The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review

Abstract

This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet the increasing energy demands and the new requirements of renewable source integration, DTs offer promising solutions to enhance the monitoring, control, and optimization of these systems. In this paper, we examine the concept of DTs in the context of smart grids, and their requirements, challenges, and integration with the Internet of Things (IoT) and Artificial Intelligence (AI). We also discuss different applications in asset management, system operation, and disaster response. This paper analyzes current challenges, including data management, interoperability, cost, and ethical considerations. Through case studies from various sectors in Canada, we illustrate the real-world implementation and impact of DTs. Finally, we discuss emerging trends and future directions, highlighting the potential of DTs to revolutionize smart grid networks and contribute to more efficient, reliable, and sustainable power systems.

1. Introduction

In today’s world, no one can deny the fact that the power industry plays a crucial role in the progress of society. It can be seen as an engine driving the global economy forward and therefore directly influencing the day-to-day life of individuals. Thus, ensuring safe, stable and sufficient power supply is one of the keys for the sustainable development of our nations. However, we are facing many important challenges in meeting the growing demand for energy with the limited resources we have and the issue of climate change. Projections indicate an anticipated increase of 50% in demand by the year 2050 [1]; this presents a huge challenge for our existing infrastructure as we have to adapt as soon as possible to meet this requirement in a short time. Moreover, a significant portion of the power grids in developed countries have origins tracing back to the early to mid-20th century, and as such, a considerable segment of this foundational infrastructure is nearing the end of its intended lifecycle [2]. To deal with these issues, there arises an urgent need for the modernization and strengthening of our power grids. One promising avenue for progress manifests in the form of the smart grid concept and the seamless integration of renewable energy resources into the grid framework. As the landscape of the power system undergoes a significant transformation and embraces innovative technologies, the complexity of its structural framework increases exponentially. These new technologies require supplementary functionalities such as computational ability and communication capabilities, which cannot be fully represented in the physical layer. The increased sophistication of power systems necessitates the development of new approaches to address these challenges by providing enhanced information about the physical infrastructure.

The main difference between smart grids and conventional power grids relies on the flow of information. What used to be unidirectional transmission from the grid to consumers has evolved into a bidirectional exchange between both parties [3]. Alongside necessitating prompt compliance from consumers with grid commands, smart grids must also adapt their power supply based on user-reported data. The fluid nature of grid networks means their topology can shift continuously due to factors like new generation sources, overloads and outages [4]. Despite remarkable progress, the current state of smart grid development remains incomplete, with anticipated changes on the horizon [5]. Thus, Digital Twin (DT) technology is emerging as a potential remedy for this complexity, facilitating improved network element interaction and aiding grid operators in decision-making, especially during faults and cyber-attacks [6].

DTs are virtual replicas initially conceptualized a decade ago to mimic physical objects like aircraft engines, and have since evolved into comprehensive representations of organizational dynamics. For instance, NASA’s Digital Twin initiative, detailed in [7], integrates high-fidelity simulations with health management systems, historical maintenance records, and operational data onboard spacecraft for predictive maintenance, ensuring unparalleled safety and reliability. Similarly, Dassault employs Digital Twins for refined product design and production quality [8]. As shown in Figure 1, earlier models like Grieves’ three-dimensional Digital Twin [9] focused on basic connections between physical and virtual spaces. This concept was then expanded by [10] into a model with five dimensions for manufacturing applications, as shown in Figure 2a. Building on these foundations, [11] proposed a new five-dimensional DT system. This model, illustrated in Figure 2b, consists of five key dimensions: the physical system, digital system, updating engine, prediction engine, and optimization engine. It emphasizes cyclical data flow and decision-making processes between the physical and virtual realms, allowing for the real-time mirroring of a physical system’s lifecycle and enabling more informed, optimal decision-making.

The convergence of smart grids and DT signals progresses, offering a roadmap for sustainable and efficient energy systems in the future [12]. This integration will help tackle the contemporary challenges related to energy management, including the overbearing need to reduce carbon emissions, adapt to new energy sources and meet the increasing energy demand in a sustainable fashion [1].

With these technologies combined, energy providers can achieve an enhanced level of grid efficiency, reliability and flexibility. Indeed, this can be attained by combining the real-time operational data and interactive abilities of smart grids with the predictive analytics and simulation strengths of DTs. Achieving these goals involves the dynamic monitoring and maintenance of power generation equipment and control centers, along with the real-time operational management of the power grid. This includes overcoming challenges related to the safe and stable operation of power systems, developing advanced simulation platforms, enhancing computational precision, and innovating fault diagnosis and recovery technologies. Additionally, efforts are directed towards strengthening the defense capabilities of power systems to resist cascading failures, extreme weather events and external damage [13]. This synergy not only enhances the actual operational performance of power systems but also provides a more resilient and secure energy infrastructure. The robust cybersecurity features embedded in the smart grids, coupled with the predictive maintenance capabilities offered by Digital Twins, enhance grid resilience against potential disruptions and cyber-attacks. This ensures a continuous energy supply and protects vital national infrastructure from harm.

The convergence of new rapidly advancing technologies like Artificial Intelligence (AI), IoT, and cyber–physical systems with smart grid infrastructure marks the start of a new era of enhanced capabilities in power distribution networks [14]. These innovations enable the continuous gathering, processing, and analysis of different real-time data, constructing a digital representation of physical systems to accurately assess current and future states. This concept, known as DT modeling, is embraced across various industrial sectors, notably, the power energy industry. By employing a mature DT power system, both real-time and old data can be securely managed and efficiently utilized to enhance system operation, aiding in maintenance, design, and operational management tasks.

The rest of this paper is organized as follows: In Section 2, an overview is provided on the concept of Digital Twin technology in smart grids, encompassing the integration of Digital Twins with smart grids and their associated benefits. Section 3 delves into applications and recent research regarding Digital Twin technology in smart grids, covering aspects such as asset management, system operation and optimization, and disaster response and recovery. To provide a systematic analysis of the current research trend, this paper includes a comprehensive distribution analysis of research focuses across recent DT applications in smart grids. Section 4 addresses the challenges and limitations encountered in the implementation of Digital Twin technology. Section 5 explores current trends and future directions in the field. Finally, Section 6 presents the conclusion of this work.

2. Methodology

In this section, we discuss the details of the methodology used to conduct this comprehensive review. The main goal of our search was to review the latest work on DT applications in smart grids. The methodology follows guidelines from Kitchenham’s paper [15]. Our review process was designed to ensure the comprehensive coverage, systematic selection, and rigorous analysis of relevant literature published in the last five years, with particular emphasis on recent developments (2022–2024).

The review methodology consists of four main phases: (1) research protocol development and research question formulation, (2) search strategy and study selection process, (3) quality assessment of selected papers, and (4) data extraction and analysis.

2.1. Research Protocol and Research Questions

Research studies were found by performing a search using keywords on major digital libraries focusing on smart grid and Digital Twin applications. The search method was designed to capture both theoretical frameworks and practical implementations of Digital Twin technology in power systems.

The key search terms were combined using Boolean operators:

(“Digital Twin” OR “Digital Replica”) AND (“Smart Grid” OR “Power System” OR “Energy Grid”) AND (“Implementation” OR “Application” OR “Integration”)

For technology-specific aspects, the following additional terms were included:

• AI/ML integration: AND (“Artificial Intelligence” OR “Machine Learning”).
• Security aspects: AND (“Security” OR “Privacy” OR “Cybersecurity”).
• Implementation: AND (“Case Study” OR “Deployment” OR “Framework”).

Table 1. Initial search result distribution.

Database	Papers Found	After Filtering
IEEE Xplore	180	165
ScienceDirect	145	132
Web of Science	95	88
Google Scholar	65	58
Semantic Scholar	35	32
Total	520	475

summarizes initial search results from five databases. After filtering, the initial set of papers was refined, with major sources including IEEE Xplore, ScienceDirect, Web of Science, Google Scholar, and Semantic Scholar, resulting in a more focused collection of relevant studies.

Based on the paper’s main goal mentioned earlier in this section, three primary research questions were formulated:

RQ1: What are the current applications and implementation approaches, with case studies, of Digital Twins in smart grid systems?

RQ2: How are emerging technologies like AI, IoT, and blockchain being integrated with Digital Twins in smart grid applications?

RQ3: What are the key challenges, limitations, and future directions in implementing Digital Twins for smart grid applications?

The initial search results provided a lot of studies across different aspects of Digital Twin applications, including asset management frameworks [16,17,18], security implementations [19,20,21], and system operation solutions [22,23,24]. Moreover, recent papers (2022–2024) showed increased focus on AI integration and security aspects compared to earlier works.

A timeline visualization in Figure 3 shows the distribution of final selected papers across the years 2019–2024.

• • 2019: References [25,26,27,28].
  • 2020: References [24,29,30].
  • 2021: References [18,20,23,31,32,33,34,35].
  • 2022: References [22,36,37].
  • 2023: References [17,19,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54].
  • 2024: References [21,55,56,57,58,59,60,61,62,63,64].

2.2. Study Selection Process

The paper selection process was designed to capture the most relevant studies demonstrating practical Digital Twin implementations or significant theoretical contributions to smart grid applications. Selection was conducted in three stages:

Stage 1: Initial Screening (520 → 180 papers): Papers were screened based on title and abstract review. This stage identified papers with clear focus on Digital Twin applications in power systems, eliminating papers with only peripheral mentions of the technology.

Stage 2: Full-Text Assessment (180 → 95 papers): Detailed review of full texts focused on technical content and implementation details. Key papers emerged in several areas:

• Grid intelligence enhancement frameworks [16,17,21].
• Energy management implementations [17,18,20].
• Security testing frameworks [21,23,24].
• Novel application architectures [21].

Stage 3: Final Selection (49 papers): we applied our quality assessment criteria to the 95 papers; this resulted in 49 papers for detailed analysis. In this final stage, we prioritized papers demonstrating robust methodology, comprehensive validation, and significant contribution to the field. These papers formed the core foundation for our subsequent analysis of DT applications in smart grids.

A flow diagram showing the complete selection process with numbers at each stage and filtering criteria applied is shown in Figure 4.

The selection process used the following criteria:

Inclusion Criteria:

• Papers presenting original Digital Twin implementations in smart grids.
• Studies with clear validation of proposed solutions.
• Works demonstrating integration with emerging technologies.
• Papers with comprehensive security considerations.
• Studies reporting real-world deployment results.

Exclusion Criteria:

• General IoT papers without specific Digital Twin focus.
• Papers lacking technical implementation details.
• Duplicate studies covering the same implementation.
• Non-peer-reviewed technical reports.
• General blogs without technical contributions.

2.3. Quality Assessment Framework

The quality assessment method was used to evaluate each paper, from the result found in stage 2, across three key dimensions: technical depth, research contribution, and implementation maturity.

The technical depth dimension examined the level of implementation detail and validation. For instance, papers like [21], which presented a novel application architecture with detailed security testing framework, showed the highest score (3) in this category. Papers with partial implementation or simulation results, such as [18], received a medium score (2), while conceptual studies without implementation details were assigned the lowest score (1).

For research contributions, we evaluated the novelty and significance of each paper’s approach. The highest scores were awarded to papers that introduced novel approaches advancing the field, such as [16] that proposed a grid intelligence enhancement framework using DTs. Papers that offered significant improvements to existing methods, like the method in [20] that showed an enhanced energy management system, received medium scores, while those presenting minor modifications to existing approaches were scored lowest.

Implementation maturity was assessed based on the level of practical deployment and validation. Production-level implementations with documented results, like the real-time monitoring system proposed in [19], earned the highest scores. Pilot implementations and extensive simulations were scored as medium, while theoretical proposals or limited prototypes received the lowest scores.

Several remarkable trends resulted from this assessment. For example, security-focused papers [22,23,24] consistently achieved high technical depth scores, while AI integration studies [19,20] demonstrated particularly strong research contributions. Real-world implementations [16,17] excelled in implementation maturity, and papers published between 2022 and 2024 generally scored higher in technical depth, indicating increasing use of cutting-edge technologies in recent research.

For the final analysis, we selected papers that scored at least medium in two dimensions and showed no low scores in implementation maturity. This rigorous selection process resulted in 49 papers that formed a robust foundation for our subsequent analysis.

2.4. Data Analysis

The analysis method examined three primary dimensions that emerged from our review. The first one was to examine the technical implementation, and we observed several consistent architectural approaches across successful implementations. For instance, distributed architectures incorporating edge computing capabilities, as demonstrated by [16,18], emerged as a dominant subject. The integration of AI/ML for predictive capabilities formed another crucial pattern, with significant contributions from [20,22], while security-by-design principles, showcased in [23,24], became increasingly prominent in recent implementations.

The second aspect of our examination method was the applications of DT in smart grids. Based on the findings, we clustered them into three distinct categories: Asset management applications, focused primarily on predictive maintenance and real-time monitoring capabilities. System operation implementations emphasized grid optimization and energy management. Disaster response applications concentrated on system resilience and fault recovery mechanisms.

Several key findings emerged from our analysis. Recent implementations (2022–2024) demonstrated an increased emphasis on security integration, reflecting growing concerns about cybersecurity in smart grid systems. The integration of AI/ML technologies appeared in 67% of the successful implementations, indicating their crucial role in modern Digital Twin applications. Real-time monitoring capabilities were present in almost all of the studied cases, while blockchain integration emerged as a promising trend in newer security frameworks.

The evolution of the research focus from 2019 to 2024 revealed a clear shift from basic implementation concerns toward more sophisticated integration challenges. Early work (2019–2021) primarily addressed fundamental implementation issues, while recent research (2022–2024) increasingly focused on advanced security measures and AI integration.

3. Digital Twin Technology in Smart Grids

3.1. Evolution of Electric Grids Towards Digital Integration

The development of the electric grid showcases a long-standing process of innovation and technological advancement. It began with the straightforward direct current (DC) systems that Thomas Edison launched in 1882 at the Pearl Street Station in New York City. This evolution has led to the intricate smart and virtual power systems that we utilize today [65]. Despite DC’s limitations in transmission distance and efficiency, the transition to alternating current (AC) systems, pioneered by Nikola Tesla and George Westinghouse, allowed for more efficient transmission over long distances and set the stage for modern electricity distribution starting in 1886 [66].

As the 20th century progressed, as shown in Figure 5, the grid evolved into a more complex network. The first industrial microgrid, conceptualized in 1960, proposed a decentralized, reliable grid capable of autonomous operation or seamless integration with existing power infrastructure, utilizing local energy resources [67]. This concept paved the way for the Virtual Power Plant (VPP) introduced in 1997, which aggregated distributed energy resources to enhance power generation and trading within the energy market, embodying flexibility and decentralization [68].

The turn of the century marked significant advancements with the start of the smart grid, defined officially in 2007 by the Energy Independence and Security Act (EISA). Utilizing digital communications technology, smart grids improved the efficiency of energy use, reliability, and integration of other renewable sources [69]. The expansion of the Internet and the proliferation of connected devices ushered in the Internet of Things (IoT) by 2012, enhancing grid management through the interconnectedness of devices [70].

Further expanding connectivity, the Internet of Everything (IoE) was introduced, incorporating not only devices but also data, people, and processes into grid management, allowing for unprecedented integration and smarter energy management. The adoption of DTs around 2020 has enabled the simulation, analysis, and optimization of the entire energy system, boosting grid resilience and sustainability [71].

The electric grid constitutes an extensive system that delivers electricity. The electricity generated from multiple sources is distributed to consumers over broad areas, encompassing entire countries. This expansive network facilitates the generation of power from a diverse array of resources and efficiently manages a vast array of prosumers and consumers, even those located at great distances. It provides electricity at a relatively low cost, accommodating both expected and unexpected losses while fulfilling the demand for electricity. The progression of the electric grid has seen several advancements (Figure 6), moving from the conventional electric grid (CEG) to more sophisticated systems such as the smart grid (SG), microgrid (MG), Virtual Power Plant (VPP), and the Internet of Energy, each offering improvements in efficiency, reliability, and adaptability to modern energy needs.

3.2. Core Concepts and Implementation Requirements

Before diving into Digital Twin implementation requirements, it is important to understand the different grid types in which DT technology can be applied. The evolution of grid technology has progressed through several distinct stages, each with unique characteristics and capabilities that influence DT integration.

The conventional electric grid is designed around a centralized power generation station that connects transmission and distribution networks through electromechanical systems. It delivers electricity across extensive distribution areas via centralized one-way transmission and distribution lines controlled by electrically operated mechanical devices. This centralized setup features limited sensors, causing control challenges. As a result, the monitoring of power distribution and transmission is typically conducted manually, and the system lacks self-healing capabilities. The one-directional nature of this energy system contributes to significant transmission and distribution losses, increases the risk of grid overheating incidents, and complicates fault detection due to manual monitoring [72]. These issues can lead to frequent power disruptions, extended recovery times, and substantial economic losses.

In response to these limitations, the smart grid has emerged as an advanced energy infrastructure that enhances the capabilities of previous grids, making them faster, more intuitive, and collaborative. It integrates the roles of all participants, including producers, consumers, and utilities, to ensure reliable, cost-effective, and efficient energy distribution. Unlike the traditional grid, which is characterized by centralized generation and high transmission losses, the smart grid employs a bidirectional exchange of data and electricity, supporting both large-scale and localized power generation methods [73]. The next evolution in the grid technology is the Virtual Power Plant (VPP), which is a system hosted in the cloud that aggregates multiple types of power sources, functioning as a cohesive energy plant. VPPs are integral to enhancing the stability and intelligence of smart grids by utilizing distributed energy resources (DERs). They rely on sophisticated software systems to automatically manage, integrate, dispatch, and store electricity. Essentially, VPPs serve as the infrastructure for integrating DERs, allowing each connected resource to interact and synchronize like a neural network, akin to the Internet of Things.

3.2.1. Digital Twin Integration Framework

Digital Twin technology represents a transformative approach for monitoring and regulating smart grids, where it facilitates a dynamic and real-time simulation environment that mirrors the physical aspects of electrical grids. This integration promises to enhance the performance related to strategic planning, predictive maintenance, and monitoring within the grid infrastructure.

Figure 7 illustrates the key aspects of Digital Twin implementation in smart grids, encompassing the following:

• Real-Time Monitoring and Control: DT allows for the real-time monitoring and control of grid components, providing operators with up-to-date information to make informed decisions. For instance, the work by Kumar et al. in [40] explores the use of Digital Twins integrated with software-defined networking to enhance the cybersecurity of smart grids through real-time data exchange and analysis.
• Predictive Maintenance: By simulating various grid scenarios, Digital Twins help predict potential failures before they occur, thus minimizing downtime and extending the lifespan of grid components. This application is detailed in the study by Mourtzis et al. in [74], where a Digital Twin-enabled system facilitates predictive maintenance strategies for energy distribution.
• Enhanced Cybersecurity: With the increasing connectivity of smart grids, cybersecurity becomes crucial. Digital Twins can simulate cyber-attack scenarios to help develop robust defense mechanisms. An example of this is described by Olivares-Rojas et al. [75], where Digital Twins are used to forecast demand and enhance cybersecurity measures within the grid.
• Optimization of Grid Operations: Digital Twins contribute to the optimization of grid operations by modeling and analyzing the flow of electricity. This ensures efficient energy distribution and helps in managing demand response strategies effectively.

3.2.2. Implementation Requirements and Components

Microgrids are a progressive technology that leverages distributed energy resources (DERs) to address the limitations of the conventional electric grid. These resources help reduce the transmitted power and related distribution losses, thus offering a more efficient and secure energy system at reduced costs [76]. DERs are transforming our energy landscape through the integration of renewables like solar, wind, and wave power. This shift reduces our dependence on fossil fuels, fostering a greener future. Their flexibility and scalability encourage clean energy technology adoption, creating a more resilient and eco-friendly power infrastructure. By transitioning from a traditional centralized grid to a more complex yet manageable and operational system, microgrids simplify the integration of DERs and enhance grid expansion, quality, security, and efficiency.

Microgrids, whether operating independently or connected to a larger grid, consist of localized energy sources and loads. They can operate in dual modes: connected to the grid or as an island (standalone mode). In the standalone configuration, a microgrid operates independently without connection to a broader power network. These systems can seamlessly transition between the grid-connected and independent modes, providing ancillary services through grid interactions in the connected state. In both scenarios, microgrids continuously monitor small-scale power generators, loads, storage components, sensors, and control units, effectively forming a cohesive and controllable network segment [77]. They support both AC and DC outputs, or a hybrid of both, providing essential protections and introducing ’plug and play’ and ’peer-to-peer’ functionalities within the distributed grid framework. While they primarily promote renewable energy integration, not all microgrids fully utilize renewable distributed generation due to various constraints. Moreover, protective devices like reclosers and circuit breakers are crucial for maintaining safety and reliability, especially when the microgrid operates in dual modes and needs to manage significant variations in leakage currents during mode transitions.

The implementation of Digital Twins in smart grids requires several key technological components and considerations:

Communication and Information Technology (IT): This component plays vital roles in the smart grid’s operations. It incorporates a vast array of sensors and control devices across the transmission and distribution infrastructure, including remote monitoring systems like SCADA and household appliances that communicate with the grid [78]. This setup results in large volumes of data from meters, sensors, and synchrophasors, necessitating sophisticated data management systems to process and utilize this information effectively. Smart grids utilize advanced ICTs to enhance various aspects such as economic development, renewable energy integration, environmental impact, efficiency, reliability, safety, and security. The infrastructure should support the real-time analysis and management of the power system, enabling rapid response times and reducing the need for extensive human intervention. This leads to a highly adaptable energy production model that greatly improves efficiency and responsiveness in the energy sector. However, transitioning from conventional to smart grids involves high costs that can significantly impact industrial expenses. Additionally, the reliance on internet connectivity for real-time data exchange increases the vulnerability to cybersecurity threats, and the use of unauthorized software could lead to data breaches and cyber-attacks [79].

Data Management and Processing: Virtual power plants (VPPs), for instance, can simplify the complex data management of grids and facilitate rapid adjustments in market prices, optimizing energy transactions by selling surplus energy at higher rates and purchasing power during shortages at lower costs. This model benefits from demand-side flexibility, where consumers adjust their energy usage during peak and off-peak periods to maximize the financial returns from VPP operations. In scenarios like isolated wind generators within VPPs, they help mitigate quick fluctuations in power production that can cause disturbances in voltage and frequency. Energy storage systems (ESSs) within VPPs manage these fluctuations, ensuring voltage stability and providing time shifting to align energy availability with demand, thus enhancing grid reliability [80].

Security and Operational Requirements: For instance, Blockchain technology could enhance VPPs by ensuring large-scale data transparency, system information security, and real-time demand-side data integrity. However, the scalability, speed, and regulatory costs of blockchain need addressing before its full integration. VPPs also face challenges like universal acceptance among DER units, necessitating the development of standardized communication protocols to foster broader adoption. The operational challenges are significant; different production units may respond variably based on decisions by their owners or the VPP operators, requiring robust security measures and protocols for handling service disruptions. Establishing safety standards and web service criteria is crucial for maintaining system integrity [81]. When a DER seeks a new VPP connection, it consults a ”Match Maker” to facilitate this transition if its relationship with the current VPP operator ends, ensuring continuous and efficient operation.

The implementation requirements can be further understood through the comparative analysis shown in

Table 2. Comparative analysis of different grid types.

Grid Type	Advantages	Disadvantages	Principal Components	Ref
Traditional Grid	- Established reliability; - Economies of scale in power generation.	- Less flexibility with renewables; - Higher transmission losses.	- Coal, nuclear, hydroelectric power stations; - High-voltage transmission lines; - Substations	[82]
Microgrid	- Enhanced reliability and resilience; - Effective integration of renewables.	- High initial costs; - Technical complexity.	- Solar panels and wind turbines; - Battery storage systems; - Energy management systems.	[83]
Smart Grid	- Increased efficiency through real-time data; - Improved renewable integration.	- Significant investment in technology; - Complexity in data management.	- Smart meters for real-time monitoring; - Automated switches and relays; - Data communication networks.	[84]
Virtual Power Plant (VPP)	- Optimizes resources across units; - Enhances grid stability.	- Complex coordination; - Vulnerable to cyber threats.	- Distributed generation sources (e.g., solar and wind); - Central control and optimization software; - Network communication interfaces.	[16]

, which highlights the key components and considerations for different grid types, particularly relevant to Digital Twin integration.

3.2.3. Benefits and Implementation Outcomes

Digital Twins, a cutting-edge simulation tool, create detailed virtual replicas of physical grid systems, providing a platform for real-time monitoring, analysis, and optimization. This technology not only enhances the efficiency of smart grids operations but also significantly improves the predictive maintenance, resilience to disruptions, and overall system sustainability. By exploring the benefits of Digital Twins, we gain insights into their transformative impact on the energy sector, particularly in facilitating smarter energy management and accelerating the transition towards more responsive and integrated power systems.

Table 3. Analysis of research contributions and benefits in Digital Twin smart grid applications.

References	[17]	[18]	[19]	[20]	[21]	[22]	[23]	[24]	[31]
Research Aspects
Key Contributions
Grid intelligence enhancement	√	√	-	√	-	√	√	-	-
Energy management framework	-	√	√	-	√	-	√	-	-
Coordination model	-	-	√	-	√	-	-	-	-
Security testing framework	-	-	-	-	-	√	-	√	√
Authorization architecture	-	-	-	-	-	-	-	-	√
Novel application architecture	-	-	-	-	-	√	-	-	-
Benefits Delivered
Enhanced grid intelligence	√	√	-	√	-	√	√	-	-
Improved collaborative management	-	√	√	-	√	-	-	-	-
Real-time monitoring and control	√	-	√	√	√	√	-	-	-
Enhanced security	-	-	-	-	-	√	-	√	√
Operational efficiency	√	-	√	√	√	√	√	-	-
Integration support	√	√	-	-	-	-	√	-	-

Note: √ indicates that the reference covers aspects of the respective research contribution or benefits.

explores the various benefits of Digital Twins for smart grids that have been investigated in recent years.

4. Critical Applications

Digital Twin technology has emerged as a transformative development in smart grids, offering extensive benefits from asset management to predictive maintenance and beyond. This technology’s core idea is to create a virtual representation of physical assets, processes, or systems to simulate and predict their behavior under various conditions. Below, we delve into the applications and recent research concerning Digital Twin technology in smart grids.

4.1. Asset Management

Asset management in the domain of smart grids fundamentally leverages Digital Twin technology to ensure the optimal performance, maintenance, and lifecycle management of electrical grid assets. This comprehensive approach is pivotal not only for maximizing asset utility but also for minimizing operational costs and enhancing grid reliability and efficiency. At its core, asset management encompasses strategies and technologies aimed at optimizing both the performance and lifespan of these assets. Digital Twins play an instrumental role in this endeavor by providing a virtual representation of physical assets, which is continuously updated with real-time data. This enables dynamic analysis and decision-making, allowing for a more informed and effective asset management practice. Through the utilization of predictive analytics, condition monitoring, and performance optimization, Digital Twins facilitate a nuanced understanding and management of grid assets, thereby ensuring their sustained efficiency and reliability.

The application of DT technology in smart grid asset management encompasses three critical aspects: strategic maintenance planning, lifecycle management, and performance optimization, each underpinned by scholarly research. Strategic maintenance planning involves the use of Digital Twins in leveraging predictive analytics to forecast potential failure modes and maintenance needs, thereby optimizing maintenance schedules. This approach effectively reduces operational disruptions and costs by preventing unplanned outages [85,86].

Lifecycle management is significantly enhanced by Digital Twins, which provide crucial insights into the performance and degradation of assets over time. This enables operators to make well-informed decisions concerning asset refurbishment, upgrades, or replacement, thus extending the asset’s useful life and optimizing its utilization [87,88]. Lastly, performance optimization through Digital Twins offers a simulation platform for various operational scenarios to identify the most efficient usage of assets. This capability is pivotal for optimizing asset performance, reducing energy consumption, and ensuring compliance with regulatory standards [89].

4.1.1. Real-Time Health Monitoring and Performance Analysis

Digital Twins enable real-time health monitoring and performance analysis of grid components [90]. By leveraging sensor data and advanced analytics, they can provide instant feedback on the condition of electrical assets, detect anomalies, and assess performance degradation over time [91]. This approach allows for the immediate identification of potential issues before they escalate, ensuring the reliability and efficiency of the smart grid.

4.1.2. Predictive Maintenance and Forecasting

Predictive maintenance and forecasting are critical in minimizing downtime and operational costs. Digital Twins play a crucial role here by predicting when maintenance should be performed based on the current condition of the assets rather than on a predetermined schedule. This method significantly reduces unnecessary maintenance, extends the assets’ life, and ensures their optimal performance [92].

4.1.3. Extending Asset Life and Optimizing Replacement Strategies

The strategic use of Digital Twins can significantly extend the life of smart grid assets. By continuously monitoring the assets’ health and predicting future performance, Digital Twins help in making informed decisions about maintenance, upgrades, and replacements. This not only extends the assets’ operational life but also optimizes replacement strategies by ensuring that assets are only replaced when necessary, thus reducing costs and resource consumption.

4.2. System Operation and Optimization

This section discusses how Digital Twin technology enhances the operation and optimization of smart grids by providing real-time simulations and data-driven insights.

4.2.1. Load Balancing and Optimization

Digital Twin technology plays a crucial role in load balancing and optimization in smart grids. By creating a dynamic digital replica of the physical grid, operators can simulate various load scenarios in real time, allowing for the prediction and mitigation of potential imbalances before they occur. This proactive approach not only ensures a stable and efficient distribution of electrical power but also optimizes the grid’s operational efficiency. Advanced analytics applied to the Digital Twin can identify optimal load distribution patterns, reduce peak load pressures, and suggest modifications to improve system resilience and efficiency.

4.2.2. Demand Response Management

Demand response management is a critical component of modern smart grids, aimed at adjusting the demand for power instead of the supply. Digital twins facilitate a more responsive and flexible demand management system by accurately modeling the grid and consumer behavior. This allows utilities to incentivize reduced consumption during peak times and manage the distribution of resources more effectively. The integration of Digital Twins in demand response programs enhances the grid’s ability to adapt to real-time changes and maintain an equilibrium between supply and demand, contributing to overall energy savings and system reliability.

4.2.3. Integration of Renewable Energy Sources

The incorporation of renewable energy sources into the grid is essential for sustainable development but introduces variability and unpredictability in the power supply. Digital Twins assist in the seamless integration of these energy sources by simulating their impact on the grid, allowing operators to anticipate fluctuations and adjust the system accordingly. This capability ensures a stable energy supply and optimizes clean energy utilization, by reducing dependence on non-renewable resources and decreasing carbon emissions. Additionally, Digital Twins can help in optimizing the placement and operation of renewable energy assets, ensuring their maximum efficiency and contribution to the grid.

4.2.4. Overall Grid Optimization

At the heart of system operation and optimization is the overarching goal of achieving overall grid optimization. Digital twins provide a comprehensive toolset for this purpose, enabling the simulation and analysis of the entire grid under various conditions. This holistic view allows for the identification of bottlenecks, vulnerabilities, and opportunities for efficiency improvements. By leveraging real-time data and predictive analytics, Digital Twins can guide decisions on infrastructure investments, maintenance schedules, and operational strategies, leading to a more resilient, efficient, and sustainable grid.

4.3. Disaster Response and Recovery

Essential to reinforcing smart grid resilience against disruptive forces like natural disasters and cyber threats, DTs have emerged as indispensable tools for comprehensive simulations, scenario analysis, and meticulous recovery planning. By virtually mirroring the physical grid infrastructure, these digital replicas can model the real-time impacts of potential disaster scenarios, empowering utilities to proactively formulate response strategies and restorative measures. This section explores how Digital Twins bolster grid resilience through their ability to conduct granular simulations and analyze diverse scenarios, fortifying preparedness and guiding efficient recovery initiatives.

4.3.1. Simulation of Natural Disasters

Natural disasters, such as hurricanes, earthquakes, floods, and wildfires, could pose some significant challenges to the resilience and stability of the critical power grid infrastructure. To reduce the impact of these events that can be difficult to predict in advance, power operators are turning to new advanced technologies like DT that can simulate these events and their potential impact on the smart grid infrastructure, and therefore, find weaknesses and adjust mitigation strategies.

For instance, a Digital Twin can simulate the impact of a hurricane, including high winds and flooding, on specific parts of the grid. This simulation helps in pinpointing areas that require reinforcement, such as securing transmission lines or waterproofing substations, and planning for the strategic deployment of repair crews and resources in anticipation of damage. In this direction, the study conducted in [38] introduces an innovative approach to modeling and predicting the performance of Electric Power Networks (EPNs) when exposed to extreme weather events. The suggested DT framework integrates physical models, such as hazard and vulnerability assessments, with data-centric methodologies, specifically, a dynamic Bayesian network (DBN), to create a high-fidelity hybrid model that can receive updates in near-real time through sensor data. The authors demonstrate the scalability and accuracy of their DT framework by applying it to Galveston Island’s EPN that was subjected to Hurricane Ike. The results, validated against historical records, show that DTs can deliver precise and comprehensive predictions regarding power outages, utility pole failures, and substation failures. The paper concludes that the proposed DT framework is simple, practical, and applicable for prediction and decision-making in the face of hurricanes, with the potential to be adapted for various critical infrastructure networks and their interdependencies. However, the current work has some limitations, such as focusing solely on the EPN without modeling the interdependencies with other critical infrastructure systems. Additionally, future research can expand the framework to conceptualize the community as an interconnected network of systems, evolve it into a comprehensive lifecycle and recurring upkeep digital replica, and further explore the proposed DTs’ potential for post-disaster management and guiding restoration plans. Another study introduced in [55] proposed a design for a smart earthquake prevention and mitigation service platform for the city of Chuzhou in China that integrates various data sources, including geographic information, earthquake thematic data, and public data. The platform employs advanced technologies such as Digital Twins, big data, and GIS to provide a range of services for earthquake disaster risk prevention and control, public earthquake prevention and mitigation services, and visualization-assisted decision-making. It consists of several subsystems, including an earthquake comprehensive application system, earthquake DT decision-making visualization system, earthquake public service information system, and smart earthquake prevention app. These subsystems offer various functionalities, such as earthquake structure information inquiry, three-dimensional geological spatial analysis, disaster risk assessment and zoning, seismic damage prediction, situational display, planning and site selection, seismic fortification, scenario simulation, emergency response, and public information services. While the Chuzhou smart earthquake prevention and mitigation service platform demonstrated promising results in its trial operation, there are some limitations to the work. The platform primarily focuses on earthquake-related data and services, and the integration with other critical infrastructure systems or disaster types is not explicitly addressed. The paper does not provide a detailed discussion on the challenges associated with data sharing, integration, and updating mechanisms between the platform and provincial-level seismic departments. The scalability and adaptability of the platform to other cities or regions with different earthquake risk profiles and data availability are not thoroughly explored.

4.3.2. Cyber-Attack Scenario Analysis

In addition to the natural disasters discussed in the previous section, smart grid infrastructures face an increasing threat of cyber-attacks that can disrupt the power supply process and compromise grid security. DTs could be used as a potential real-time testbed for these infrastructures to simulate different possible attacks, including attacks on grid control systems, data theft, and the introduction of malware into operational networks. By having access to this option, power suppliers can assess the grid’s vulnerabilities and the efficacy of their cybersecurity countermeasures.

The study discussed in [39] developed a new strategy for the real-time identification and localization of cyber-attacks in power distribution systems by leveraging DT technology. The proposed approach creates a cyber–physical reference model that mirrors the dynamic characteristics of the actual system. The key contribution of this research was the introduction of the new metric called Residual Rate of Change (RRC), which is computed by comparing actual physical measurements against those generated by the DT model. The RRC method showed good results particularly in distinguishing False Data Injection (FDI) attacks targeting load measurements and protective devices. The experimental outcomes of this research validated the method’s efficacy in localizing cyber-attacks across diverse operational scenarios within the power distribution system. Notably, the approach showed good improvements over existing learning-based strategies. However, the current work has some limitations: The proposed method is tested on a specific test distribution system, and its scalability to larger and more complex distribution networks is not thoroughly explored. Moreover, the study did not address the impact of measurement errors and uncertainties on the performance of the proposed approach. Finally, the computational complexity and real-time performance of the proposed method for large-scale distribution systems are not discussed in detail. Another study [40] proposed DT-driven software-defined networking (SDN) for smart grid application. It incorporates blockchain technologies to secure data sharing across the network nodes and uses deep learning (DL) models as an intrusion detection method. The proposed framework includes a secure communication channel between smart meters and service providers using an authentication scheme based on blockchain; this will ensure data integrity and confidentiality through mutual authentication and key agreement phases. Additionally, a new DL-based Intrusion Detection System (IDS) is introduced, using deep learning with self-attention and Bi-GRU, integrated with SDN to enhance smart grid performance. Evaluated on the N-BaIoT dataset, it achieves 99.73% accuracy. However, the current work still has some limitations, such as the lack of a comprehensive investigation into the scalability, computational complexity, and integration of the proposed framework with existing security protocols.

4.3.3. Enhancing Recovery Efforts

In the wake of disasters, Digital Twins prove indispensable for guiding the recovery and restoration of critical systems like smart grids. By virtually replicating the current state of the physical grid, Digital Twins pinpoint severely impacted areas, allowing utilities to prioritize recovery initiatives for essential services and vulnerable populations. Their true strength lies in integrating diverse data—from field crews, drones, satellite imagery and more—into a unified view of the damage and restoration progress. This convergence empowers precise resource allocation, the efficient execution of recovery actions, and swift, safe power restoration. The studies discussed in this section exemplify how Digital Twins enhance situational awareness, enable proactive decision-making, and facilitate coordinated responses during this crucial recovery phase.

For instance, the paper in [36] proposes a multi-tiered framework leveraging Digital Twins to enhance both the preventative measures and incident response of Critical Cyber Infrastructures (CCIs). The proposed model includes three distinct sections: a foundational layer focused on technical systems, an intermediary layer managing operational processes, and an overarching layer facilitating external collaborations. The study also highlights the importance of situational awareness (SA) and Common Operating Picture (COP) in enabling coordinated actions; the goal is to streamline response efforts and improve the overall system resilience. While the proposed model shows promise, its broader use across varied infrastructure contexts remains to be examined through practical implementation and testing. The study in [41] proposes the application of DT to enhance fault tolerance in small-scale power networks, particularly focusing on a Battery Energy Storage System (BESS). The proposed model includes four interconnected components: the physical device, its digital counterpart, an adaptive decision-making module, and a communication interface. The DT uses state prediction models, generating error vectors to identify unaccounted-for impacts on the physical BESS. The intelligent module interprets these vectors to guide responses, such as IEEE Std. 1547-2018 ride-through actions or operational mode switches [93]. The study demonstrates this approach with a BESS voltage source converter (VSC) simulation, presenting error vector outcomes under various fault scenarios. However, the research is limited to this specific component, without exploring the scalability to broader nanogrid applications. The intelligent module’s implementation details and decision-making processes remain underdeveloped. Moreover, the framework’s effectiveness in enhancing system recovery and ride-through capabilities during simultaneous faults lacks quantitative comparisons with existing methods.

4.3.4. Long-Term Resilience Planning

Extending beyond the immediate disaster response and recovery, DTs make invaluable contributions to long-term resilience planning for smart grids and critical infrastructure. By analyzing data from past disruptive events and simulated scenarios, power utilities can identify emerging trends, refine their predictive models, and continuously enhance their disaster response and recovery strategies. This ongoing analysis catalyzes a virtuous cycle of resilience improvement, enabling grid operators to adapt proactively to the evolving nature of threats and the escalating demands on energy infrastructure. The studies highlighted in [21,42,56] exemplify how Digital Twins, coupled with advanced optimization techniques, data spaces, and recommender systems, empower utilities to fortify the resilience of smart grids against a spectrum of challenges, from natural disasters to cyber-attacks. The authors of the work introduced in [41] present a new approach for optimizing microgrid operations under varying conditions, addressing uncertainties in network costs, renewable generation, and load demands. The proposed hybrid stochastic–robust optimization (HSRO) method combines two optimization strategies to manage these uncertainties effectively. The study incorporates demand response programs to enhance long-term microgrid resilience and utilizes Monte Carlo simulations to improve the performance under uncertain conditions. It also explores the potential of IoT device integration for real-time grid monitoring to prepare for extreme scenarios. The HSRO framework is tested on a large-scale microgrid model based on the IEEE 33-bus system, integrating various energy sources including wind turbines, conventional generators, energy storage systems, and photovoltaic panels. However, the scalability and applicability of the proposed HSRO method to different microgrid configurations and sizes are not thoroughly investigated. The paper does not provide a detailed discussion on the implementation challenges and computational complexity of the proposed method, particularly of real-time operation and decision-making.

The work in [56] proposes a new architecture for building long-term cyber-resilient smart grids. This approach integrates DT model and data space concepts, transforming grid information from Common Information Models (CIMs) to the NGSI-LD format to improve its interoperability. The framework uses FIWARE technology to create a real-time DT of the physical infrastructure. It incorporates an open-source security event management solution, OpenSearch, for continuous monitoring, event analysis, and threat detection. The proposed approach demonstrates compliance with the IDMEF v2 standards for incident reporting, enabling potential integration with external cybersecurity frameworks. However, the paper does not extensively discuss the scalability and performance evaluation of the proposed architecture for large-scale smart grid deployments. Additionally, the integration challenges and potential conflicts with existing smart grid security protocols and standards are not thoroughly addressed. Furthermore, the computational overhead and resource requirements for real-time data processing and analysis, especially in complex grid configurations, are not comprehensively analyzed. In [21] a smart grid-based hybrid DT framework is proposed to provide demand-side recommendation services in distributed power systems and therefore has a better long-term resilience plan. The framework develops a hybrid DT model that emulates the behavior of smart grid end-users based on their electrical components, such as solar photovoltaics (PVs); heating, ventilation, and air conditioning (HVAC) systems; and lithium-ion batteries. The hybrid model combines physics-based ordinary differential equations (ODEs) that capture the domain-specific physical constraints and recurrent neural networks (RNNs) for machine learning-based predictions. This hybrid approach allows for accurate modeling and adaptation to dynamic changes in the behavior of distributed grid entities. The framework also integrates non-intrusive load monitoring (NILM) techniques to disaggregate the overall energy consumption into individual appliance models. By connecting the hybrid Digital Twins with AI-driven recommender systems, the framework can provide personalized energy efficiency recommendations to end-users based on near-real-time deviations in their consumption patterns. The study demonstrates the potential of the proposed approach to achieve a 36.8% reduction in the net energy consumption through the implementation of the recommendations. However, the scalability and computational complexity challenges associated with large-scale deployments of the framework are not extensively discussed. Additionally, the integration challenges with existing smart grid infrastructure and potential conflicts with established standards and protocols are not thoroughly addressed. Furthermore, the impact of the data quality, availability, and labeling efforts required for training the machine learning models is not comprehensively analyzed. To synthesize the research papers discussed above,

Table 4. Distribution of research focused on Digital Twin applications (2022–2024).

Research Aspect	Number of Papers	Percentage *	Key References
Technology Focus:
AI/ML integration	6	67%	[55]
Real-time processing	9	100%	All papers
Blockchain	2	22%	[39,56]
IoT integration	5	56%	[36,39,41,42,92]
Application Domain:
Disaster management	3	33%	[38,40,92]
Security and privacy	4	44%	[39,40,55,56]
Grid optimization	3	33%	[41,42,56]
Asset management	2	22%	[38]
Research Type:
Theoretical framework	4	44%	[39,40,55,56]
Simulation results	7	78%	[38]
Real-world implementation	7	44%	[38]

* Number of papers (From column 2) divided by total of covered papers (9) in this section

presents a quantitative analysis of the research focus distribution across the reviewed papers.

5. Challenges and Limitations

Overall, the examples of applications we discussed in the previous section illustrate that for the entire duration of the data lifecycle of the physical twin, the practices of data identification and storage for the Digital Twin will require a level of planning and governance beyond that currently used to access the same data and may impede the perceived ease and speed at which data can be used in decision-making [94].

On the other hand, many pieces of data from the physical twin will already have existing data storage systems with governance processes around it already being used in the organization. An example would be maintenance records for a piece of equipment. In order for the Digital Twin to simulate the effect of changes to the maintenance schedule on the performance data of the piece of equipment, the maintenance records must also be integrated into the digital environment [95]. An understanding of the existence of the maintenance data in the data storage system for the physical twin, as well as the importance and purpose of the data and how it will be accessed, will be required before deciding to duplicate the data into the digital environment. This process will be unfamiliar to the engineers responsible for their own piece of equipment who can access the data they need on an ad hoc basis from their storage systems, often without consideration as to what the data actually are and additional processes to ensure the availability and quality [96,97]. Without going into too much detail on data management best practice, we know that practices will involve identifying what data are important, creating data storage systems, specifying the availability need for access and the level of data quality, creating processes to ensure data quality, and removing weak data. All these processes must be performed for data from the Digital Twin [98].

5.1. Implementation Challenges and Solutions

5.1.1. Limitations

In a system with multiple Digital Twins and integration into a comprehensive emulated system, there is a possible point of data exchange between the twins and the real system. At this point, data protection issues may fall under the wide definition of exportation, which, regardless of Brexit, involves the transfer of data outside the EEA [99,100]. For any transfer of personal data, a lawful basis for that transfer and a data protection impact assessment are necessary to ascertain that data protection standards are being maintained, something that may not be possible given the state of the art of the technology and the lack of a well-defined precedent. In the Digital Twin approach, the data being used by the physical twin should always be in a synchronized state with the same data on the digital representation. Any divergence of the data can lead to unexpected results from the digital model. This principle raises questions for data residency and its regulation, including the legal and jurisdictional aspects.

Data governance is necessary to determine who will be responsible for data at every stage of their lifecycle, including creation, initial storage, use and maintenance, withdrawal from storage, and final deposition. It includes information about access control, data consent and policies, data quality, compliance, and audits. A lack of clarity on the access to the data can lead to potential security issues or undermine the usefulness of the data. Data provenance helps determine the original source of data and the changes that have occurred over time. In case of data breaches, forensics can be used as an application of provenance to an isolated set of data. As per the literature reviewed, data security and management are very critical points of Digital Twin technology. Due to the use of advanced analytic techniques, some proprietary information of businesses and organizations might be compromised. To mitigate this risk, custodians of information should make sure that this information is classified before it is used in any algorithms.

5.1.2. Proposed Solutions

To overcome the data management and security challenges, the following solutions are proposed:

-

Data Encryption and End-to-End Security Protocols:

Data encryption and end-to-end security protocols are crucial for safeguarding Digital Twins (DTs) in various applications. The integration of advanced encryption methods, such as AES and Homomorphic Encryption, enhances data protection during transmission and storage. For instance, a proposed architecture utilizes AES encryption alongside a keystore to secure data interactions, while identity authentication ensures data integrity [43]. Additionally, TwinCrypt, a novel algorithm, combines Homomorphic Encryption with Secure Multi-Party Computation to achieve efficient data synchronization in Fog-Edge Cloud environments [57]. Furthermore, the incorporation of blockchain technology with Digital Twins provides a tamper-proof environment for data exchange, enhancing security and transparency across sectors [58].

-

Cloud and Edge Computing for Data Processing:

Cloud and edge computing are integral to optimizing data processing in Digital Twin systems, which handle large volumes of real-time data. Cloud platforms offer scalable storage and computational power, while edge computing reduces latency by processing data closer to the source. This hybrid approach enhances both the efficiency and security. Cloud computing ensures scalability and cost-efficiency, reducing operational costs by up to 30% through dynamic resource allocation [101], while edge computing significantly decreases latency, with average delays dropping as low as 3.03 milliseconds [102]. Additionally, localized data processing improves security by minimizing transmission risks, making this combination vital for industries like healthcare and finance [103].

-

Automated Data Integrity Checks and Synchronization Protocols:

Automated data integrity checks and synchronization protocols are essential for maintaining alignment between physical and Digital Twins. These checks enable real-time synchronization, alerting users to discrepancies, and are further enhanced by predictive algorithms that anticipate changes in physical systems, minimizing data lags. Automated checks ensure continuous data flow between twins, reducing misalignment risks [44], while systems leveraging cloud services and in-house servers enhance scalability. Predictive algorithms forecast changes in physical assets, allowing timely updates to Digital Twins, mitigating risks from delayed data updates [45]. Additionally, integrity-preserving schemes, such as homomorphic fingerprinting, ensure data security during aggregation [46], fostering trust in Digital Twin applications.

5.2. Interoperability and Standardization in Digital Twins

5.2.1. Limitations

An excellent example of the best practice requirements can be found in the aerospace and defense industries, where there are numerous different Digital Twin systems that must work together with various complex systems and simulations. OpenMETA is an open-source data management system used to create and manage models and simulations from different tools and/or domains [104]. It achieves this by providing a common data model with a domain-specific language tailored to modeling and simulation. This allows for seamless integration between toolsets, model reuse, and sharing and executing complex workflows for model-based systems engineering. OpenMETA has huge potential to be applied to Digital Twin systems outside the aerospace and defense industries once it is more developed.

Moving forward, Digital Twin systems must maintain interoperability with other systems via data integration and/or the ability to interface directly with other systems. In order to achieve this, new Digital Twin systems must be designed with open architectures and common data models to allow for easier integration with other systems. Open architectures allow systems to be more easily understood and accessed by external systems, while common data models would provide a common ground for different systems to map, transform, and exchange data.

Interoperability between different Digital Twin systems is vital for a variety of reasons. It facilitates collaboration on shared projects, allows businesses to operate and innovate more efficiently, and enables a surge in new and improved technologies. Unfortunately, interoperability has almost been an afterthought with the multitude of Digital Twin systems employed by businesses. Existing systems are often overhauled or abandoned for newer systems as business requirements and technologies advance. This has resulted in a disparate landscape of systems where there is little communication between different Digital Twins of the same asset, as well as different Digital Twins of different assets.

5.2.2. Proposed Solutions

Several specific methods can be implemented to overcome these limitations.

-

Adoption of Open Architectures:

To address interoperability challenges, organizations should implement open architectures that enhance data transparency and modularity. This approach allows for the integration of various systems, as seen in the AECO sector, where poor interoperability has significant economic impacts due to fragmented data exchange. The adoption of common data models, such as the Industry Foundation Class (IFC), can facilitate seamless communication across platforms [59]. Collaborations with standardization bodies like ISO and IEEE are crucial for developing these models, as demonstrated by the Smart Interoperability Architecture (SINA), which promotes decentralized data management and enhances trust among users [105]. Furthermore, the use of standards like OPC UA can optimize data-driven decision-making and improve system integration [47].

-

API Integration and Middleware Solutions:

API integration and middleware solutions play a crucial role in connecting legacy systems with modern Digital Twins (DTs). These technologies enable real-time data sharing, facilitating communication between platforms not initially designed to interact. Middleware serves as an abstraction layer, allowing the seamless integration of existing infrastructures with minimal disruption, thereby improving the interoperability [48]. Key approaches include middleware strategies that manage virtual replicas of real-world entities, such as YANG-powered DT middleware, which enhances communication in IoT networks by reducing latency and resource consumption [49]. API integration, particularly through Data Link architecture, merges dispersed data from different systems, supporting the creation and operation of Digital Twins while streamlining data management and control systems [29].

-

Standardized Communication Protocols:

The standardization of communication protocols is crucial for efficient data transfer and real-time information exchange between Digital Twin systems, ensuring scalability and interoperability. Common protocols like MQTT, OPC UA, and RESTful APIs support seamless communication across diverse platforms. Standardization is vital for digital transformation and promotes the effective implementation of Digital Twins [106,107]. MQTT and RESTful APIs offer lightweight communication, while OPC UA ensures secure data exchange in industrial settings [108].

5.3. Cost and Complexity

5.3.1. Limitations

The cost and complexity associated with developing and deploying Digital Twin systems pose significant barriers to adoption, particularly for smaller organizations. Companies face challenges in accurately defining the Return on Investment (ROI) for Digital Twin implementations, as the technology is still relatively new, and cost estimates can be difficult to determine. While Digital Twins are expected to reduce product development and operational costs over time, the upfront investment required for building digital representations of physical entities is often prohibitive. These costs include data acquisition, sensor deployment, and the integration of IoT technologies, all of which can add substantial expenses.

Furthermore, the complexity of integrating multiple data sources and technologies, such as simulation, enterprise IT, operational technology (OT), and IoT-connected devices, exacerbates the cost and complexity issues. A proportion of 72% of early Digital Twin adopters report difficulties in unifying disparate data types, leading to challenges in maintaining system interoperability and ensuring robust cybersecurity practices. This fusion of technologies adds to the already high cost of creating and maintaining Digital Twin environments.

Despite these challenges, the potential for Digital Twin technology remains substantial, driving organizations to make difficult investment decisions. However, due to the nascent nature of the technology, best practices for cost management and complexity reduction are still evolving, and there is a degree of risk and experimentation involved in the deployment of Digital Twins.

5.3.2. Proposed Solutions

To overcome the cost and complexity challenges, the following solutions are proposed:

-

Modular Deployment and Scalability:

Adopting a modular approach to Digital Twin (DT) implementation enables organizations to minimize upfront costs while enhancing scalability. By starting with smaller, targeted implementations, businesses can expand their DT infrastructure gradually as benefits become evident. This strategy is supported by microservice architectures, which facilitate the modularization and efficient management of components. For example, micro-frontend technologies decompose complex systems into manageable web-based DTs, improving maintainability and productivity [60]. Additionally, cloud-based platforms with pay-as-you-go models reduce initial investments and allow for flexible scaling [61]. Microservices enhance the connectivity between DT components, streamlining integration [50]. Despite the benefits, this approach can introduce complexities in integration, requiring careful planning for seamless operation across systems.

-

Open-Source Tools and Shared Resources:

Open-source Digital Twin platforms, such as OpenMETA and OpenPLC, offer significant cost reductions by providing essential tools without the financial burden of proprietary solutions. These frameworks make advanced technologies accessible to smaller organizations, enabling the creation of complex Digital Twins that integrate technologies like IoT and machine learning. Open-source solutions eliminate licensing fees, reducing costs for smaller firms [62], while collaborative efforts with industry consortia further lower expenses through shared resources and best practices [51]. OpenPLC, for example, supports scalable automation, improving operational efficiency [62]. Frameworks like OpenTwins enhance Digital Twin capabilities by orchestrating multiple technologies. Additionally, open-source communities foster innovation through collaboration and shared knowledge [63].

-

Cost–Benefit Analysis and Phased Investments:

Conducting a thorough cost–benefit analysis (CBA) at the outset of a Digital Twin project is crucial for identifying potential savings and operational improvements. This approach helps prioritize investments in areas that offer the highest returns, particularly in sectors like renewable energy, where Digital Twins enhance the operational efficiency and predictive maintenance. Phased investments distribute costs over the project lifecycle, mitigating financial risks and reducing upfront expenses, allowing for better capital allocation and risk assessment [109]. Key benefits of Digital Twins include streamlining workflows and reducing manual monitoring costs [110], identifying faults before they disrupt operations, and supporting detailed financial modeling of project feasibility. They also enhance strategic decision-making and investor confidence through data-driven insights [110] and optimize performance across the product lifecycle [111]. Additionally, indirect societal and environmental benefits should not be overlooked.

5.4. Ethical Considerations

5.4.1. Limitations

Favela in [112] also highlights that Digital Twins and their applications are posing a threat towards their users’ privacy. A Digital Twin will be a direct representation of its physical entity, and all of its data and information will be collected from it. With more personal and sensitive data being collected, there is an increased risk of the misuse or mismanagement of the data. Digital twins in the healthcare sector, for example, can benefit from the predictive modeling of diseases; however, this will involve accessing historical data and predicting future states. Therefore, any adverse event occurring in the creation or usage of health Digital Twins that results in harm to a patient can be viewed as medical malpractice. Digital Twins from the aerospace sector have already witnessed potential collisions with the law in certain aspects [113]. As the Digital Twin and its relevant physical entity cannot be discriminated in a legal context, the Digital Twin has potential liability and tort exposure equivalent to its physical counterpart.

5.4.2. Proposed Solutions

The following solutions are proposed to address data management and security challenges:

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Data Governance and Privacy Frameworks:

To effectively address privacy concerns in Digital Twin systems, robust data governance frameworks are critical. These frameworks should establish clear policies for data access, consent, and usage, ensuring compliance with regulations such as GDPR. Implementing data anonymization techniques helps protect individual identities while maintaining data utility, while differential privacy allows for data analysis without compromising individual privacy [114]. Strict access control policies should be established to limit data sharing to authorized users only [115], with role-based access controls enhancing security and reducing the risk of unauthorized access [116]. Best practices for data governance should align with regulatory requirements [114], and stakeholder engagement in the governance process ensures comprehensive oversight. While these frameworks are essential for protecting sensitive data, balancing privacy with data utility and accessibility can present implementation challenges.

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Clear Legal Accountability and Liability Policies:

Establishing clear legal accountability and liability policies for Digital Twins is crucial, especially in safety-critical industries. These frameworks should define the legal equivalence of Digital Twins to their physical counterparts and outline liability scenarios for damage caused by errors in digital models. A unified model law is needed to bridge regulatory gaps, similar to proposals in social media laws [117]. It is also important to define civil and criminal liabilities within digital technologies. The EU’s AI Liability Directive offers a framework that can be adapted for Digital Twins to address harms caused by AI, with clear guidelines on the burden of proof and duty of care for accountability. Balancing accountability with innovation is essential to avoid stifling technological progress [118]. However, overly stringent regulations could slow the development and deployment of Digital Twin technologies, limiting their potential benefits.

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Ethical AI and Predictive Modeling Standards:

To mitigate the risks associated with predictive algorithms, organizations must adopt ethical AI frameworks that emphasize transparency, fairness, and inclusivity. Regular audits of predictive models are crucial for identifying biases and ensuring accuracy, particularly in sensitive areas like healthcare, where errors can have severe consequences. Ethical frameworks should establish guidelines that prioritize fairness, accountability, and transparency in AI development [119], and involve multidisciplinary teams, including ethicists, throughout the AI lifecycle [120]. Regular audits help detect biases in predictive models, and it is important that decisions made by Digital Twins are verified by human experts. Clear guidelines should also be provided on the limitations of predictive modeling [121], while advocating for policies that promote human rights and societal values in AI applications. Despite the importance of these frameworks, there is ongoing debate about balancing ethical constraints with innovation, as overly strict regulations could hinder technological progress in AI.

6. Case Studies of Digital Twin Implementation

6.1. Regulatory and Policy Development

Before we discuss different real-world case studies, it is essential to bring attention to the critical need for a new regulatory framework of this new emerging technology. As we discussed in the previous sections, DT technologies can be used in all areas of concern of the various infrastructures and systems. Therefore, there is a common understanding of the need for an overarching regulatory framework to ensure that this complex digital dynamic of infinite data sharing among cyber representations of the physical world occurs fairly and transparently. For instance, DTs are becoming more prevalent in the transportation sector and have garnered the attention of regulators and policymakers as new opportunities arise for improving safety, security, and transportation systems in general. These new opportunities raise a number of regulatory, legal, and policy issues that need to be examined and understood. Despite the promise of the new opportunities and the worldwide interest in and investment behind the technologies, Digital Twins and their associated technologies have received little focused attention from regulators and policymakers in Canada. A comparison of the regulatory and policy development associated with DT technologies in Canada to that in Europe, the US, and Mexico reveals that most jurisdictions are monitoring DT development, but only some have actively launched initiatives to better understand and address it.

Canadian jurisdictions in the transportation and infrastructure are slowly starting to react to the rapid development of DT technologies that can address specific needs and priorities [122]. However, the array of developments, interests, concerns, and uncertainties in Canada varies among jurisdictions. In addition, the Canadian federal government has not as yet expressed any overarching interests, concerns, or uncertainties regarding these technologies’ development. With this context in mind, an in-depth understanding of the current state of regulations and standards related to DT technologies is necessary to inform policy and the development of the technologies going forward.

6.2. Case Studies

A review of case studies can illustrate some of the more progressive and widely adopted Digital Twins. Case studies were commonly found with widespread applications in industry sectors, including manufacturing, environment management and agriculture, healthcare, energy and smart cities. The pilot projects represent a deeper aspect of the implementation of Digital Twins in Canada. By studying real-world applications across various fields, researchers can understand each sector’s scope, reach, implications, and follow-on projects. This scope and scale can bring insight and more clarity into the challenges associated with implementing a Digital Twin and its multiple potential outputs from continued research and development (R&D), as well as future projects.

In Canada, the Municipal Asset Management Service (MAMS) was established in 2017 to work with municipalities and service organizations to look for better ways to plan, invest, run, and maintain municipal systems [123]. The service focuses on providing a deeper understanding of municipal asset management through a municipal asset management Digital Twin. The Digital Twin will focus on visualizing asset management processes at each municipality’s service level, identifying gaps and prioritizing solutions for continuous improvement over time [11]. Based on open-source software, this toolkit will be applied to sixty municipalities in parallel across Canada. The six-month-long project has entered its pilot year, with the Tasman District Council, New Zealand, recently onboarded as an additional case study. Further trials across Canadian municipalities continue through 2024. A spin-off similar to the toolkit is being developed for service organizations and is still under review.

Real-world case studies of Digital Twins in Canada are presented in

Table 5. Real-world case studies in Canada.

Project	Project Overview	Sector	Outcome
Toronto Waterfront Smart City Initiative	In collaboration with Sidewalk Labs, Toronto initiated the Waterfront Smart City project. The Digital Twin technology was employed to create a virtual replica of the city’s waterfront area. This Digital Twin was used to simulate various urban scenarios, optimize energy usage, manage traffic flow, and design sustainable urban infrastructure. The project emphasized data-driven decision-making and public engagement in urban planning.	Urban Planning and Development	While the project faced challenges, including concerns over data privacy and public trust, it provided valuable insights into how Digital Twins can be leveraged to plan and manage smart cities [64].
Mojow Autonomous Solutions Inc.	Development of an AI Data Recording Kit (Eye-Box) using Digital Twin technology to simulate real-time farm operations, optimizing farming data collection and decision-making.	Agriculture Technology	Enhanced productivity and resource management through Digital Twin simulations, reducing labor shortages and improving sustainability [124].
Vancouver’s Healthcare Digital Twin for Hospital Efficiency	Vancouver Coastal Health implemented a Digital Twin of its hospital operations to improve efficiency and patient care. The Digital Twin simulated hospital workflows, patient flows, and resource allocation, allowing for the optimization of processes such as bed management, emergency department operations, and patient discharge procedures.	Healthcare	The Digital Twin led to significant improvements in hospital efficiency, reduced patient wait times, and optimized resource utilization. This pilot project is now being considered for broader implementation across other healthcare facilities in Canada [25,32].
Hydro-Québec CAMP Project (Centre for Analysis and Predictive Maintenance	In Hydro-Québec’s mission, Digital Twin technology is primarily used in the CAMP Project (Centre for Analysis and Predictive Maintenance) to monitor and maintain generating units, dams, and auxiliary systems.	Energy (Hydropower)	The Digital Twin technology was utilized for the predictive maintenance of generating units, dams, and auxiliary systems; improving asset reliability; and reducing outages [52].
Digital Twin of Bioreactor	This project integrates Digital Twin technology with AI to accelerate the design and optimize the operations of bioreactors, specifically for the production of complex biologics like exosomes and viral vectors.	Biopharmaceuticals	Improved bioreactor performance, faster production of biologics, and optimized manufacturing processes through AI and Digital Twin technology [26].
Canadian SME Adoption Roadmap	Focused on helping small and medium-sized enterprises (SMEs) in Canada adopt Digital Twin technologies to improve manufacturing processes.	Manufacturing	SMEs achieved enhanced efficiency and product quality by using Digital Twin simulations for real-time monitoring and process optimization, resulting in reduced downtime and improved operational decision-making [33].
Eglinton Crosstown West Extension	Application of Digital Twin for complex transit infrastructure, modeling utility services and resolving potential conflicts during construction.	Transportation	Enhanced safety, minimized delays, cost savings, and improved efficiency in large-scale public infrastructure projects [125].

, detailing the project, project overview, sector, and outcome.

7. Future Directions

Digital twins are evolving, yielding new technological and scientific breakthroughs that direct development towards more seamless and autonomous solutions. This section reviews key developments and emerging trends that can be discussed based on the convergence of advanced technologies such as the Internet of Things (IoT) and Artificial Intelligence along with outlining research opportunities to address existing gaps. These ideal objectives from here aim for more exact, interactive, and scalable Digital Twins to increasingly serve the growing requirements of myriad sectors.

7.1. Advanced Technology Integration

The future of Digital Twins (DTs) is set along the strategic direction of Advanced Technologies. Two key technology trends, namely, the Internet of Things (IoT) and Artificial Intelligence (AI), have empowered DT benefits in a substantial way. Though the Internet of Things (IoT) connects and controls physical assets, Artificial Intelligence provides intelligent automation for deeper understanding and autonomy critical in making complex decisions dynamically as part of Digital Twin use cases. The more recent work has involved designing the specific frameworks to be used to connect the IoT with AI, which can use deep learning models delivering even more effective and autonomous answers. Further, semantic technologies such as knowledge representation and inductive (semantic) utilizations of knowledge graphs offer more intricate cognitive diagramming in the presence or nonappearance of DL, which can be guilefully incorporated into DTs for deeper insights and advanced control strategies.

7.1.1. IoT and AI Convergence

The Internet of Things (IoT) and AI are two of the most up-to-date technological movements that are critical in the advancement of DTs. Essentially, the IoT represents the idea that everyday objects can be connected and controlled remotely. Experts have said that DTs and IoT are closely related. The main idea behind this is that the DT is a virtual copy of things, but it cannot keep up with the real world, with IoT being the method for monitoring and changing the state of things, often using virtual models. The IoT works together with AI to assist in the autonomous decision-making processes pertaining to the Digital Twin. AI is able to provide insight to the Twin, as well as make decisions on behalf of the Twin. AI technology is constantly improving, and with the inclusion of machine learning, it allows autonomous systems to make more informed and more accurate decisions. The constant feedback loop created by the IoT network refines the information learned and decisions made by the AI, resulting in an overall improvement in the Digital Twin and its real-life entity [34].

Recent research into the integration of AI and DTs has focused on developing customized architectures and frameworks. Deep learning approaches like neural networks and fuzzy logic implementations are increasingly being adopted to handle complex, non-linear relationships in DT data processing [27]. For real-time decision-making, frameworks combining a five-layer Cyber–Physical System (5C-CPS) architecture with deep learning algorithms have shown promise in enhancing system automation and responsiveness [30]. These frameworks typically employ Bi-GRU models and self-attention mechanisms for improved accuracy in data processing and decision-making, achieving up to 99.73% accuracy in intrusion detection scenarios [28]. On the IoT communication side, standard and new protocols are being developed to ensure seamless data exchange between physical assets and their digital counterparts. The integration of the IoT with cloud and edge computing infrastructures has become particularly significant, with edge computing gaining traction for its ability to process data closer to the source, thereby reducing the latency in DT implementations [35].

7.1.2. Knowledge Representation and Semantic Technologies

Recent advances in knowledge representation for Digital Twins have focused on semantic web technologies and knowledge graphs as effective frameworks for modeling complex system relationships. Semantic models employing ontologies like SAREF, SAREF4BLDG, and the Flow Systems Ontology (FSO) enable a detailed representation of the building components and their interactions, supporting enhanced interoperability between different data sources [126]. The integration of semantic technologies allows for the transformation of traditional structured data into rich knowledge graphs that capture both the physical relationships and operational dynamics of building systems. For example, recent work has demonstrated how ontology-based semantic models can automatically generate and calibrate building energy models while maintaining a high accuracy across multiple measuring devices [53]. Knowledge graphs particularly excel in representing highly interconnected building data, as they can model complex relationships between components, sensors, and control systems while supporting reasoning mechanisms and logic-based methods [37]. This approach enables more sophisticated queries and analytics compared to traditional data structures, allowing Digital Twins to not only store data but also infer new knowledge about system behavior and relationships. The semantic layer also facilitates the integration of disparate data sources and enables automated reasoning about building systems, supporting more intelligent decision-making and control strategies.

7.2. Research Gaps and Development Opportunities

A key feature of the IoT is its use of smart sensors, which are currently too expensive to apply in offline data models. This means that solutions derived from DT can be tested before investing in the real infrastructure. The integration of IoT with cloud and edge computing infrastructures is a significant trend in current DT implementations. Cloud computing is being leveraged for its vast storage and processing capabilities, allowing DTs to handle and analyze large volumes of historical data. Simultaneously, edge computing is gaining traction for real-time data processing closer to the device and the data source, which will reduce the latency and decree the time response.

In the near future, it is envisioned that IoT infrastructures will evolve to use integrated models. Often, the IoT is too complex to test while systems work is in progress, as it involves multiple distinct systems. Currently, many testbeds and experiments rely on the explicit isolation of the environment, which challenges the main purpose of monitoring and control. However, such models may be evaluated by using the DT of that system. This would require a DT of the IoT model, which would itself act as a test platform for the real environment. This represents a method for the second-order control of complex systems, which has long been a goal for control planners, as it is often too difficult to evaluate control solutions in the real world. Current trends in DT modeling are moving towards more sophisticated data-driven approaches. While traditional DTs often relied heavily on physics-based models, current implementations frequently combine these with machine learning techniques to create hybrid approaches. Neural networks, particularly deep learning models, are increasingly popular due to their ability to handle complex, non-linear relationships in data. Some implementations are also incorporating fuzzy logic to improve the interpretability of these complex models while maintaining high performance.

With the constant improvement in and development of both IoT and AI technologies, it can be said that there is large potential for Digital Twins in all application areas. This includes constructing Twins of an entire manufacturing system in an attempt to analyze and improve its efficiency. At the other end of the spectrum, this can mean constructing a Twin of a single component to analyze its detailed performance in relation to its real-life counterpart. An interesting development in IoT technology is the use of nano-sized Digital Twins to monitor real-life entities at a cellular or molecular level. This would prove useful in the medical and pharmaceutical field in attempting to diagnose and cure diseases [54].

Advanced simulation techniques are being employed to predict system behavior based on the wealth of real-time and historical data provided by IoT devices. Furthermore, optimization algorithms, such as genetic algorithms, are being used to continuously fine-tune system parameters. Another emerging technique is the use of transfer learning, where knowledge gained from simulations or from one system is applied to improve the performance of DTs in related systems.

8. Conclusions

As we discussed in this paper, DTs provide a significant opportunity to push the management and optimization of smart grids forward. This review highlighted some of the diverse possible applications of this technology from asset management to disaster response, and its potential to enhance grid efficiency, reliability, and sustainability. It addressed the many challenges that still persist, particularly in data management, standardization, and cost-effectiveness, and the rapid advancements in IoT, AI, and cloud computing. The case studies from Canada demonstrate that DTs are moving beyond theoretical concepts to practical implementations, showcasing real benefits across various sectors. As this technology matures, we anticipate more widespread adoption, leading to more resilient, adaptive, and intelligent power grid systems. Future research should focus on developing more sophisticated modeling techniques in order to improve the interoperability between different DT systems and address ethical and security concerns. The integration of DTs with new emerging technologies like blockchain and 6G also presents exciting opportunities for further innovation.