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Advanced Approaches, Business Models, and Novel Techniques for Management and Control of Smart Grids
| Advanced Approaches, Business Models, and Novel Techniques for Management and Control of Smart Grids |
| Autore | Siano Pierluigi |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (210 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
power system oscillations
stability Prony method intrinsic mode functions (IMFs) wide-area measurement system (WAMS) phasor measurement units (PMUs) frequency stability load frequency control communication system delay virtual inertia microgrid recursive damped least squares black-box modeling online identification energy management system demand-side management uncertainty energy storage distributed generation activity recognition activity detection activity prediction smart building energy and comfort management digital microgrid power grid integer linear programming routing energy distributed energy resources Dijkstra algorithm Islanding detection modern power system anti-islanding protection sequence components earth fault location negative sequence current holistic power system architecture smart grid fractal design fractal grid LINK-paradigm market design local electricity market large-scale distributed generations low-voltage active distribution network islanded mode non-homogeneous model synchronization |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557685503321 |
Siano Pierluigi
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah
| Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2022] |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 307.760285 |
| Soggetto topico |
Smart cities
Smart power grids |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-74832-1
1-119-74834-8 1-119-74831-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions.
References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References. Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking. 8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion. 9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV). 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850). |
| Record Nr. | UNINA-9910555142703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah
| Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2022] |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 307.760285 |
| Soggetto topico |
Smart cities
Smart structures Smart power grids |
| ISBN |
1-119-74832-1
1-119-74834-8 1-119-74831-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions.
References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References. Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking. 8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion. 9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV). 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850). |
| Record Nr. | UNINA-9910830498703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Trading in local energy markets and energy communities : concepts, structures and technologies / / Miadreza Shafie-khah, Amin Shokri Gazafroudi
| Trading in local energy markets and energy communities : concepts, structures and technologies / / Miadreza Shafie-khah, Amin Shokri Gazafroudi |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (264 pages) |
| Disciplina | 333.79 |
| Collana | Lecture notes in energy |
| Soggetto topico | Energy industries |
| ISBN | 3-031-21402-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Energy communities -- Local energy markets -- Active players in local energy markets -- Community-based local energy markets -- Peer-to-peer local energy trading -- Hybrid structures for local energy trading.-mClustering-based local energy markets. |
| Record Nr. | UNINA-9910659484203321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||