Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (333 pages)

Ausgabe: First edition.

ISBN: 9780443140716 , 0443140715

Anmerkung: Front Cover -- Digital Twin Technology for the Energy Sector -- Copyright Page -- Dedication -- Contents -- List of contributors -- About the editors -- Foreword -- Preface -- Acknowledgments -- Reviewers -- 1 Introduction to emerging technologies for acceleratingthe energy transition -- 1.1 Introduction -- 1.2 Energy transition: overview and importance -- 1.2.1 Understanding energy transition -- 1.2.2 Significance of energy transition -- 1.2.3 Key drivers and challenges -- 1.3 Emerging technologies in energy transition -- 1.3.1 Definition and characteristics of emerging technologies -- 1.3.2 Categories of emerging technologies -- 1.4 Renewable energy technologies -- 1.4.1 Solar energy -- 1.4.2 Wind energy -- 1.4.3 Hydropower -- 1.4.4 Biomass and bioenergy -- 1.4.5 Geothermal energy -- 1.4.6 Ocean energy -- 1.5 Energy storage technologies -- 1.5.1 Batteries -- 1.5.2 Pumped hydro storage -- 1.5.3 Thermal energy storage -- 1.5.4 Hydrogen energy storage -- 1.6 Smart grid and grid integration technologies -- 1.6.1 Smart grid concepts and components -- 1.6.2 Demand response technologies -- 1.6.3 Grid integration of renewable energy sources -- 1.6.4 Advanced metering infrastructure -- 1.7 Electrification and decentralization -- 1.7.1 Electrification of transportation -- 1.8 Conclusions, lessons learned, and future perspectives -- Acknowledgment -- References -- 2 Digital twin technology: fundamental aspects and advances -- 2.1 Introduction -- 2.2 Definition and typology -- 2.2.1 Definition -- 2.2.2 Typology -- 2.2.2.1 Prototype digital twin -- 2.2.2.2 Monitoring digital twin -- 2.2.2.3 Predictive digital twin -- 2.2.2.4 Prescriptive digital twin -- 2.2.2.5 Imaginary digital twin -- 2.2.2.6 Autonomous digital twin -- 2.2.3 Complexity of digital twins -- 2.3 Fundamental aspects of digital twins -- 2.3.1 Data acquisition -- 2.3.1.1 Sensor systems. , 2.3.1.2 Communication systems -- 2.3.2 Digital model -- 2.3.2.1 Model visualization -- 2.3.2.2 Mathematical/behavior model -- 2.3.3 Simulation and visualization -- 2.3.4 Feedback information -- 2.4 Classification of digital representation -- 2.5 Digital model -- 2.6 Digital shadow -- 2.7 Digital twin -- 2.8 Conclusions -- Acknowledgments -- AI disclosure -- References -- 3 Cybersecurity, digital privacy, and modeling aspects of digital twins -- 3.1 Introduction -- 3.2 System modeling framework -- 3.2.1 Overview of modeling -- 3.2.2 Framework -- 3.2.3 Examples -- 3.3 Digital twins under the context of cybersecurity and privacy considerations -- 3.3.1 Security of the model -- 3.3.2 Security by the model -- 3.3.3 Managing DTMs in tightly regulated environments -- 3.3.4 Privacy and encryption for collaborative digital twin development -- 3.3.5 Cloud-based deployment: trusted execution environment on Google Cloud for digital twin development -- 3.3.5.1 Overview of trusted execution environment -- 3.3.5.2 Comparison of various privacy-enhancing technologies -- 3.4 Frameworks and assessment models -- 3.4.1 Digital twin maturity model -- 3.4.2 Lockheed Martin digital twin maturity model -- 3.4.3 RAMI 4.0 -- 3.4.4 D-Arc -- 3.4.5 Artificial intelligence trust framework and maturity model -- 3.4.5.1 Secure artificial intelligence framework+ artificial intelligence trust maturity model -- 3.5 Use-cases -- 3.5.1 Use of digital twins to enhance operator training capabilities -- 3.5.2 Use of digital twins to increase grid observability -- 3.5.3 Use of digital twins to optimize and increase operational efficiency -- 3.5.4 Use of digital twins to increase cyber threat analysis capabilities -- Appendix A: Operationalizing the artificial intelligence trust framework and maturity model -- Appendix B: A deep-dive on Security goals pertinent to SAIF -- References. , 4 Digital twin technology in the electrical power industry -- 4.1 Introduction -- 4.2 Related works -- 4.3 Digital twin architecture -- 4.4 Methodologies and enabling technologies -- 4.4.1 Methodologies -- 4.4.2 Enabling technologies -- 4.5 Digital twin applications -- 4.5.1 Digital twins for full lifecycle management -- 4.5.2 Digital twin for visualization and monitoring -- 4.5.3 Digital twin for control -- 4.6 Case studies -- 4.6.1 Digital twins for inspection -- 4.6.2 Digital twin wind farm for education -- 4.7 Conclusion -- Acknowledgment -- References -- 5 Digital twin technology in microgrid systems -- 5.1 Introduction -- 5.2 Related works -- 5.3 Digital twin architecture in microgrid systems -- 5.3.1 Digital twin design requirements -- 5.3.2 Digital twin modeling and rrocessing -- 5.3.3 Real-time data connection for microgrid -- 5.4 Digital twin applications for microgrid components -- 5.4.1 Solar power -- 5.4.2 Wind energy -- 5.4.3 Biogas energy -- 5.4.4 Battery -- 5.4.5 Electric vehicle -- 5.4.6 Power converters -- 5.5 Digital twin applications in microgrid systems -- 5.5.1 Forecasting -- 5.5.2 Management and monitoring -- 5.5.3 Fault detection -- 5.5.4 Cyber security -- 5.6 Conclusion -- Acknowledgment -- References -- 6 Digital twin in design and control optimization of marine renewable energy and offshore wind energy systems -- 6.1 Introduction -- 6.2 Foundations of marine renewable energy systems -- 6.3 Design, Control, and optimization aspects of marine renewable and offshore wind energy systems -- 6.3.1 Frequency domain -- 6.3.2 Time domain -- 6.4 Digital twin engineering for marine renewable energy systems -- 6.5 Digital twin industrial, educational, and work safety aspects -- 6.6 Conclusion and future work -- References -- 7 Simulation and visualization aspects of a digital twin for wind farms. , 7.1 Introduction to cosimulation and model exchange -- 7.1.1 Understanding model -- 7.1.2 Definition of cosimulation and model exchange -- 7.1.2.1 Cosimulation -- 7.1.2.2 Model exchange -- 7.2 Standards, tools, and frameworks supporting cosimulation and/or model exchange -- 7.2.1 Functional mock-up interface -- 7.2.2 High-level architecture -- 7.3 Combining frameworks and standards -- 7.4 Wind turbine cosimulation use case -- AI disclosure -- References -- 8 Enhancing wind farm energy prediction through digital twin integration -- 8.1 Introduction -- 8.2 The role of digital twins in wind energy -- 8.2.1 Operator training -- 8.2.2 Monitoring and visualization -- 8.2.3 Predictive maintenance -- 8.2.4 Energy production forecasting -- 8.2.5 Optimizing turbine performance -- 8.2.6 Layout and site design -- 8.2.7 Remote operation and control -- 8.3 Prediction of energy production in wind farms -- 8.4 Digital twin implementation and case studies -- 8.5 Benefits and challenges -- 8.5.1 Predeployment digital twins: benefits and challenges -- 8.5.1.1 The benefits of predeployment digital twins include -- 8.5.1.2 The challenges of predeployment digital twins include -- 8.5.2 Postdeployment digital twins: benefits and challenges -- 8.5.2.1 The benefits of postdeployment digital twins include -- 8.5.2.2 The challenges of predeployment digital twins include -- AI disclosure -- References -- 9 Digital twin technology in solar energy -- 9.1 Introduction to digital twin technology in solar energy -- 9.1.1 Definition of digital twin technology for solar energy uses, solar systems, and applications -- 9.1.2 Classification and working principles of digital twins in solar energy -- 9.1.3 Enabling technologies for digital twins in solar energy -- 9.2 Application of digital twin for solar plants -- 9.2.1 Solar potential analysis and solar energy planning strategies. , 9.2.1.1 Accurate solar potential assessment -- 9.2.1.2 Virtual testing and experimentation -- 9.2.1.3 Performance monitoring and optimization -- 9.2.1.4 Risk mitigation and resilience planning -- 9.2.1.5 Urban planning and solar integration -- 9.2.1.6 Microgrid and energy storage optimization -- 9.2.1.7 Lifecycle management and predictive maintenance -- 9.2.1.8 Public awareness and stakeholder engagement -- 9.2.2 Solar plants maintenance and management -- 9.2.2.1 Lower analytics latency -- 9.2.2.2 Closed-loop integration of analytics and local control -- 9.2.2.3 Faster evolution of the digital twin -- 9.3 Data collection and analysis for digital twins in solar energy -- 9.4 Challenges and opportunities for digital twin technology in solar energy -- 9.5 Conclusion -- Acknowledgment -- Disclosure -- References -- 10 Digital twins for hydropower applications -- 10.1 Introduction -- 10.2 Hydroelectric power plants -- 10.2.1 Hydroelectric power plant classification -- 10.2.1.1 Impoundment facility -- 10.2.1.2 Diversion facility -- 10.2.1.3 Pumped storage facility -- 10.2.2 Hydropower main components -- 10.2.2.1 Turbine -- 10.2.2.2 Generator -- 10.2.2.3 Transformer -- 10.3 Digital twin in Hydroelectric power generation -- 10.3.1 Modeling of digital twin in hydropower -- 10.3.1.1 Turbine system model -- 10.3.1.2 Turbine speed (frequency) control system -- 10.3.1.3 Torque and water flow -- 10.3.1.4 State-space model for nonelasticity water system dynamics -- 10.3.1.5 State-space model for elastic water flows -- 10.3.1.6 Proportional-integral-derivative controller for shaft speed -- 10.3.1.7 Hydro-generator model -- 10.4 Summary -- 10.4.1 Benefits of digital twins in hydropower -- 10.4.2 Enhancing flexibility and efficiency in the value chain -- 10.4.3 Challenges and future prospects -- Acknowledgement -- References. , 11 Digital twin technology for energy flexibility and saving.

Weitere Ausg.: ISBN 9780443140709

Weitere Ausg.: ISBN 0443140707

Sprache: Englisch