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Article

Smart Grids and Sustainability: The Impact of Digital Technologies on the Energy Transition

by
Paola Campana
*,
Riccardo Censi
,
Roberto Ruggieri
and
Carlo Amendola
Department of Management, Sapienza University of Rome, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Energies 2025, 18(9), 2149; https://doi.org/10.3390/en18092149
Submission received: 13 March 2025 / Revised: 16 April 2025 / Accepted: 18 April 2025 / Published: 22 April 2025
(This article belongs to the Special Issue The Future of Energy Systems: Integration of Energy Technologies in Distribution Grids)
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Abstract

:
Smart Grids (SG) represent a key element in the energy transition, facilitating the integration of renewable and conventional energy sources through the use of advanced digital technologies. This study analyzes the main research trends related to SG, energy efficiency, and the role of Artificial Intelligence (AI) and the Internet of Things (IoT) in smart energy management. Following the PRISMA protocol, 179 relevant academic articles indexed in the Scopus database were selected and analyzed using VOSviewer software, version 1.6.20, to identify the main thematic clusters. The results reveal a converging research focus on energy flow optimization, renewable energy integration, and the adoption of digital technologies—including cybersecurity solutions—to ensure grid efficiency, security, and resilience. The study confirms that digitalization acts as a key enabler for building a more sustainable and reliable energy system, aligned with the objectives of the European Union and the United Nations 2030 Agenda. The contribution of this work lies in its integrated approach to the analysis of digital technologies, linking the themes of efficiency, resilience, and infrastructure security, in order to provide valuable insights for future research and sustainable energy policy development.

1. Introduction

Energy is a fundamental element for economic and social development, influencing both quality of life and the competitiveness of modern economies [1,2]. In recent decades, the increasing energy demand, coupled with the need to reduce greenhouse gas emissions and the growing focus on sustainability, has made the transition toward more efficient and resilient production and consumption models imperative [1,3,4]. This transformation is driven by technological innovation and the implementation of energy policies aimed at decarbonization. The rising energy demand and the urgent need to mitigate greenhouse gas emissions necessitate a radical transformation of energy infrastructures [5]. In this context, SGs represents a pivotal evolution, enabling the optimization of energy production, distribution, and consumption through the deployment of digital technologies and advanced management systems [6,7].
Figure 1 presents the reductions in greenhouse gas emissions compared to 1990 levels within the European Union for the period 2019–2023 [8].
Furthermore, energy efficiency has become a central pillar in this evolution, not only to minimize consumption but also to ensure the optimization of resources throughout the energy value chain. Technologies such as predictive analytics, real-time data processing, and decentralized control systems contribute to the reduction of transmission losses and enable more sustainable grid operation [6,7,8]. The integration of renewable sources, such as wind and solar energy, with traditional energy sources presents a complex challenge that requires innovative solutions to ensure the balance between energy supply and demand [9]. In 2022, renewable energy accounted for 23% of the European Union′s total energy consumption [10]. By 2024, renewable energy production surpassed fossil fuel-based energy in terms of contribution [11]. Solar energy generated 11% of the EU’s electricity, exceeding coal for the first time, which contributed 10% [12]. Table 1 presents the contribution percentages of different energy sources to the EU’s electricity consumption in 2024.
A key component of this transformation is the adoption of Artificial Intelligence (AI) and the Internet of Things (IoT), which facilitate real-time monitoring, predictive maintenance, and adaptive demand response strategies [5]. These technologies empower the grid to become more autonomous, intelligent, and capable of self-regulation, aligning supply with fluctuating demand while improving overall system reliability [6].
Cybersecurity also plays a decisive role in this digital transition. As energy infrastructures become increasingly connected and reliant on digital systems, they are exposed to higher risks of cyber threats. Ensuring the effectiveness of cybersecurity frameworks is essential to protect critical assets and guarantee the continuity and safety of energy supply. Measures such as encryption protocols, intrusion detection systems, and AI-based threat analysis are becoming integral components of smart energy systems.
One aspect of SGs is the reduction of energy waste through the use of monitoring tools, predictive analytics, and automation [13]. The application of AI, IoT, and blockchain enhances system efficiency by minimizing energy transmission and distribution losses [13]. In 2023, emissions related to energy generation in the EU decreased by 18%, primarily due to increased renewable energy production and the adoption of advanced energy management technologies [14]. The European Union has established a clear regulatory framework to support the energy transition and emission reduction efforts [15].
The SDGs serve as a strategic guide to addressing these challenges, with a particular focus on SDG 7 (Affordable and Clean Energy), which promotes equitable access to modern, reliable, and sustainable energy services [16], SDG 9 (Industry, Innovation, and Infrastructure), which encourages the development of resilient infrastructure and the adoption of innovative technologies [17], and SDG 13 (Climate Action), which emphasizes the urgency of measures to combat climate change, an objective pursued by the EU through the European Green Deal and Fit for 55, aiming to reduce emissions by 55% by 2030 [18]. The three analyzed SDGs are illustrated in Figure 2.
This manuscript is structured to guide the reader through a clear progression of content. It includes a literature-based Introduction, a detailed Methodology section grounded in PRISMA and bibliometric analysis, followed by the Results and Discussion sections, which present and interpret the findings, and concludes with a summary of key insights and study limitations. This structure is designed to ensure methodological transparency and thematic coherence across all sections of the paper.
This study aims to analyze European distribution networks in relation to the SDGs and EU energy plans, highlighting the role of digital technologies in smart energy management.

2. Methodology

The methodology adopted in this study consists of two main phases: systematic literature selection and bibliometric analysis.
In the first phase, a PRISMA-based approach was employed to identify and filter the most relevant academic literature on SG, the integration of renewable energy sources, and the role of digital technologies in energy management [19]. The selection was conducted using the Scopus database, applying rigorous inclusion and exclusion criteria to ensure the relevance and scientific quality of the analyzed documents [20].
The Scopus database has been used as a source of articles for review [21].
Scopus is among the most comprehensive information resources globally, covering many disciplines and providing scholars with high-quality and reliable academic information, and has gradually become the main source of data for bibliometric analysis and systematic literature review [22].
In the second phase, a bibliometric analysis was performed using VOSviewer software to map the key research trends in the field [23]. The integration of these two phases, along with the analysis of the 20 most cited articles, provided a systematic and updated overview of research dynamics related to SGs and the energy transition.

2.1. Systematic Literature Selection and Inclusion Criteria

To ensure a systematic and reproducible analysis of the scientific literature on SGs and the integration of energy sources, an approach based on the principles of the PRISMA methodology was adopted [19]. The research was conducted using the Scopus database with an advanced search string (“smart grid” OR “intelligent grid” OR “smart energy network”) AND (“renewable energy” OR “solar energy” OR “wind energy” OR “hydropower” OR “non-renewable energy”) AND (“energy efficiency” OR “energy waste reduction” OR “grid optimization”) AND (“digital technology” OR “artificial intelligence” OR “IoT” OR “blockchain” OR “big data”).
This string was carefully designed to include the most relevant synonyms and semantically related keywords commonly used in the literature on smart grids and digital energy technologies. The use of Boolean operators (OR, AND) allowed for the inclusion of a wide range of terms while maintaining a coherent and targeted scope. For this reason, a single, comprehensive string was considered sufficient to capture the breadth of existing studies in the field.
The initial query returned a total of 256 documents. To ensure temporal relevance, a publication date filter was applied (2015–2025), reducing the set to 249 documents. Only peer-reviewed scientific articles and conference papers were retained, leading to a refined set of 188 documents [24]. Subsequently, documents were filtered based on disciplinary relevance, focusing on areas such as engineering, computer science, energy, decision sciences, environmental science, and materials science. Studies outside these domains—particularly those focused on purely sociological, economic, or philosophical frameworks—were excluded as they fell outside the technological and infrastructural scope of the present research. The final corpus consisted of 179 documents considered suitable for analysis.
The selection process is illustrated in the PRISMA flow diagram in Figure 3. The diagram follows the standard four-phase structure of PRISMA—Identification, Screening, Eligibility, and Inclusion—providing a transparent overview of how the dataset was progressively refined. In the Identification phase, documents were retrieved using a clearly defined Boolean search string. In the Screening phase, non-peer-reviewed content was excluded. The Eligibility step involved manual screening based on relevance to the research fields. Finally, in the Inclusion phase, only articles meeting all methodological and thematic criteria were selected for analysis [25,26].
This stepwise filtering process ensured methodological rigor and reproducibility, aligning with the best practices of systematic review protocols. It also guaranteed that the final dataset was both current and aligned with the research objective, enabling a robust bibliometric and thematic analysis.

2.2. Bibliometric Analysis

From a bibliometric perspective, VOSviewer is a software widely used in bibliometric analysis for the visualization and analysis of bibliographies and datasets containing bibliographic information, such as title, author, and keywords [27,28].
Thanks to its versatility, it allows for the identification of trends, impacts, and thematic evolutions through the analysis of citation recurrence [29]. In the landscape of scientific research, VOSviewer establishes itself as an essential tool for the representation of bibliometric data, facilitating the identification of research opportunities in specific sectors and the recognition of the most frequently cited sources [30,31].
The software focuses on analysis at an aggregated level, particularly in the field of cluster analysis [30]. It has been employed to examine and represent connections between keywords, applying the VOS clustering method to the frequency of occurrences and assigning a distinctive color to each group [32].
The interpretation of this methodology assumes that the circle sizes reflect the frequency of keyword usage, while the colors represent the different clusters [33]. It is important to note that the x and y axes do not have a specific meaning; therefore, the generated maps can be freely rotated or flipped without altering their informational content [34].
The analyzed data were extracted from Scopus in CSV format to ensure compatibility with VOSviewer. Subsequently, a map based on bibliographic results was created using both author keywords and indexed keywords. To ensure a meaningful representation, a filter was applied, considering only keywords with a minimum of three occurrences.
This methodology made it possible to highlight the most relevant concepts in the field of study, providing a clear and detailed vision of emerging terms in the scientific literature.

3. Results

The bibliometric analysis conducted using VOSviewer generated the visualization shown in Figure 4.
In the VOSviewer visualization, concepts and keyword connections are organized into two main clusters, distinguishable by color.
The red cluster, located on the left and central part of the map, is strongly focused on energy management and SGs. The main keywords in this group include smart power grids, energy management, renewable energy, and IoT, indicating a focus on energy transition, renewable energy integration, and the role of digitalization in optimizing energy flows. This cluster also highlights the link between real-time monitoring and energy efficiency, suggesting the increasing use of technologies for dynamic network control.
On the other hand, the green cluster, positioned on the right side of the map, is more focused on optimization, security, and advanced technologies for energy management. Concepts such as optimization, digital twin, network security, and artificial intelligence emerge here, reflecting a growing emphasis on AI-based tools and digital simulation to enhance grid resilience. The importance of cybersecurity is evident, with network security directly linked to energy management, demonstrating the crucial role of protecting digitalized infrastructures.
Visually, the red cluster is denser and more concentrated in the center-left, with many internal connections between renewable energy management concepts and system efficiency. The green cluster, more distributed on the right side, highlights a research trend oriented towards digital simulation, optimization, and the security of modern power grids.
The interconnection between the two clusters suggests that digitalization and smart grid optimization are key elements for enhancing the efficiency and sustainability of renewable energy integration in the global power system.
The bibliographic analysis then examined the 20 most cited articles, shown in Table 2, which revealed the state-of-the-art of the research topic.
The analysis of the 20 most cited articles in the field of digitalization of energy grids and the transition towards sustainable models highlights the central role of technologies in the management of energy resources and the optimization of renewable energy integration.
Ali and Choi [37] illustrate how AI has become a key component for SG, improving the prediction of demand and supply, supporting the distributed management of energy resources, and enhancing cybersecurity mechanisms. In addition, recent developments in reinforcement learning applied to grid-following converters show promising results in improving system adaptability and stability. For instance, Zeng et al. (2025) [55] proposed a multi-objective controller using Easy Transfer Reinforcement Learning, which significantly reduces training effort while maintaining control performance and robustness across varying grid conditions [55]. The Internet of Energy (IoE), explored by Strielkowski et al. [45], represents an emerging paradigm that enables peer-to-peer energy exchange, the optimization of electric vehicle charging, and a more efficient distribution of renewable sources, favoring the decentralization of energy markets. On the data processing front, the study by Minh et al. [44] highlights how Edge Computing improves the operational efficiency of SG, reducing latency in monitoring systems and allowing for more flexible management of storage resources.
To address the variability of renewable sources, Meenal et al. [46] demonstrate that the use of predictive models based on machine learning and deep learning helps reduce uncertainties related to intermittent generation, optimizing network planning and management. Building on this trend, recent approaches have combined deep reinforcement learning with physics-informed models to improve the control of power converters in smart grids. For instance, Zeng et al. (2024) [56] developed a method for optimizing Input-Series Output-Parallel Dual Active Bridge (ISOP-DAB) converters, enhancing energy transfer efficiency and system stability in complex decentralized energy networks [56].
In parallel, the adoption of IoT and big data technologies in smart cities, described by Bibri and Krogstie [42], reveals the importance of integrating smart meters, smart buildings, and SGs to improve energy efficiency and reduce environmental impact. The evolution of SGs also intersects with the issue of sustainability and infrastructure security: according to Kumari et al. [38], the combination of blockchain and AI can enhance microgrid management, ensuring greater transparency in energy markets and increasing the resilience of electrical networks.
Finally, the importance of weather forecasting for improving the reliability of renewable energy networks is emphasized by Meenal et al. [46], who demonstrate how advanced predictive algorithms can mitigate the effects of the intermittency of solar and wind generation. The analysis of these studies highlights that the energy transition and the digitalization of networks are closely interconnected processes, where the adoption of solutions based on AI, IoT, Edge Computing, and blockchain represents the key to ensuring more resilient, efficient, and sustainable electrical grids. Compared to existing literature, this study stands out for its integrated approach, which jointly examines technological trajectories related to energy efficiency, digitalization, and infrastructure security. The combination of bibliometric analysis and thematic cluster interpretation makes it possible to highlight not only the areas of highest scientific intensity, but also the emerging interconnections between advanced management tools and network protection strategies. In particular, the emphasis on the relationship between AI, IoT, blockchain, and cybersecurity represents an original contribution aimed at promoting a systemic vision for the development of smart grids.

4. Discussion and Conclusions

The analysis conducted in this study has highlighted how SGs represent a key element for the energy transition, facilitating the integration of renewable sources with traditional energy systems [1,2]. The combination of advanced technologies allows for a significant improvement in the efficiency of energy production, distribution, and consumption [37,38]. The adoption of these digital solutions not only optimizes the use of resources but also enables the reduction of greenhouse gas emissions, contributing to the sustainability goals defined by the European Union and the SDGs of the United Nations [15].
The bibliometric analysis has identified two main research streams: the first, centered on energy management and SG, highlights the crucial role of digitalization in optimizing energy flows [40]; the second, focused on optimization, security, and advanced technologies, demonstrates how the integration of digital simulation tools and cybersecurity is essential to ensuring the resilience of energy infrastructures [48].
The dimension of energy efficiency has emerged as a key priority, not only from a technological standpoint but also as a strategic goal for policy and market design. The use of AI-driven forecasting models, combined with real-time IoT-based monitoring systems, enables smart grids to dynamically adapt to load variations, prevent energy waste, and reduce peak loads [44,57].
Moreover, the discussions on Artificial Intelligence (AI) and the Internet of Things (IoT) should be reinforced by recognizing their foundational role in automating energy management, improving decision-making, and creating self-healing networks. AI supports advanced forecasting, predictive maintenance, and optimization algorithms, while IoT ensures real-time communication between grid components, sensors, and user devices, forming a synergistic digital ecosystem for energy resilience [38,40,55].
Cybersecurity emerges as another cornerstone of smart grid effectiveness. The increasing reliance on connected devices and cloud platforms exposes the grid to new vulnerabilities. Therefore, the development and implementation of robust cybersecurity frameworks—capable of threat detection, anomaly resolution, and system recovery—are essential to ensure the integrity, availability, and confidentiality of energy data and services. Recent advances in AI-based cybersecurity are promising, offering adaptive responses to emerging threats and contributing to overall grid stability [45,56].
The growing interconnection between these two areas suggests that digitalization and network protection are complementary and essential aspects for a sustainable energy system. The results emerging from the literature review show that AI-based solutions are among the most studied and implemented, thanks to their ability to improve demand and supply forecasting, optimize the distributed management of resources, and strengthen network security [46].
Furthermore, blockchain is emerging as a promising option to ensure transparency and reliability in decentralized energy markets, supporting the development of peer-to-peer energy exchange models [38]. At the same time, Edge Computing offers new opportunities to reduce latency in monitoring and control operations of SG, improving the operational efficiency of the system [42]. A summary is given in Table 3.
A fundamental aspect emerging from our analysis concerns the importance of weather forecasting in managing renewable energy sources [46]. The use of advanced predictive algorithms based on machine learning and deep learning helps mitigate uncertainties related to the variability of solar and wind energy, improving grid planning and balance [48]. This approach fits into a broader dynamic energy management framework, where the system’s adaptability becomes increasingly crucial.
While this study highlights the growing convergence between energy efficiency, renewable integration, and digital technologies in the evolution of SG, several challenges still remain, requiring further technological and regulatory developments. Cybersecurity of SGs represents one of the main critical issues, considering the growing risk of cyberattacks on digitalized energy infrastructures [56]. Scalability and interoperability of different digital technologies require greater standardization to ensure a smooth and effective transition [43]. Moreover, the economic sustainability of the proposed solutions must be carefully evaluated to prevent the high implementation costs from limiting the large-scale adoption of innovative technologies [41,57].
This study highlights how the digitalization of energy grids serves as a fundamental lever for the transition toward a more efficient, resilient, and sustainable energy system. The integration of AI, IoT, blockchain, and Edge Computing not only enhances resource management but also opens new perspectives for the creation of decentralized and more equitable energy models.

5. Limitations of the Study

While this study offers a comprehensive bibliometric and thematic analysis of the role of digital technologies in smart grid development and the energy transition, several limitations should be acknowledged. First, the literature review is based solely on documents indexed in the Scopus database. Although Scopus is a widely recognized and reliable academic source, this choice may have led to the exclusion of relevant studies published in other databases such as Web of Science or IEEE Xplore. Second, the selection criteria focused primarily on technical and scientific disciplines (e.g., engineering, energy, computer science), potentially underrepresenting interdisciplinary perspectives from social sciences or public policy, which are crucial for understanding the broader implications of smart grid implementation.
Moreover, the study provides a static snapshot of the current research landscape, which may not fully reflect the fast-paced evolution of technologies such as AI, IoT, and blockchain. Emerging innovations and newly published research might not yet be adequately represented. Another limitation lies in the lack of empirical validation: while the study maps conceptual trends and technological trajectories, it does not assess how these solutions are applied in real-world energy infrastructures or their actual impact on grid performance, resilience, and sustainability.
Future research should integrate empirical case studies, cross-disciplinary frameworks, and longitudinal data to enrich the understanding of how digital technologies interact with regulatory, social, and economic contexts in shaping the energy transition.
Future research should integrate empirical case studies and longitudinal data to enrich the understanding of how digital technologies interact with regulatory, social, and economic contexts in shaping the energy transition.

Author Contributions

Conceptualization, R.C. and P.C.; methodology, R.C., R.R., C.A. and P.C.; software, R.C., R.R. and C.A.; validation, R.C., R.R., C.A. and P.C.; formal analysis, R.C.; resources, R.C.; data curation, R.C.; writing—original draft preparation, R.C.; writing—review and editing, R.C., R.R., C.A. and P.C.; visualization, R.R., C.A. and P.C.; supervision, P.C.; project administration, P.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

AIArtificial Intelligence
IoTInternet of Things
SDGsSustainable Development Goals
SGsSmart Grids

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Figure 1. Reduction in greenhouse gas emissions in the European Union (2019–2023) in million tons of CO2 equivalent. Source: [8].
Figure 1. Reduction in greenhouse gas emissions in the European Union (2019–2023) in million tons of CO2 equivalent. Source: [8].
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Figure 2. SDGs 7, 9, and 13: Drivers of Energy Transition and Innovation. Our elaboration.
Figure 2. SDGs 7, 9, and 13: Drivers of Energy Transition and Innovation. Our elaboration.
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Figure 3. PRISMA flow diagram for document selection. The diagram summarizes the four phases (Identification, Screening, Eligibility, and Inclusion) and shows the number of records excluded at each step. Our elaboration.
Figure 3. PRISMA flow diagram for document selection. The diagram summarizes the four phases (Identification, Screening, Eligibility, and Inclusion) and shows the number of records excluded at each step. Our elaboration.
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Figure 4. Bibliometric keyword analysis of articles and conference papers. Our elaboration.
Figure 4. Bibliometric keyword analysis of articles and conference papers. Our elaboration.
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Table 1. Contribution of energy sources to electricity consumption in the EU (2024). Source: [8].
Table 1. Contribution of energy sources to electricity consumption in the EU (2024). Source: [8].
Energy SourceContribution (%)
Nuclear energy24%
Wind energy18%
Natural gas16%
Solar energy11%
Coal10%
Hydroelectric8%
Biomass7%
Imports and other renewables6%
Table 2. State-of-the-art analysis. Our elaboration.
Table 2. State-of-the-art analysis. Our elaboration.
TitleAuthorYearCiteMain FocusFuture Recommendation
Designing microgrid energy markets: A case study: The Brooklyn Microgrid[35]20181265This study focuses on the design and evaluation of a blockchain-based microgrid energy market, using the Brooklyn Microgrid as a case study. It introduces a framework of seven key components for efficient market operation, highlighting the economic, environmental, and social benefits of local energy generation and peer-to-peer trading.Future work should explore more efficient market mechanisms, implement advanced bidding strategies, and address regulatory barriers to commercial peer-to-peer energy markets. Socio-economic research is recommended to better align market design with user preferences and community values.
Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions[36]2020227This paper presents a comprehensive review of artificial intelligence (AI) techniques applied to stability analysis and control in smart grids. It outlines AI methodologies such as machine learning, deep learning, and reinforcement learning, and evaluates their applications in areas including security assessment, fault diagnosis, and various control strategies, highlighting their advantages over traditional methods.Future research should focus on addressing challenges such as data quality, imbalanced learning, AI interpretability, transfer learning difficulties, and robustness to communication issues or cyber-attacks. Emphasis is also placed on integrating model- and data-driven approaches and developing adaptive, explainable, and scalable AI systems suitable for real-world smart grid operations.
State-of-the-art artificial intelligence techniques for distributed smart grids: A review[37]2020202This review explores the application of cutting-edge artificial intelligence (AI) techniques in distributed smart grids. It covers AI-enabled solutions for integrating renewable energy sources, managing energy storage systems, enabling demand response, securing grid operations, and supporting home energy management. The paper emphasizes how AI can enhance automation, efficiency, and adaptability in future energy systems.Future work should focus on developing self-learning, fully automated, and self-healing smart grid systems. There is a need for scalable AI algorithms, advanced cybersecurity protocols, improved user-centric designs, and plug-and-play capabilities. Further, a skilled workforce and better integration of AI with regulatory and market frameworks are critical for real-world deployment.
Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions[38]2020135The paper provides a comprehensive survey on the integration of blockchain and artificial intelligence for secure and efficient energy cloud management (ECM). It proposes a decentralized AI-based ECM architecture that enables energy load prediction, secure data transmission, and peer-to-peer energy trading, while addressing security, privacy, and scalability challenges in smart grids.Future research should address secure data analytics, enhance privacy in public blockchains such as Ethereum, protect against 51% attacks, and carefully develop bug-free smart contracts. There is also a need for standardized frameworks, affordable blockchain data storage solutions, and broader professional expertise to enable scalable and real-world deployment of AI–blockchain ECM systems.
Optimized Day-Ahead Pricing with Renewable Energy Demand-Side Management for Smart Grids[39]2017130This study proposes a distributed, day-ahead dynamic pricing framework for smart grids that incorporates renewable energy generation and storage at the user level. It models the interaction between electric companies and users as a convex optimization problem, aiming to maximize social welfare. The framework enables privacy-preserving energy trading while balancing grid load and incentivizing carbon emissions reduction through a dynamic buyback pricing strategy.Future research should explore the integration of additional real-world factors such as uncertainty in renewable generation, user behavior variability, and communication delays. There is also potential in enhancing the scalability of the distributed algorithms and incorporating real-time adaptability into the pricing scheme to better support large-scale deployment in smart grid environments.
Privacy-Preserving Transactive Energy Management for IoT-Aided Smart Homes via Blockchain[40]2021109This paper proposes a blockchain-based transactive energy management (TEM) system for IoT-enabled smart homes. It enables both vertical (with the grid) and horizontal (peer-to-peer) energy transactions while preserving user privacy. The authors develop a distributed optimization algorithm using smart contracts on a lightweight IoT blockchain, demonstrating improved efficiency, privacy, and feasibility on real IoT devices.Future work should aim to reduce the computational complexity of decentralized algorithms to support large-scale IoT deployments. The authors also suggest building broader test networks and exploring efficient smart contract execution methods, such as WebAssembly and predefined contracts, to scale and optimize blockchain-based TEM systems.
Developing Novel 5th Generation District Energy Networks[41]2020109This paper presents a novel concept and design methodology for 5th generation (5G) district energy networks that integrate thermal, power, and mobility sectors. Using a case study from the London Borough of Islington, the authors demonstrate how ultra-low temperature “ambient loop” systems can leverage local renewable and secondary energy sources (e.g., waste heat from data centers and subways), heat pumps, energy storage, and smart control systems to deliver efficient, low-carbon energy to urban communities.Future research should refine modeling tools for dynamic thermal behavior, optimize ambient loop temperatures, and improve integration of smart control systems. Further exploration is also needed on real-time balancing of heat and cooling demands, stakeholder engagement models, and regulatory adjustments to enable large-scale deployment of 5G smart energy networks.
Environmentally Data-driven Smart Sustainable Cities: Applied Innovative Solutions for Energy Efficiency, Pollution Reduction, and Urban Metabolism[42]2020100This paper investigates the role of IoT and big data technologies in enhancing environmental sustainability within the framework of smart and sustainable cities. Using case studies of Stockholm and Barcelona, it explores how data-driven solutions—such as smart grids, smart buildings, and urban metabolism monitoring—can reduce pollution, improve energy efficiency, and support real-time urban management.Future work should aim at advancing integration between data-driven technologies and environmental strategies, improving urban analytics platforms, and addressing city-specific needs through tailored solutions. Research should also focus on scalable infrastructures and interoperable systems to support continuous environmental monitoring and adaptive urban planning.
Green Industrial Internet of Things from a Smart Industry Perspective[43]202095This paper explores the evolution of the Green Industrial Internet of Things (GIIoT) as a key enabler of Industry 4.0, Society 5.0, and MIC2025 strategies. It reviews the technological foundations, protocols, and applications of GIIoT across sectors including energy, manufacturing, agriculture, healthcare, and transportation. The study emphasizes how smart technologies—such as IoT, AI, cobots, and digital twins—enhance efficiency, sustainability, and automation in modern industrial ecosystems.Future work should address challenges in device interoperability, energy efficiency, communication reliability in industrial environments, and cybersecurity. It is recommended to develop robust, scalable, and secure GIIoT architectures tailored to real-time applications. Further integration of AI, machine learning, and decentralized systems (e.g., blockchain) is also crucial for enabling predictive analytics and autonomous industrial processes.
Edge Computing for IoT-Enabled Smart Grid: The Future of Energy[44]202290This paper presents a comprehensive survey on integrating edge computing with IoT-enabled smart grids (SGs). It proposes a layered architecture and an all-in-one computing model aimed at reducing latency and improving real-time decision-making in SG systems. Applications span power distribution, micro-grids, and advanced metering, emphasizing benefits such as faster service response, efficient energy management, and scalability across domains such as smart cities and factories.Future research should address challenges such as scalability, system sustainability, flexible resource allocation, and cybersecurity for edge-enabled SGs. Emphasis is placed on building resilient infrastructures, adopting lightweight security protocols, and integrating advanced technologies such as AI, blockchain, and homomorphic encryption to support real-time, secure, and sustainable smart grid operations.
Intelligent Power Equipment Management Based on Distributed Context-aware Inference in Smart Cities[45]201883This paper proposes a context-aware framework for intelligent power equipment management in smart cities. It introduces a distributed inference architecture that utilizes IoT sensor data, context ontologies, and semantic reasoning to monitor and manage power equipment efficiently. The system enhances decision-making by converting low-level data into high-level context, enabling real-time, automated, and intelligent control of smart grid infrastructure.Future work should explore decentralized decision-making in smart grids, including the handling of fuzzy and uncertain data. Additional research is needed to address privacy and cybersecurity challenges in distributed intelligent control systems, and to improve the scalability and reliability of semantic reasoning frameworks across diverse smart city applications.
Weather Forecasting for Renewable Energy System: A Review[46]202277This paper reviews forecasting methods for integrating solar and wind energy into smart grids, emphasizing the need for accurate weather prediction to manage the variability of renewable resources. It covers physical, statistical, artificial intelligence (AI), machine learning, and deep learning models, highlighting hybrid and ensemble methods as effective tools for improving forecasting accuracy.Future work should focus on enhancing forecast accuracy through hybrid deep learning models (e.g., CNN–LSTM), increasing data availability and resolution, and integrating AI-driven forecasting into real-time smart grid operations. There is also a need to develop lightweight, scalable models that maintain high accuracy while reducing computational complexity and training time.
Investigation and Analysis of Effective Approaches, Opportunities, Bottlenecks and Future Potential Capabilities for Digitalization of Energy Systems and Sustainable Development Goals[47]202274This paper analyzes the role of digitalization in transforming energy systems to meet Sustainable Development Goals (SDGs), particularly under the impact of the COVID-19 crisis. It explores how technologies such as blockchain, IoT, smart grids, and Industry 4.0 can improve energy efficiency, resilience, and integration of renewables. The study presents policy strategies and short-, mid-, and long-term planning for achieving net-zero emissions and enhancing environmental sustainability.Future efforts should focus on overcoming bottlenecks in renewable energy adoption, such as economic, social, and regulatory barriers. Emphasis is placed on enhancing public awareness, developing skilled workforces, and modernizing infrastructures. The integration of digital tools for decentralized energy systems and real-time management is key to accelerating the energy transition and achieving global climate targets.
A Blockchain-enabled Secure Power Trading Mechanism for Smart Grid Employing Wireless Networks[48]202062This paper proposes a secure and decentralized power trading mechanism for smart grids that integrates blockchain with wireless networks. It introduces a dual-chain structure—Local Energy Trading Blockchain (LETB) and Renewable Energy Trading Blockchain (RETB)—to facilitate efficient, secure, and trustable energy transactions. Smart contracts manage trading decisions, optimize distribution based on reputation and pricing, and incentivize renewable energy producers through automated feedback mechanisms.Future work should focus on integrating electric vehicle energy trading into the system and expanding the platform to support a wider range of renewable energy sources. Further research is also recommended on improving blockchain scalability, reducing transaction overhead, and enhancing system adaptability to diverse energy market scenarios.
Internet of Energy (IoE) and High-renewables Electricity System Market Design[49]201962This paper explores the integration of the Internet of Energy (IoE) into future high-renewables electricity market design. It analyzes how IoE technologies—including smart grids, smart meters, electric vehicles, and peer-to-peer trading—can support decentralized, flexible, and intelligent energy systems. The study highlights the benefits of IoE in enhancing energy efficiency, real-time coordination, and sustainability through two-way energy and data flows.Future efforts should focus on fostering social acceptance of IoE technologies, addressing technical challenges such as interoperability and cybersecurity, and incentivizing early adoption through targeted government policies. Additionally, flexible planning frameworks and dynamic market designs are needed to manage demand uncertainty, support prosumers, and scale up smart grid infrastructure in line with decarbonization goals.
Green Energy Aware and Cluster Based Communication for Future Load Prediction in IoT[50]202249This paper introduces a green energy-aware clustering and load prediction method (GEQCC-FLP) for IoT networks. It combines a satin bowerbird optimizer (SBO) for energy-efficient cluster head selection with a deep random vector functional link network (DRVFLN) for future load forecasting. The approach aims to optimize energy consumption, communication delay, and network lifetime in energy-constrained IoT environments, proving effective through extensive simulations.Future work should focus on integrating the proposed GEQCC-FLP technique with real-world IoT infrastructure and exploring further optimization of the model for large-scale, heterogeneous networks. Additionally, addressing security, data privacy, and interoperability issues will be crucial for practical deployment in smart grid and smart city applications.
Design and implementation of an AI-based and IoT-enabled Home Energy Management System: A case study in Benguerir—Morocco[51]202146This paper presents the design and implementation of an AI-based and IoT-enabled Home Energy Management System (HEMS), deployed in a smart testbed house in Morocco. The system integrates supply-side and demand-side management using real-time pricing, user preferences, and forecasting data to optimize energy flows. A hybrid control strategy—combining rule-based and PSO-based optimization—enhances energy efficiency, cost savings, and comfort while promoting renewable energy self-consumption.Future efforts should address the scalability of the HEMS framework, reduce implementation costs, and improve forecasting under uncertainty. Additional research is recommended on enhancing interoperability, user comfort modeling, and adapting pricing models to local contexts. Expanding dynamic pricing and integrating energy storage and electric vehicles could further boost system adoption and effectiveness in real-world settings.
Blockchain-based Decentralized Energy Intra-trading with Battery Storage Flexibility in a Community Microgrid System[52]202245This paper presents a blockchain-enabled peer-to-peer (P2P) energy intra-trading system integrated with a community microgrid. It leverages smart contracts and a decentralized energy market to optimize local energy transactions among prosumers, consumers, and renewable energy source owners. The system incorporates battery energy storage and dynamic pricing strategies using cryptocurrency (Cosmos ATOM), aiming to improve energy self-sufficiency, cost efficiency, and transparency in energy exchanges.Future research should explore the scalability of the proposed blockchain-based framework in larger microgrid networks and investigate its integration with electric vehicles and broader renewable sources. Further development of incentive mechanisms, demand-side management models, and more efficient smart contract deployment methods will enhance real-world applicability and user engagement.
Network Selection Criterion for Ubiquitous Communication Provisioning in Smart Cities for Smart Energy System[53]201945This paper proposes a blockchain-based peer-to-peer (P2P) energy intra-trading mechanism within a community microgrid integrated with battery storage systems. The framework enables decentralized energy exchange among prosumers, consumers, and renewable energy owners using smart contracts and cryptocurrency (Cosmos ATOM). The model considers supply-demand dynamics and price incentives to optimize energy distribution and enhance local self-sufficiency while reducing grid dependency and energy costs.Future research should explore large-scale deployment of the proposed system, integration with electric vehicles and other flexible loads, and refinement of smart contract mechanisms for greater efficiency and scalability. Enhancing the economic models, improving user participation incentives, and addressing regulatory and cybersecurity challenges will also be critical for real-world adoption.
Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources[54]202243This paper introduces a demand-side management (DSM) framework for smart homes using a hybrid optimization algorithm (ACTLBO) that combines ant colony optimization and teaching learning-based optimization. The proposed system incorporates renewable energy sources (RESs), battery storage systems (BSS), and user comfort metrics (thermal, visual, air quality) to minimize energy cost, carbon emissions, peak-to-average ratio (PAR), and appliance delay. Future research should focus on real-world deployment of the proposed ACTLBO-based scheduling framework and its integration with large-scale residential energy systems. Additional work is needed to refine the balance between user comfort and optimization objectives, improve resilience to uncertain renewable supply, and ensure compatibility with evolving real-time pricing models and smart grid standards.
Table 3. Summary of constructs. Our elaboration.
Table 3. Summary of constructs. Our elaboration.
ConstructItemsReferences
Smart GridsDigitalization of networks, smart energy management, and waste reduction.[37]
Energy EfficiencyOptimization of demand and supply and reduction of transmission losses.[44]
Digital TechnologiesAI, IoT, Blockchain, and Edge Computing for energy management.[38]
CybersecurityProtection of digital infrastructures and cybersecurity for SGs.[56]
Renewable EnergyIntegration of solar, wind, and hydropower into SGs.[45,57]
Blockchain and EnergyDecentralized energy exchanges and peer-to-peer transactions.[40]
Demand ForecastingAI and machine learning models for resource management optimization.[46]
Grid OptimizationDigital Twin, simulation models, and dynamic management of energy sources.[41]
Energy PoliciesFit for 55, European Green Deal, SDG 7, and SDG 13.[42]
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Campana, P.; Censi, R.; Ruggieri, R.; Amendola, C. Smart Grids and Sustainability: The Impact of Digital Technologies on the Energy Transition. Energies 2025, 18, 2149. https://doi.org/10.3390/en18092149

AMA Style

Campana P, Censi R, Ruggieri R, Amendola C. Smart Grids and Sustainability: The Impact of Digital Technologies on the Energy Transition. Energies. 2025; 18(9):2149. https://doi.org/10.3390/en18092149

Chicago/Turabian Style

Campana, Paola, Riccardo Censi, Roberto Ruggieri, and Carlo Amendola. 2025. "Smart Grids and Sustainability: The Impact of Digital Technologies on the Energy Transition" Energies 18, no. 9: 2149. https://doi.org/10.3390/en18092149

APA Style

Campana, P., Censi, R., Ruggieri, R., & Amendola, C. (2025). Smart Grids and Sustainability: The Impact of Digital Technologies on the Energy Transition. Energies, 18(9), 2149. https://doi.org/10.3390/en18092149

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