter, Jan Martin	Energy and Buildings	2019	173	24.71
Microgrid control methods toward achieving sustainable energy management	Roslan, M. F.	Applied Energy	2019	94	13.43
A Sustainable Energy Management System for Isolated Microgrids	Solanki, Bharatkumar V.	IEEE Transactions on Sustainable Energy	2017	90	10
Power Quality Enhancement and Power Flow Analysis of a PV Integrated UPQC System in a Distribution Network	Ray, Pragnyashree	IEEE Transactions on Industry Applications	2022	69	17.25
Towards electric digital twin grid: Technology and framework review	Sifat, Md. Mhamud Hussen	Energy and AI	2023	68	17
Optimal Model for Local Energy Community Scheduling Considering Peer to Peer Electricity Transactions	Faia, Ricardo	IEEE Access	2021	67	13.4
A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids	Tajjour, Salwan	Sustainable Energy Technologies and Assessments	2023	53	17.67
LiPSG: Lightweight Privacy-Preserving Q-Learning-Based Energy Management for the IoT-Enabled Smart Grid	Wang, Zhuzhu	IEEE Internet of Things Journal	2020	48	8
Binary Particle Swarm optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving	Hannan, M. A	IEEE Access	2019	46	6.57
Household responsiveness to residential demand response strategies: Results and policy implications from a Swedish field study	Nilsson, Anders	Energy Policy	2018	45	5.63

). To capture the intellectual structure, the top 10 most-cited works are grouped into five thematic categories. This grouped analysis not only identifies citation leaders but illustrates the technological and policy shifts shaping the field.

Group 1. Peer-to-Peer Energy Trading and Community Energy Management

Zepter et al. (2019) introduced a pivotal study published in Energy and Buildings. With a total of 173 citations and an average of 24.71 citations per year, this article is the most cited among the selected works. It highlights the role of prosumers in electricity markets and emphasizes the synergies between peer-to-peer energy trading and residential storage systems [59]. Similarly, Faia et al. (2021), in IEEE Access, explored an optimal model for local energy community scheduling considering peer-to-peer electricity transactions, gathering 67 citations with a yearly average of 13.4 [60]. Both studies underscore the growing relevance of decentralized energy models, particularly those that empower consumers to become active participants in the energy market. Together, these studies demonstrate the rapid maturation of decentralized, consumer-centric energy models, which move beyond technical feasibility toward real-world pilot projects in Europe and Asia. They also indicate strong policy relevance, aligning with global discussions on energy democratization and market liberalization [61].

Group 2. Microgrid Control and Sustainable Energy Systems

Research on microgrids forms a significant body of the literature. Roslan et al. (2019), in Applied Energy, examined Microgrid control methods toward achieving sustainable energy management, receiving 94 citations and an average of 13.43 citations per year [62]. Solanki et al. (2017) added to this discourse with their study in IEEE Transactions on Sustainable Energy, presenting a sustainable energy management system for isolated microgrids. With 90 citations and a steady average of 10 citations per year, this work contributes to the operational efficiency and energy resilience of microgrids [63]. Tajjour and Chandel (2023) [64], in Sustainable Energy Technologies and Assessments, provided a comprehensive review of sustainable energy management systems tailored to solar microgrids. Despite its recent publication, the study has already amassed 53 citations, averaging 17.67 annually—an indication of its early impact in the field. This group illustrates both continuity and renewal: earlier works established operational efficiency and resilience, while recent contributions highlight the rapid adoption of renewable-based microgrids in emerging economies. The citation momentum of Tajjour et al. (2023) reflects the strategic urgency of solar microgrids in energy transitions [64].

Group 3. Technological Innovations and Digitalization in Energy Systems

Sifat et al. (2023) presented an innovative framework for the electric digital twin grid in their article, “Towards electric digital twin grid: Technology and framework review”, published in Energy and AI. With 68 citations and an average of 17 citations per year, the study reflects the current momentum of digital transformation in the energy sector [65]. Wang et al. (2020) [42], writing in the IEEE Internet of Things Journal, proposed LiPSG, a lightweight privacy-preserving Q-learning-based energy management system for smart grids, achieving 48 citations with an annual average of 8 citations per year. These contributions emphasize the intersection of artificial intelligence, privacy, and real-time management in smart energy systems. These works illustrate the digital turn in energy research, where artificial intelligence, digital twins, and data-driven management redefine system efficiency and transparency. The integration of privacy-preserving algorithms underscores a critical convergence between technical optimization and societal trust, an emerging dimension noted less frequently in earlier bibliometric studies [66].

Group 4. Optimization and Power Quality in Grid Integration

Ray et al. (2022) [67] focused on the power quality enhancement of photovoltaic (PV)-integrated unified power quality conditioner (UPQC) systems within distribution networks. Published in the IEEE Transactions on Industry Applications, the article has garnered 69 citations, averaging 17.25 per year [67]. Hannan et al. (2019) [68], in IEEE Access, presented a scheduling model for microgrid-integrated virtual power plants using binary particle swarm optimization. With 46 citations and 6.57 average annual citations, this study illustrates the application of heuristic optimization methods in achieving energy efficiency and grid stability. These works confirm optimization as the backbone of technical innovation in smart grids. While earlier optimization studies emphasized theoretical algorithm development, recent highly cited works reveal direct application to grid stability, storage integration, and power quality enhancement, signaling a higher degree of technological maturity [68].

Group 5. Consumer Behavior and Demand Response

Lastly, Nilsson et al. (2018) [69], in Energy Policy, addressed residential demand response strategies and their policy implications through a field study in Sweden. Although this article has fewer total citations (45) and a lower annual average (5.63), it provides essential insights into household energy consumption behavior and the social acceptance of demand-side management interventions [68]. This human-centric approach complements the predominantly technical nature of other studies in the dataset. Though less cited than the technical studies, this work highlights the human dimension of energy transitions—social acceptance, behavioral change, and policy support. This perspective complements the technological clusters and reflects a more holistic view of sustainable energy management [70].

The high citation impact and recency of these works suggest that the convergence of digitalization, sustainability, and community-based energy solutions is an evolving, high-priority research frontier. Compared to earlier bibliometric reviews that have emphasized optimization and control as isolated domains, the present analysis highlights an interdisciplinary shift where technology, markets, and consumer behavior increasingly intersect. This convergence underscores the field’s growing maturity and provides a forward-looking agenda for policymakers, system designers, and researchers.

5. Conclusions

This bibliometric study provides a comprehensive overview of the research landscape on sustainable energy and semiconductors by analyzing 136 English-language documents indexed in the Web of Science Core Collection. The findings highlight regional, thematic, and temporal patterns that reflect the evolution and future direction of the field. A substantial proportion of the publications originate from Asia, with India (19.85%), China (19.12%), Saudi Arabia (9.56%), and Malaysia (8.82%) identified as the leading contributors, alongside the United States, which also plays a significant role (7.35%). These trends underscore the strategic policy investments and industrial priorities that have accelerated research in clean energy technologies and semiconductor innovations across different regions.

The thematic clustering revealed four major research directions. The red cluster emphasizes the application of semiconductors in smart grids, energy management, and deep learning technologies, while the green cluster focuses on renewable energy systems, particularly solar and wind power, and optimization strategies to enhance system efficiency. The blue cluster highlights artificial intelligence and machine learning applications that improve predictive performance and operational resilience, whereas the yellow cluster introduces emerging topics, such as blockchain, IoT, energy resilience, and storage systems, which together signal a shift toward decentralized, intelligent energy ecosystems. An overlay visualization further illustrates a temporal progression of priorities, beginning with the integration of AI and smart grid foundations in 2020–2021, shifting toward renewable optimization and semiconductor applications in 2022–2023, and, most recently, focusing on resilience, electric vehicle integration, smart infrastructure, and digital twin technologies from 2024 onward. Collectively, these developments mark an ongoing transition from narrowly defined engineering applications toward holistic and intelligent energy systems aligned with global sustainability goals.

Despite its contributions, this study has several limitations. The exclusive reliance on the Web of Science Core Collection may omit relevant works indexed in other major databases, such as Scopus and IEEE Xplore. The keyword-driven search strategy, while systematic, may exclude interdisciplinary contributions or studies employing alternative terminology. The relatively small dataset of 136 documents constrains the representativeness of the results, and metadata inconsistencies, such as author names and affiliations, may affect the accuracy of network visualizations. Furthermore, the bibliometric approach is primarily quantitative and does not capture the theoretical depth, methodological rigor, or societal relevance of individual contributions.

Future research can address these limitations by expanding the datasets to include multiple indexing databases, applying qualitative content analysis to thematic clusters, and examining how semiconductor technologies intersect with adjacent sectors, such as healthcare, mobility, and smart manufacturing. Further studies should also investigate the influence of policy frameworks, funding ecosystems, and industrial collaborations on regional contributions, as well as employ advanced text-mining and machine learning techniques for forecasting emerging research frontiers. Integrating sustainability assessment metrics into bibliometric research would further enable the evaluation of environmental, economic, and social dimensions, ensuring that technological innovation aligns with the United Nations Sustainable Development Goals (SDGs). In sum, this study demonstrates that sustainable energy and semiconductor research is transitioning from fragmented, technology-specific inquiries toward integrated, interdisciplinary systems research. By identifying both the established and emerging directions, the findings provide valuable guidance for scholars, policymakers, and industry leaders in shaping future innovation pathways.