Enabling coordination in energy communities: A Digital Twin model

Bâra, Adela; Oprea, Simona-Vasilica

doi:10.1016/j.enpol.2023.113910

Cited by 13 publications

(5 citation statements)

References 45 publications

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“…Ensuring the seamless integration of this diverse data into DT models remains critical for accurate and reliable performance monitoring and predictive analytics. Large-scale computation Computational infrastructure, fog-, cloud-, and edge-computing [63,[79][80][81]88,90,112] Cyber security issues Advanced cyber-security protocols [22,85,92,106,117,118] Social impact Redistribute the workplace with minimum effects on employment [22,103,107,116,119] Another key challenge is model fidelity and validation. DT models must accurately represent the complex behaviour of offshore wind assets, including dynamic environmental conditions, structural loads, and system interactions [115].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Establishing common data formats, communication protocols, and interoperable platforms will facilitate seamless collaboration and data exchange among stakeholders, ultimately enhancing efficiency and innovation. Additionally, concerns arise regarding the safeguarding of physical and virtual infrastructure against cyber-attacks [117,118], as well as potential social ramifications stemming from widespread DT applications in the future [119].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Chen,

Liu,

et al. 2024

Energies

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This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Chen,

Liu,

et al. 2024

Energies

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“…In the practical domain, the imperative to precisely forecast and optimize the energy consumption of buildings emerges as an exigent issue that commands immediate and rigorous attention [14], Moreover, a pronounced disparity exists between the theoretical constructs of Digital Twin (DT) technology and its pragmatic applications within the energy sector, delineating a significant chasm in its current utility and potential exploitation. Bâra and Oprea [15] attempted to develop and implement a DT model to enhance coordination within energy communities. Bâra and Oprea [15] conducted a thorough analysis that highlighted the necessity for additional research into integrating elements like social dynamics and economic frameworks into Digital Twins (DTs) to enhance the energy management system's comprehensiveness.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Bâra and Oprea [15] attempted to develop and implement a DT model to enhance coordination within energy communities. Bâra and Oprea [15] conducted a thorough analysis that highlighted the necessity for additional research into integrating elements like social dynamics and economic frameworks into Digital Twins (DTs) to enhance the energy management system's comprehensiveness. The study advocates for future research on the scalability and adaptability of DTs across diverse energy communities and settings.…”

Section: Problem Definitionmentioning

confidence: 99%

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Tahmasebinia,

Lin,

et al. 2024

Buildings

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The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.

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“…This strategy is based on the concept of creating a digital twin, a virtual representation of physical processes, to facilitate real-time decision-making. Through this digital twin, smart decisions can be made collaboratively by humans and machines, fostering seamless cooperation between the two entities within the manufacturing environment [3][4][5]. Furthermore, Industry 4.0 is characterized by the widespread adoption of numerous new technologies in the industrial sector.…”

Section: Introductionmentioning

confidence: 99%

Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis

Ciucu-Durnoi,

Delcea,

Stănescu

et al. 2024

Sustainability

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The rapid advancement of technology has led to significant milestones in industrial progress, surpassing previous capabilities and presenting new challenges for adaptation. The current phase of industrial revolutions is characterized by accelerated technological development, particularly in automation and digitalization. For instance, the global industrial robotics market was valued at approximately USD 43.0 billion in 2022 and is projected to reach USD 70.6 billion by 2028. The integration of human labor alongside robotic machinery, though a tangible reality, may still seem abstract in certain regions. Despite the recent announcement of the fourth industrial revolution, Industry 5.0 has quickly emerged as the new standard toward which industries aspire. This study performs a bibliometric analysis of articles published between 2020 and 2023 that explores the implications of these two industrial revolutions and the transition between them. Using the Clarivate Analytics’ Web of Science Core Collection, the study identifies 154 articles using the Biblioshiny package in R, which simultaneously discuss Industry 4.0 and Industry 5.0 within their titles, abstracts, or keywords. An impressive annual growth rate of 119.47% among the published papers included in the dataset underlines the interest of the research community in this field. Additionally, key findings include the identification of prominent sources, prolific authors, highly cited articles and their content, as well as common research themes explored across the analyzed papers. Among the most relevant sources in terms of the number of publications, the journal Sustainability plays a key role, holding the first position, followed by Applied Sciences, and Sensors. In terms of motor themes, digital transformation, artificial intelligence, the Internet of Things, and smart manufacturing have been found to play a key role. As a result, the present research contributes to understanding the rapid evolution from Industry 4.0 to Industry 5.0, highlighting key trends, influential research, and emerging themes that are shaping the future of industrial advancements.

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Enabling coordination in energy communities: A Digital Twin model

Cited by 13 publications

References 45 publications

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis

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