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For Authors
Submit a ProposalDigital Twin Technology for Solar Concentrators
One Line Description
Discover how to lead the renewable energy transition with this definitive, expert-led guide on integrating digital twins, AI, and predictive analytics with advanced solar concentrator systems to maximize operational efficiency and accelerate global decarbonization.
Description
The global transition toward intelligent and sustainable energy systems has created a growing demand for technologies that can improve the efficiency, reliability, and operational flexibility of renewable energy infrastructure. This book presents a comprehensive exploration of how digital twin frameworks can be integrated with solar concentrator technologies to transform the design, monitoring, optimization, and maintenance of solar energy systems. It covers the fundamentals of solar thermal technologies and solar concentrators before progressing to advanced concepts involving digital twins, artificial intelligence, machine learning, cloud computing, predictive analytics, and cyber-physical systems. Providing expert insights, this volume demonstrates how parabolic trough collectors, linear Fresnel systems, heliostat fields, parabolic dish concentrators, and Scheffler solar concentrators can operate alongside digital twin implementations for real-time monitoring, predictive maintenance, performance optimization, and intelligent control. Featuring contributions from leading researchers and industry professionals, the book includes practical case studies, simulation methodologies, mathematical models, and real-world applications. It serves as a valuable reference for researchers, engineers, industry practitioners, and students seeking to understand and implement next-generation digital twin-enabled renewable energy systems that support global sustainability and decarbonization goals.
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The global transition toward intelligent and sustainable energy systems has created a growing demand for technologies that can improve the efficiency, reliability, and operational flexibility of renewable energy infrastructure. This book presents a comprehensive exploration of how digital twin frameworks can be integrated with solar concentrator technologies to transform the design, monitoring, optimization, and maintenance of solar energy systems. It covers the fundamentals of solar thermal technologies and solar concentrators before progressing to advanced concepts involving digital twins, artificial intelligence, machine learning, cloud computing, predictive analytics, and cyber-physical systems. Providing expert insights, this volume demonstrates how parabolic trough collectors, linear Fresnel systems, heliostat fields, parabolic dish concentrators, and Scheffler solar concentrators can operate alongside digital twin implementations for real-time monitoring, predictive maintenance, performance optimization, and intelligent control. Featuring contributions from leading researchers and industry professionals, the book includes practical case studies, simulation methodologies, mathematical models, and real-world applications. It serves as a valuable reference for researchers, engineers, industry practitioners, and students seeking to understand and implement next-generation digital twin-enabled renewable energy systems that support global sustainability and decarbonization goals.
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Author / Editor Details
Surajit Mondal, PhD is an academic and researcher in renewable energy systems, digital technologies, and sustainable engineering. He is associated with the Amity School of Engineering and Technology at Amity University, Bengaluru, India. He has published extensively in reputed journals and actively contributes to research, innovation, and technology development in clean energy systems.
Surajit Mondal, PhD is an academic and researcher in renewable energy systems, digital technologies, and sustainable engineering. He is associated with the Amity School of Engineering and Technology at Amity University, Bengaluru, India. He has published extensively in reputed journals and actively contributes to research, innovation, and technology development in clean energy systems.
Paramjeet Singh Paliyal, PhD works with the Department of Electrical and Electronics Engineering in the College of Engineering at Roorkee University. His research focuses on solar thermal systems, solar concentrators, renewable energy technologies, energy efficiency, and sustainable engineering solutions. He has contributed significantly to research in solar energy harvesting, thermal systems, and advanced renewable energy applications.
Sachin Sharma, PhD is the Centre Head at the Bajaj Engineering Skills Training at the University of Petroleum and Energy Studies, Dehradun, India. He has extensive experience in research, academic leadership, and interdisciplinary technology development. His expertise spans thermal engineering, sustainable technologies, renewable energy systems, and advanced manufacturing.
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Table of Contents
Preface
1. Solar Thermal Technology – A Brief
Paramjeet Singh Paliyal and Surajit Mondal
References
2. Introduction to Digital Twin Technology
Sanidhya Sarthak
2.1 Introduction to Digital Twin Technology
2.2 Core Components of Digital Twin Technology
2.2.1 Physical Entity and Sensor Integration
2.2.2 Digital Model and Data Representation
2.2.3 IoT and Cloud Computing for Real-Time Synchronization
2.2.4 AI, Machine Learning, and Predictive Analytics
2.2.5 User Interface and Visualization Tools
2.3 Solar Concentrator Types
2.3.1 Tower (Heliostat Field) Systems
2.3.2 Parabolic Trough Collectors
2.3.3 Linear Fresnel Collectors
2.3.4 Parabolic Dish Systems
2.4 Digital Twin for Solar Concentrators
2.5 Digital Twin Applications by Concentrator Type
2.5.1 Parabolic Trough Systems
2.5.2 Fresnel Collector Systems
2.5.3 Tower (Heliostat) Systems
2.5.4 Parabolic Dish Systems
2.6 Optimization Strategies
2.7 Real-World Case Studies
2.8 Mathematical Models and Equations
2.9 Future Directions and Challenges
2.10 Conclusion
Bibliography
3. Solar Concentrators: A Study Pertaining to Advancements and Challenges in the Current Scenario
Deepak Singh Karki and Vinita Sirala
3.1 Introduction
3.2 Classification of Solar Concentrators
3.2.1 Classification Based on Optical Geometry
3.2.2 Classification Based on Concentration Ratio
3.2.3 Classification Based on Tracking Mechanism
3.2.4 Classification Based on Application
3.2.5 Classification Based on Structural Design
3.3 Recent Advancements in Solar Concentrator Technology
3.3.1 Advanced Materials for Enhanced Optical Efficiency
3.3.2 Improvements in Solar Tracking Mechanisms
3.3.3 Integration of Digital Twin Technology for Performance Optimization
3.3.4 Advanced Heat Storage Solutions
3.3.5 Hybridization with Photovoltaics and Other Renewable Technologies
3.3.6 Smart Sensors and AI for Performance Optimization
3.3.7 Self-Healing and Adaptive Optics
3.4 Conclusion
3.5 Future Scope
References
4. Basics of Solar Concentrators
Ayushman Srivastav, Rajesh Maithani and Sachin Sharma
4.1 Introduction
4.2 Principles of Solar Concentration
4.2.1 Mechanism of Solar Concentration
4.2.2 Concentration Ratio and Its Importance
4.2.3 Impact of Concentration on System Efficiency
4.2.4 Optical Efficiency and Losses
4.3 Types of Solar Concentrators
4.4 Advanced Configurations: Multi-Surface and Multi-Element Combinations
4.5 Applications and Advantages of Solar Concentrators
4.6 Challenges and Considerations
4.7 Future Prospects
4.8 Conclusion
References
5. Solar Scheffler Concentrators: The Future of Sustainable Cooking
Amit Kumar, Ashish Karn and Varun Pratap Singh
5.1 Introduction
5.2 Solar Scheffler Concentrator
5.2.1 Design and Structure
5.3 Cooking with Solar Scheffler Concentrator
5.4 Case Studies on Scheffler Dish-Based Solar Cooking Systems
5.5 Results and Discussion
5.6 Conclusion
Acknowledgements
References
6. Digital Twin-Based Real-Time Solar Concentrator Control and Monitoring System
Aruna Pant and Adesh Kumar
6.1 Introduction
6.2 The Digital Twin Concept
6.3 Elements of the System for Real-Time Control and Monitoring
6.3.1 System Solar Concentrators
6.3.2 Digital Twin Framework
6.3.3 System for Data Acquisition and Communication
6.3.4 Control System
6.3.5 Interface and Functional Block
6.4 Uses for Solar Concentrators
6.4.1 Use in Photovoltaic Systems
6.4.2 Applications in Solar Thermal Systems
6.5 Cutting Edge Solar Concentration Technologies
6.5.1 Integration of Digital Twins
6.5.2 Real-Time Control Systems Based on FPGA
6.5.3 Leveraging AI and Machine Learning for Optimization
6.6 Performance Evaluation
6.6.1 Solar Tracking Performance Evaluation
6.6.2 Power Output with vs. without Tracking
6.6.3 Digital Twin Data Exchange Latency
6.6.4 Solar Panel Temperature Monitoring
6.7 Conclusions
References
7. Environmental and Health Impacts of Utilizing Solar PV Panels
Ayushman Srivastav, Rajesh Maithani and Sachin Sharma
Introduction
Energy and Resource-Intensive Manufacturing
Raw Materials
Energy Consumption
Chemical Usage
Land Use and Habitat Displacement
Large-Scale Installations
Soil Degradation
Variability and Intermittency of Solar Energy
Intermittency and Grid Stability
Energy Storage and Environmental Impacts
Disposal Challenges and Hazardous Materials
Presence of Hazardous Materials
Recycling Challenges
Landfill Disposal and Leachate Generation
Mitigating Negative Environmental Impacts
Improved Recycling Technologies
Extended Producer Responsibility (EPR)
Second-Life Applications
Mitigation Measures for Wildlife Impact
Lifecycle Assessment (LCA)
Future Directions for Research and Development
Conclusion
References
8. Integration of IoT, AI, and Digital Twin Technology for Smart Solar Concentrators
Paramjeet Singh Paliyal and Surajit Mondal
8.1 Introduction
8.2 Fundamentals of Solar Concentrators
8.2.1 Thermal and Optical Performance Considerations
8.2.2 Limitations of Conventional Concentrator Systems
8.3 Digital Twin Technology: Concepts and Architecture
8.4 Integration Framework: IoT, AI, and Digital Twin in Solar Concentrators
8.5 Conclusion
References
9. A Digital Twin-Based NLP Chatbot for Self-Assessment and Psychoeducation of Obsessive-Compulsive Disorder
Shweta Gupta, Adesh Kumar, Surajit Mondal, Niraj Kumar and Ompal
9.1 Introduction
9.2 Symptoms of OCD
9.3 Types of OCD
9.4 Causes of OCD
9.5 Treatment of OCD
9.6 Management
9.7 SELF Assessment of OCD
9.7.1 AI-Based Treatments
9.7.2 Real World Scenario
9.8 NLP Chatbot for Psychoeducation and Self-Assessment of OCD
9.9 Conclusions
References
10. FPGA Performance Analysis of LDPC Codes for Communication Systems
Aakanksha Devrari, Adesh Kumar and Amit Kumar
10.1 Introduction
10.2 Related Work
10.3 LDPC Codes
10.3.1 LDPC Encoder and Decoder
10.4 Hardware Architecture
10.5 Results and Discussion
10.6 Conclusions
References
11. Optimizing Renewable Energy Management with the Use of Digital Twin Technology: Insights from NICE China Energy
Supriya, Vriti Khandelwal, Kushagar Sharma, Vasu Sharma and Kashish Kalra
11.1 Introduction
11.1.1 Renewable Energy Generation with Digital Twin
11.1.1.1 Wind Energy
11.1.1.2 Solar Energy
11.1.1.3 Marine Renewable Energy (MRE)
11.2 Digital Twin in Energy Transmission and Transformation
11.2.1 Power Transformers
11.2.2 Gas-Insulated Switchgear (GIS)
11.2.3 Power Cables
11.3 Digital Twin in Renewable Energy Storage Systems
11.3.1 Lithium-Ion Batteries (LIBs)
11.3.2 Fuel Cells (PEMFC & SOFC)
11.4 Advantages and Challenges of Digital Twin Technology
11.4.1 Advantages
11.4.2 Challenges
11.5 Case Study: Digital Twin Technology in Power Plant Optimization
11.5.1 Overview
11.5.2 Key Components of the DT Power Plant
11.5.3 Key Findings and Benefits of China Energy’s Digital Twin Implementation
11.6 Conclusion
Bibliography
12. Enhancing Solar PV Efficiency through a Digital Twin Framework and Machine Learning Models
Nitin Kumar, Rupendra Kumar Pachauri and Piyush Kuchhal
12.1 Introduction
12.2 Literature Review
12.3 Experimental Location and Methodology
12.4 Result and Discussion
12.5 Conclusion
References
13. Digital Twin-Enabled Solar Concentrators: A Cyber-Physical Approach to Intelligent Renewable Energy
Laxman Singh Rana, Lakhan Singh, Surabhi Chauhan, Kundan Singh and Deepak Singh Karki
13.1 Introduction
13.2 Fundamentals of Digital Twin Technology
13.2.1 Definition and Concept
13.2.2 Core Components of a Digital Twin System
13.2.3 Types of Digital Twins
13.2.4 Technological Enablers
13.2.5 Benefits of Digital Twin Technology
13.3 Solar Concentrator Systems: An Overview
13.3.1 Principle of Operation
13.3.2 Types of Solar Concentrator Systems
13.3.2.1 Parabolic Trough Collectors (PTCs)
13.3.2.2 Linear Fresnel Reflectors (LFRs)
13.3.2.3 Parabolic Dish Concentrators
13.3.2.4 Heliostat Field with Central Receiver (Solar Tower)
13.3.3 Performance Considerations
13.3.4 Why Solar Concentrators Need Digital Twins
13.3.5 Summary of System Characteristics
13.4 Digital Twin Architecture for Solar Concentrators
13.4.1 System Architecture
13.4.2 Communication Technologies
13.5 Applications of Digital Twins in Solar Concentrators
13.5.1 Real-Time Monitoring and Diagnostics
13.5.2 Predictive Maintenance
13.5.3 Optimization and Control
13.5.4 Design and Simulation
13.6 Case Studies
13.6.1 Digital Twin for Heliostat Field Optimization
13.6.2 Predictive Maintenance of Parabolic Troughs
13.7 Challenges and Considerations
13.8 Future Outlook and Research Directions
13.9 Conclusion
Bibliography
14. Artificial Neural Networks for Solar Panel Tilt Angle Optimization: A Power Generation Analysis
Nitin Kumar, Rupendra Kumar Pachauri and Piyush Kuchhal
14.1 Introduction
14.1.1 Literature Review
14.2 ANN and Tilt Angle Relation with Latitude
14.3 The Process of Data Curation
14.4 Test Metric
14.5 Results and Discussion
Conclusion
Nomenclature
References
15. Machine Learning and Artificial Intelligence in Digital Twins
Sampath Emani and Gurunadh Velidi
15.1 Introduction to Digital Twins and Solar Concentrators
15.1.1 Overview of Digital Twin Technology
15.1.2 Role of Digital Twins in Solar Concentrator Systems
15.1.3 Key Components of Solar Digital Twin Architectures
15.1.4 Scope of This Chapter
15.1.5 Organization of the Subsequent Sections
15.2 Machine Learning and Artificial Intelligence Fundamentals in Digital Twins
15.2.1 Introduction to Machine Learning and Artificial Intelligence in Digital Twins
15.2.2 Supervised Learning in Digital Twins
15.2.2.1 Applications in Solar Concentrators
15.2.3 Unsupervised Learning for Operational Insights
15.2.3.1 Applications in Solar Concentrators
15.2.4 Reinforcement Learning for Adaptive Control
15.2.4.1 Applications in Solar Concentrators
15.2.5 Physics-Informed Machine Learning (PIML)
15.2.5.1 Applications in Solar Concentrators
15.2.6 Comparative Analysis of Learning Paradigms
15.2.7 Key Considerations for Machine Learning Integration
15.2.8 Summary
15.3 State-of-the-Art AI Techniques in Solar Energy Systems
15.3.1 Introduction
15.3.2 Real-World Case Studies and Practical Implementations
15.3.3 Summary
15.4 Current Trends in AI-Driven Digital Twins for Solar Concentrators
15.4.1 Introduction
15.4.2 Edge AI for Real-Time Analytics in Solar Concentrators
15.4.3 Federated Learning in Distributed Solar Energy Systems
15.4.4 Explainable AI for Model Interpretability in Digital Twins
15.4.5 Hybrid Cloud-Edge Architectures for Digital Twin Scalability
15.4.6 Standards and Interoperability in AI-Driven Digital Twins
15.4.7 Summary
15.5 Modeling and Simulation Tools for AI in Digital Twins
15.5.1 Introduction
15.5.2 Numerical Simulation Platforms for Solar Concentrators
15.5.3 Machine Learning Frameworks for AI Model Development
15.5.4 Coupling AI with Physics-Based Simulations
15.5.5 Workflow Automation and Model Lifecycle Management
15.5.6 Validation and Calibration Techniques
15.5.7 Summary
15.6 Case Study 1 — Thermal Performance Prediction Using Deep Learning
15.6.1 Introduction
15.6.2 System Configuration and Dataset Acquisition
15.6.3 Deep Learning Model Design and Optimization
15.6.4 Training Process and Computational Environment
15.6.5 Model Validation and Performance Metrics
15.6.6 Digital Twin Integration and Operational Deployment
15.6.7 Implications for Predictive Maintenance and Lifecycle Management
15.6.8 Summary
15.7 Case Study 2 — Reinforcement Learning for Concentrator Tracking Control
15.7.1 Introduction
15.7.2 Problem Formulation and Digital Twin Setup
15.7.3 Algorithm Selection and Training Methodology
15.7.4 Training Outcomes and Policy Performance
15.7.5 Deployment in Operational Digital Twin
15.7.6 Broader Implications and Future Enhancements
15.7.7 Summary
15.8 Future Research Directions and Opportunities
15.8.1 Summary
15.9 Conclusions
15.9.1 Broader Implications for Renewable Energy Systems
15.9.2 Concluding Remarks
References
16. Virtual Replicas for Real Impact: Improving Solar Asset Output Using Digital Twins
Supriya and Ashutosh Shukla
16.1 Introduction
16.1.1 Digital Twins Balancing Physical and Virtual Twins
16.2 Digital Twins in Solar Applications
16.3 Methods to Utilize a Digital Twin for Enhancing Solar Asset Performance
16.4 Conclusion
Bibliography
17. The Role of Digital Twins in Enhancing the Resilience and Sustainability of Renewable Energy Systems: A Systematic Review
Supriya, Yash Singhal and Vansh Joshi
17.1 Introduction
17.1.1 Role of Renewable Energy Sector in Power Generation
17.1.2 Evolution of Digital Twins in Renewable Energy Sector
17.1.2.1 Kinds of Digital Twins
17.2 Related Work
17.3 Benefits of Digital Twins in Power Generation
17.4 Challenges in Digital Twin Implementation
17.5 Conclusion
References
18. Enhancing Renewable Energy Efficiency with Digital Twin Technology: Real-Time Monitoring and Predictive Maintenance
Supriya and Ashutosh Shukla
18.1 Introduction
18.1.1 Renewable Energy Generation with Digital Twin
18.1.2 Wind Energy
18.1.3 Solar Energy
18.1.4 Marine Renewable Energy (MRE)
18.2 Digital Twin in Energy Transmission and Transformation
18.2.1 Power Transformers
18.2.2 Gas-Insulated Switchgear (GIS)
18.2.3 Power Cables
18.3 Digital Twin in Renewable Energy Storage Systems
18.3.1 Lithium-Ion Batteries (LIBs)
18.3.2 Fuel Cells (PEMFC & SOFC)
18.4 Advantages and Challenges of Digital Twin Technology
18.4.1 Advantages
18.4.2 Challenges
Bibliography
19. Solar Concentrators: Decarbonizing Our Future, Securing Our Energy
Surya Prakash Chauhan, Sachin Sharma and Rajesh Maithani
19.1 Introduction
19.1.1 Overview of Climate Change
19.1.2 Renewable Energy Transition
19.2 Solar Concentrators
19.3 Role in Decarbonization
19.3.1 Fossil Fuel Dependency
19.3.2 Impact on Carbon Emission
19.3.3 Environmental and Economic Benefits
19.4 Advancements and Innovations
19.4.1 Research and Development
19.4.2 AI and Machine Learning for Efficiency Optimization
19.5 Challenges and Solutions
19.5.1 Technological Barriers
19.5.2 Cost Reduction Strategies
19.6 Conclusion and Future Perspective
References
Index
Back to Top
Preface
1. Solar Thermal Technology – A Brief
Paramjeet Singh Paliyal and Surajit Mondal
References
2. Introduction to Digital Twin Technology
Sanidhya Sarthak
2.1 Introduction to Digital Twin Technology
2.2 Core Components of Digital Twin Technology
2.2.1 Physical Entity and Sensor Integration
2.2.2 Digital Model and Data Representation
2.2.3 IoT and Cloud Computing for Real-Time Synchronization
2.2.4 AI, Machine Learning, and Predictive Analytics
2.2.5 User Interface and Visualization Tools
2.3 Solar Concentrator Types
2.3.1 Tower (Heliostat Field) Systems
2.3.2 Parabolic Trough Collectors
2.3.3 Linear Fresnel Collectors
2.3.4 Parabolic Dish Systems
2.4 Digital Twin for Solar Concentrators
2.5 Digital Twin Applications by Concentrator Type
2.5.1 Parabolic Trough Systems
2.5.2 Fresnel Collector Systems
2.5.3 Tower (Heliostat) Systems
2.5.4 Parabolic Dish Systems
2.6 Optimization Strategies
2.7 Real-World Case Studies
2.8 Mathematical Models and Equations
2.9 Future Directions and Challenges
2.10 Conclusion
Bibliography
3. Solar Concentrators: A Study Pertaining to Advancements and Challenges in the Current Scenario
Deepak Singh Karki and Vinita Sirala
3.1 Introduction
3.2 Classification of Solar Concentrators
3.2.1 Classification Based on Optical Geometry
3.2.2 Classification Based on Concentration Ratio
3.2.3 Classification Based on Tracking Mechanism
3.2.4 Classification Based on Application
3.2.5 Classification Based on Structural Design
3.3 Recent Advancements in Solar Concentrator Technology
3.3.1 Advanced Materials for Enhanced Optical Efficiency
3.3.2 Improvements in Solar Tracking Mechanisms
3.3.3 Integration of Digital Twin Technology for Performance Optimization
3.3.4 Advanced Heat Storage Solutions
3.3.5 Hybridization with Photovoltaics and Other Renewable Technologies
3.3.6 Smart Sensors and AI for Performance Optimization
3.3.7 Self-Healing and Adaptive Optics
3.4 Conclusion
3.5 Future Scope
References
4. Basics of Solar Concentrators
Ayushman Srivastav, Rajesh Maithani and Sachin Sharma
4.1 Introduction
4.2 Principles of Solar Concentration
4.2.1 Mechanism of Solar Concentration
4.2.2 Concentration Ratio and Its Importance
4.2.3 Impact of Concentration on System Efficiency
4.2.4 Optical Efficiency and Losses
4.3 Types of Solar Concentrators
4.4 Advanced Configurations: Multi-Surface and Multi-Element Combinations
4.5 Applications and Advantages of Solar Concentrators
4.6 Challenges and Considerations
4.7 Future Prospects
4.8 Conclusion
References
5. Solar Scheffler Concentrators: The Future of Sustainable Cooking
Amit Kumar, Ashish Karn and Varun Pratap Singh
5.1 Introduction
5.2 Solar Scheffler Concentrator
5.2.1 Design and Structure
5.3 Cooking with Solar Scheffler Concentrator
5.4 Case Studies on Scheffler Dish-Based Solar Cooking Systems
5.5 Results and Discussion
5.6 Conclusion
Acknowledgements
References
6. Digital Twin-Based Real-Time Solar Concentrator Control and Monitoring System
Aruna Pant and Adesh Kumar
6.1 Introduction
6.2 The Digital Twin Concept
6.3 Elements of the System for Real-Time Control and Monitoring
6.3.1 System Solar Concentrators
6.3.2 Digital Twin Framework
6.3.3 System for Data Acquisition and Communication
6.3.4 Control System
6.3.5 Interface and Functional Block
6.4 Uses for Solar Concentrators
6.4.1 Use in Photovoltaic Systems
6.4.2 Applications in Solar Thermal Systems
6.5 Cutting Edge Solar Concentration Technologies
6.5.1 Integration of Digital Twins
6.5.2 Real-Time Control Systems Based on FPGA
6.5.3 Leveraging AI and Machine Learning for Optimization
6.6 Performance Evaluation
6.6.1 Solar Tracking Performance Evaluation
6.6.2 Power Output with vs. without Tracking
6.6.3 Digital Twin Data Exchange Latency
6.6.4 Solar Panel Temperature Monitoring
6.7 Conclusions
References
7. Environmental and Health Impacts of Utilizing Solar PV Panels
Ayushman Srivastav, Rajesh Maithani and Sachin Sharma
Introduction
Energy and Resource-Intensive Manufacturing
Raw Materials
Energy Consumption
Chemical Usage
Land Use and Habitat Displacement
Large-Scale Installations
Soil Degradation
Variability and Intermittency of Solar Energy
Intermittency and Grid Stability
Energy Storage and Environmental Impacts
Disposal Challenges and Hazardous Materials
Presence of Hazardous Materials
Recycling Challenges
Landfill Disposal and Leachate Generation
Mitigating Negative Environmental Impacts
Improved Recycling Technologies
Extended Producer Responsibility (EPR)
Second-Life Applications
Mitigation Measures for Wildlife Impact
Lifecycle Assessment (LCA)
Future Directions for Research and Development
Conclusion
References
8. Integration of IoT, AI, and Digital Twin Technology for Smart Solar Concentrators
Paramjeet Singh Paliyal and Surajit Mondal
8.1 Introduction
8.2 Fundamentals of Solar Concentrators
8.2.1 Thermal and Optical Performance Considerations
8.2.2 Limitations of Conventional Concentrator Systems
8.3 Digital Twin Technology: Concepts and Architecture
8.4 Integration Framework: IoT, AI, and Digital Twin in Solar Concentrators
8.5 Conclusion
References
9. A Digital Twin-Based NLP Chatbot for Self-Assessment and Psychoeducation of Obsessive-Compulsive Disorder
Shweta Gupta, Adesh Kumar, Surajit Mondal, Niraj Kumar and Ompal
9.1 Introduction
9.2 Symptoms of OCD
9.3 Types of OCD
9.4 Causes of OCD
9.5 Treatment of OCD
9.6 Management
9.7 SELF Assessment of OCD
9.7.1 AI-Based Treatments
9.7.2 Real World Scenario
9.8 NLP Chatbot for Psychoeducation and Self-Assessment of OCD
9.9 Conclusions
References
10. FPGA Performance Analysis of LDPC Codes for Communication Systems
Aakanksha Devrari, Adesh Kumar and Amit Kumar
10.1 Introduction
10.2 Related Work
10.3 LDPC Codes
10.3.1 LDPC Encoder and Decoder
10.4 Hardware Architecture
10.5 Results and Discussion
10.6 Conclusions
References
11. Optimizing Renewable Energy Management with the Use of Digital Twin Technology: Insights from NICE China Energy
Supriya, Vriti Khandelwal, Kushagar Sharma, Vasu Sharma and Kashish Kalra
11.1 Introduction
11.1.1 Renewable Energy Generation with Digital Twin
11.1.1.1 Wind Energy
11.1.1.2 Solar Energy
11.1.1.3 Marine Renewable Energy (MRE)
11.2 Digital Twin in Energy Transmission and Transformation
11.2.1 Power Transformers
11.2.2 Gas-Insulated Switchgear (GIS)
11.2.3 Power Cables
11.3 Digital Twin in Renewable Energy Storage Systems
11.3.1 Lithium-Ion Batteries (LIBs)
11.3.2 Fuel Cells (PEMFC & SOFC)
11.4 Advantages and Challenges of Digital Twin Technology
11.4.1 Advantages
11.4.2 Challenges
11.5 Case Study: Digital Twin Technology in Power Plant Optimization
11.5.1 Overview
11.5.2 Key Components of the DT Power Plant
11.5.3 Key Findings and Benefits of China Energy’s Digital Twin Implementation
11.6 Conclusion
Bibliography
12. Enhancing Solar PV Efficiency through a Digital Twin Framework and Machine Learning Models
Nitin Kumar, Rupendra Kumar Pachauri and Piyush Kuchhal
12.1 Introduction
12.2 Literature Review
12.3 Experimental Location and Methodology
12.4 Result and Discussion
12.5 Conclusion
References
13. Digital Twin-Enabled Solar Concentrators: A Cyber-Physical Approach to Intelligent Renewable Energy
Laxman Singh Rana, Lakhan Singh, Surabhi Chauhan, Kundan Singh and Deepak Singh Karki
13.1 Introduction
13.2 Fundamentals of Digital Twin Technology
13.2.1 Definition and Concept
13.2.2 Core Components of a Digital Twin System
13.2.3 Types of Digital Twins
13.2.4 Technological Enablers
13.2.5 Benefits of Digital Twin Technology
13.3 Solar Concentrator Systems: An Overview
13.3.1 Principle of Operation
13.3.2 Types of Solar Concentrator Systems
13.3.2.1 Parabolic Trough Collectors (PTCs)
13.3.2.2 Linear Fresnel Reflectors (LFRs)
13.3.2.3 Parabolic Dish Concentrators
13.3.2.4 Heliostat Field with Central Receiver (Solar Tower)
13.3.3 Performance Considerations
13.3.4 Why Solar Concentrators Need Digital Twins
13.3.5 Summary of System Characteristics
13.4 Digital Twin Architecture for Solar Concentrators
13.4.1 System Architecture
13.4.2 Communication Technologies
13.5 Applications of Digital Twins in Solar Concentrators
13.5.1 Real-Time Monitoring and Diagnostics
13.5.2 Predictive Maintenance
13.5.3 Optimization and Control
13.5.4 Design and Simulation
13.6 Case Studies
13.6.1 Digital Twin for Heliostat Field Optimization
13.6.2 Predictive Maintenance of Parabolic Troughs
13.7 Challenges and Considerations
13.8 Future Outlook and Research Directions
13.9 Conclusion
Bibliography
14. Artificial Neural Networks for Solar Panel Tilt Angle Optimization: A Power Generation Analysis
Nitin Kumar, Rupendra Kumar Pachauri and Piyush Kuchhal
14.1 Introduction
14.1.1 Literature Review
14.2 ANN and Tilt Angle Relation with Latitude
14.3 The Process of Data Curation
14.4 Test Metric
14.5 Results and Discussion
Conclusion
Nomenclature
References
15. Machine Learning and Artificial Intelligence in Digital Twins
Sampath Emani and Gurunadh Velidi
15.1 Introduction to Digital Twins and Solar Concentrators
15.1.1 Overview of Digital Twin Technology
15.1.2 Role of Digital Twins in Solar Concentrator Systems
15.1.3 Key Components of Solar Digital Twin Architectures
15.1.4 Scope of This Chapter
15.1.5 Organization of the Subsequent Sections
15.2 Machine Learning and Artificial Intelligence Fundamentals in Digital Twins
15.2.1 Introduction to Machine Learning and Artificial Intelligence in Digital Twins
15.2.2 Supervised Learning in Digital Twins
15.2.2.1 Applications in Solar Concentrators
15.2.3 Unsupervised Learning for Operational Insights
15.2.3.1 Applications in Solar Concentrators
15.2.4 Reinforcement Learning for Adaptive Control
15.2.4.1 Applications in Solar Concentrators
15.2.5 Physics-Informed Machine Learning (PIML)
15.2.5.1 Applications in Solar Concentrators
15.2.6 Comparative Analysis of Learning Paradigms
15.2.7 Key Considerations for Machine Learning Integration
15.2.8 Summary
15.3 State-of-the-Art AI Techniques in Solar Energy Systems
15.3.1 Introduction
15.3.2 Real-World Case Studies and Practical Implementations
15.3.3 Summary
15.4 Current Trends in AI-Driven Digital Twins for Solar Concentrators
15.4.1 Introduction
15.4.2 Edge AI for Real-Time Analytics in Solar Concentrators
15.4.3 Federated Learning in Distributed Solar Energy Systems
15.4.4 Explainable AI for Model Interpretability in Digital Twins
15.4.5 Hybrid Cloud-Edge Architectures for Digital Twin Scalability
15.4.6 Standards and Interoperability in AI-Driven Digital Twins
15.4.7 Summary
15.5 Modeling and Simulation Tools for AI in Digital Twins
15.5.1 Introduction
15.5.2 Numerical Simulation Platforms for Solar Concentrators
15.5.3 Machine Learning Frameworks for AI Model Development
15.5.4 Coupling AI with Physics-Based Simulations
15.5.5 Workflow Automation and Model Lifecycle Management
15.5.6 Validation and Calibration Techniques
15.5.7 Summary
15.6 Case Study 1 — Thermal Performance Prediction Using Deep Learning
15.6.1 Introduction
15.6.2 System Configuration and Dataset Acquisition
15.6.3 Deep Learning Model Design and Optimization
15.6.4 Training Process and Computational Environment
15.6.5 Model Validation and Performance Metrics
15.6.6 Digital Twin Integration and Operational Deployment
15.6.7 Implications for Predictive Maintenance and Lifecycle Management
15.6.8 Summary
15.7 Case Study 2 — Reinforcement Learning for Concentrator Tracking Control
15.7.1 Introduction
15.7.2 Problem Formulation and Digital Twin Setup
15.7.3 Algorithm Selection and Training Methodology
15.7.4 Training Outcomes and Policy Performance
15.7.5 Deployment in Operational Digital Twin
15.7.6 Broader Implications and Future Enhancements
15.7.7 Summary
15.8 Future Research Directions and Opportunities
15.8.1 Summary
15.9 Conclusions
15.9.1 Broader Implications for Renewable Energy Systems
15.9.2 Concluding Remarks
References
16. Virtual Replicas for Real Impact: Improving Solar Asset Output Using Digital Twins
Supriya and Ashutosh Shukla
16.1 Introduction
16.1.1 Digital Twins Balancing Physical and Virtual Twins
16.2 Digital Twins in Solar Applications
16.3 Methods to Utilize a Digital Twin for Enhancing Solar Asset Performance
16.4 Conclusion
Bibliography
17. The Role of Digital Twins in Enhancing the Resilience and Sustainability of Renewable Energy Systems: A Systematic Review
Supriya, Yash Singhal and Vansh Joshi
17.1 Introduction
17.1.1 Role of Renewable Energy Sector in Power Generation
17.1.2 Evolution of Digital Twins in Renewable Energy Sector
17.1.2.1 Kinds of Digital Twins
17.2 Related Work
17.3 Benefits of Digital Twins in Power Generation
17.4 Challenges in Digital Twin Implementation
17.5 Conclusion
References
18. Enhancing Renewable Energy Efficiency with Digital Twin Technology: Real-Time Monitoring and Predictive Maintenance
Supriya and Ashutosh Shukla
18.1 Introduction
18.1.1 Renewable Energy Generation with Digital Twin
18.1.2 Wind Energy
18.1.3 Solar Energy
18.1.4 Marine Renewable Energy (MRE)
18.2 Digital Twin in Energy Transmission and Transformation
18.2.1 Power Transformers
18.2.2 Gas-Insulated Switchgear (GIS)
18.2.3 Power Cables
18.3 Digital Twin in Renewable Energy Storage Systems
18.3.1 Lithium-Ion Batteries (LIBs)
18.3.2 Fuel Cells (PEMFC & SOFC)
18.4 Advantages and Challenges of Digital Twin Technology
18.4.1 Advantages
18.4.2 Challenges
Bibliography
19. Solar Concentrators: Decarbonizing Our Future, Securing Our Energy
Surya Prakash Chauhan, Sachin Sharma and Rajesh Maithani
19.1 Introduction
19.1.1 Overview of Climate Change
19.1.2 Renewable Energy Transition
19.2 Solar Concentrators
19.3 Role in Decarbonization
19.3.1 Fossil Fuel Dependency
19.3.2 Impact on Carbon Emission
19.3.3 Environmental and Economic Benefits
19.4 Advancements and Innovations
19.4.1 Research and Development
19.4.2 AI and Machine Learning for Efficiency Optimization
19.5 Challenges and Solutions
19.5.1 Technological Barriers
19.5.2 Cost Reduction Strategies
19.6 Conclusion and Future Perspective
References
Index
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