Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 online resource (210 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
activity detection
activity prediction activity recognition anti-islanding black-box modeling communication system delay demand-side management digital microgrid Dijkstra algorithm distributed energy resources distributed generation distributed generations earth fault location energy and comfort management energy management system energy storage fractal design fractal grid frequency stability holistic power system architecture integer linear programming intrinsic mode functions (IMFs) islanded mode Islanding detection large-scale LINK-paradigm load frequency control local electricity market low-voltage active distribution network market design microgrid modern power system n/a negative sequence current non-homogeneous model online identification phasor measurement units (PMUs) power grid power system oscillations Prony method protection recursive damped least squares routing energy sequence components smart building smart grid stability synchronization uncertainty virtual inertia wide-area measurement system (WAMS) |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557685503321 |
Siano Pierluigi
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security
| Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security |
| Autore | Siano Pierluigi |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (207 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
Atom search optimization
calibration curves contamination cyber-attacks cyber-physical security damping busbar digital simulator discrete wavelet transform dynamic state estimation (DSE) energy efficiency ensemble square root filter (EnSRF) fault classification GIS grey wolf optimizer grounding grid harmonics Harris Hawk's optimization high gain DC-DC converter high-frequency inductance calculation hybrid MPPT algorithm insulators laser-induced breakdown spectroscopy magnetic field microgrid modified LUO converter modular multilevel converter monitoring and controlling system mother wavelet multiprocessor system n/a online non-clairvoyant scheduling optimization orientation potential analysis protective relay PV water pumping railway Sage-Husa algorithm salt STATCOM synchronous machine traction power-supply system transformer transient electromagnetic method (TEM) transmission line VFTO suppression weighted flow time |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557332903321 |
|
Siano Pierluigi
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in smart grid technology Select proceedings of PECCON 2019 . Volume I / / Pierluigi Siano, K. Jamuna, editors
| Advances in smart grid technology Select proceedings of PECCON 2019 . Volume I / / Pierluigi Siano, K. Jamuna, editors |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XI, 537 p. 401 illus., 276 illus. in color.) |
| Disciplina | 621.3 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electrical engineering
Smart power grids |
| ISBN | 981-15-7245-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Perspectives on Solar Energy and Wind Energy -- Chapter 2: Distributed Energy Generation and Hybrid Systems -- Chapter 3: Control of Power Converters in Smart Grid Integration. Chapter 4: Autonomous Vehicular Technology -- Chapter 5: Power Quality Issues -- Chapter 6: Social and Environmental Issues in Smart Grid Environment. |
| Record Nr. | UNINA-9910425158603321 |
| Singapore : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Smart Grid Technology : Select Proceedings of PECCON 2019—Volume I / / edited by Pierluigi Siano, K. Jamuna
| Advances in Smart Grid Technology : Select Proceedings of PECCON 2019—Volume I / / edited by Pierluigi Siano, K. Jamuna |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XI, 537 p. 401 illus., 276 illus. in color.) |
| Disciplina | 621.3 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electric power production
Telecommunication Energy policy Renewable energy sources Electrical Power Engineering Communications Engineering, Networks Mechanical Power Engineering Energy Policy, Economics and Management Renewable Energy |
| ISBN | 981-15-7245-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Perspectives on Solar Energy and Wind Energy -- Chapter 2: Distributed Energy Generation and Hybrid Systems -- Chapter 3: Control of Power Converters in Smart Grid Integration. Chapter 4: Autonomous Vehicular Technology -- Chapter 5: Power Quality Issues -- Chapter 6: Social and Environmental Issues in Smart Grid Environment. |
| Record Nr. | UNINA-9910863103703321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Control and estimation of dynamical nonlinear and partial differential equation systems : theory and applications
| Control and estimation of dynamical nonlinear and partial differential equation systems : theory and applications |
| Autore | Rigatos Gerasimos |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2022 |
| Descrizione fisica | 1 online resource (992 pages) |
| Disciplina | 629.8312 |
| Altri autori (Persone) |
AbbaszadehMasoud
SianoPierluigi |
| Collana | Control, Robotics and Sensors |
| Soggetto topico |
Control theory - Mathematical models
Control theory |
| ISBN |
1-83724-467-7
1-5231-4668-0 1-83953-427-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- About the authors -- Preface -- Acknowledgement -- 1. Principles of non-linear control -- 1.1 Control based on approximate linearization -- 1.2 Global linearization-based control concepts -- 1.3 Global linearization-based control using differential flatness theory -- 1.4 Control of PDE dynamical systems -- 2. Control based on approximate linearization for robotic systems -- 2.1 Nonlinear control of the cart and double-pendulum overhead crane -- 2.2 Nonlinear control of the underactuated offshore crane -- 2.3 Nonlinear control of the inertia wheel and pendulum system -- 2.4 Nonlinear control of the torsional oscillator with rotational actuator -- 2.5 Nonlinear control of robotic exoskeletons -- 2.6 Nonlinear control of brachiation robots -- 2.7 Nonlinear control of power line inspection robots -- 2.8 Nonlinear control of robots with electrohydraulic actuators -- 2.9 Nonlinear control of robots with electropneumatic actuators -- 2.10 Nonlinear control of flexible joint robots -- 2.11 Nonlinear control of redundant robotic manipulators -- 2.12 Nonlinear control of parallel closed-chain robotic manipulators -- 3. Control based on approximate linearization for autonomous vehicles -- 3.1 Nonlinear control of tracked autonomous vehicles -- 3.2 Nonlinear control of the autonomous articulated fire-truck -- 3.3 Nonlinear control of the truck and N-trailer system -- 3.4 Nonlinear control of the ball-bot autonomous robot -- 3.5 Nonlinear control of the ball-and-plate dynamical system -- 3.6 Nonlinear control of 3-DOF unmanned surface vessels -- 3.7 Nonlinear control of the 3-DOF autonomous underwater vessel -- 3.8 Nonlinear control of the vertical take-off and landing aircraft -- 3.9 Nonlinear control of aerial manipulators -- 3.10 Nonlinear control of the 6-DOF autonomous octocopter.
3.11 Nonlinear control of hypersonic aerial vehicles -- 4. Control based on approximate linearization in energy conversion -- 4.1 Nonlinear control of the VSI-fed three-phase PMSM -- 4.2 Nonlinear control of VSI fed six-phase PMSMs -- 4.3 Nonlinear control of DC electric microgrids -- 4.4 Nonlinear control of distributed marine-turbine power generation units -- 4.5 Nonlinear control of PMLSGs in wave energy conversion systems -- 4.6 Nonlinear control of Permanent Magnet Brushless DC motors -- 4.7 Nonlinear optimal control of Hybrid ElectricVehicles powertrains -- 4.8 Nonlinear control of shipboard AC/DC microgrids -- 4.9 Nonlinear control of power generation in hybrid AC/DC microgrids -- 5. Control based on approximate linearization for mechatronic systems -- 5.1 Nonlinear control of electrohydraulic actuators -- 5.2 Nonlinear control of electropneumatic actuators -- 5.3 Nonlinear control of hot-steel rolling mills -- 5.4 Nonlinear control of paper mills -- 5.5 Nonlinear control of the injection moulding machine -- 5.6 Nonlinear control of the slosh-container system dynamics -- 5.7 Nonlinear control of micro-satellites' attitude dynamics -- 5.8 Nonlinear control of the industrial crystallization process -- 6. Control based on global linearisation for industrial and PDE systems -- 6.1 Control of a robotic exoskeleton subject to time-delays -- 6.2 Adaptive control of synchronous reluctance machines -- 6.3 Control of a mobile robotic manipulator -- 6.4 State of charge estimation in EVs with a KF-based disturbance observer -- 6.5 Control of nonlinear wave PDE dynamics -- 6.6 Control of data-flow PDE for bandwidth allocation in internet routes -- 6.7 Diffusion PDE control of data flow in communication networks -- 6.8 Control of the diffusion PDE in Li-ion batteries -- 6.9 Control of the diffusion PDE in financial assets' management. 6.10 Estimation of PDE dynamics of the highway traffic -- 6.11 Estimation of the PDE dynamics of a cable-suspended bridge -- Epilogue -- Glossary -- References -- Index. |
| Record Nr. | UNINA-9911006786203321 |
|
Rigatos Gerasimos
|
||
| Stevenage : , : Institution of Engineering & Technology, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data-Driven Energy Management and Tariff Optimization in Power Systems : Shaping the Future of Electricity Distribution Through Analytics
| Data-Driven Energy Management and Tariff Optimization in Power Systems : Shaping the Future of Electricity Distribution Through Analytics |
| Autore | Arasteh Hamidreza |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (320 pages) |
| Disciplina | 333.7932 |
| Altri autori (Persone) |
SianoPierluigi
MoslemiNiki GuerreroJosep M |
| Soggetto topico |
Electric power systems - Management
Energy consumption - Forecasting |
| ISBN |
1-394-29030-6
1-394-29028-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Chapter 1 Fundamentals of Power System Data and Analytics -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems -- 1.2.2 Transformation in the Power Industry -- 1.2.3 Drivers and Barriers -- 1.3 Data‐rich Power Systems -- 1.3.1 Data Sources and Types -- 1.3.2 Data Structure -- 1.4 Data Analytics in Power Systems -- 1.4.1 What Is Data Analytics? -- 1.4.2 Analytics Techniques -- 1.5 Data Analytics‐Based Decision‐Making in Future Power Systems -- 1.5.1 Decision Framework -- 1.5.1.1 Uncertainty Issues -- 1.5.1.2 Behavioral Analytics -- 1.5.1.3 Policy Mechanisms -- 1.5.2 Computational Aspects -- 1.6 Conclusion -- 1.7 Future Trends and Challenges -- References -- Chapter 2 Advanced Predictive Modeling for Energy Consumption and Demand -- 2.1 The Role of Load Forecasting in Power System Planning -- 2.2 Need for Short‐Term Demand Forecasting -- 2.3 Components of Power Demand and Factors Affecting Demand Growth -- 2.3.1 Electricity Demand from the Consumer Type Perspective -- 2.3.2 Electricity Demand from the Supply Perspective -- 2.4 Electricity Demand in Networks with High Renewable Energy Sources -- 2.5 Machine Learning and Its Applications in Demand Forecast -- 2.5.1 Application of Clustering in Load Forecasting -- 2.6 The Impact of Macro‐decisions on Long‐term Load Forecasting -- 2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand -- 2.7 Conclusion -- References -- Chapter 3 Demand Response and Customer‐Centric Energy Management -- 3.1 Introduction -- 3.2 Background -- 3.3 Future Power Systems Aspects, Trends, and Challenges -- 3.4 Transforming to Customer‐Centric Era -- 3.4.1 Differences Between Customer‐Centric DR Solution and Other Ways in the Future Power System.
3.4.2 Drivers and Enablers -- 3.5 Customer‐Centric Power System Structure -- 3.5.1 Physical Layer -- 3.5.1.1 Physical Resources -- 3.5.1.2 Physical Constraints of the System -- 3.5.2 Cyber‐Social Layers -- 3.5.2.1 Centralized Approach (Traditional) -- 3.5.2.2 Decentralized Approach (Future) -- 3.6 Conclusion and Future Trends -- References -- Chapter 4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights -- 4.1 Introduction -- 4.1.1 Data Mining: Concepts, Procedures, and Tools -- 4.1.2 Energy Management and the Role of Data Mining -- 4.1.3 Aims and Scope -- 4.2 Investigating Industrial Load Data: Analysis Through Various Indexes -- 4.3 Classification of Industries -- 4.4 Discussion and Conclusions -- References -- Chapter 5 Data‐Driven Tariff Design for Equitable Energy Distribution -- 5.1 Introduction -- 5.1.1 Literature Review and Contributions -- 5.1.2 Chapter Organization -- 5.2 Proposed Approach and Formulations -- 5.3 Describing the Case Study -- 5.4 Simulation Results -- 5.5 Conclusions and Future Works -- References -- Chapter 6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems -- 6.1 Introduction -- 6.2 Machine Learning Techniques -- 6.2.1 Artificial Neural Network and Deep Neural Network -- 6.2.2 Convolutional Neural Network -- 6.2.3 Recurrent Neural Network -- 6.2.4 Long Short‐Term Memory -- 6.3 General View of ML/DL Methods for RES Integration -- 6.3.1 Data Preprocessing -- 6.3.1.1 Normalization -- 6.3.1.2 Wrong/Missing Values and Outliers -- 6.3.1.3 Data Resolution -- 6.3.1.4 Inactive Time Data -- 6.3.1.5 Data Augmentation -- 6.3.1.6 Correlation -- 6.3.1.7 Data Clustering -- 6.3.2 Deterministic/Probabilistic Forecasting Methods -- 6.3.2.1 Deterministic Methods -- 6.3.2.2 Probabilistic Forecasting Methods -- 6.3.3 Evaluation Measures. 6.4 ML/DL Application for Integration of RES -- 6.4.1 Renewable Resources Data Prediction/Planning -- 6.4.2 RES Power Generation Prediction/Operation -- 6.4.3 Electric Load and Demand Forecasting -- 6.4.4 Stability Analysis -- 6.4.4.1 Security Assessment -- 6.4.4.2 Stability Assessment -- 6.5 Integrated Machine Learning and Optimization Approach -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning‐Based Solutions for Renewable Energy Integration -- 7.1 Introduction -- 7.2 Machine Learning Importance in RESs Sector -- 7.2.1 AI‐Based Algorithms in RESs -- 7.2.2 ML Algorithms Application in RESs -- 7.3 Role of ML in Optimizing Renewable Energy Generation -- 7.3.1 Different Programming Models in RES Optimization -- 7.3.2 Optimization Objectives in RESs -- 7.3.3 ML Applications in Optimizing Renewable Energy Generation -- 7.4 Ensuring Grid Stability Through ML‐Based Forecasting -- 7.4.1 Grid Stability Forecasting -- 7.4.2 Grid Stability Through ML‐Based Forecasting -- 7.5 Challenges and Future Direction in ML‐Based Approaches to RESs -- 7.5.1 Challenges in ML‐Based Approaches to RESs -- 7.5.2 Future Directions in ML‐Based Approaches to RESs -- 7.6 Conclusion -- References -- Chapter 8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting -- 8.1 RES Share in World Energy Transition -- 8.2 Applications of PV Panels in Energy Systems -- 8.3 Disadvantages of PV Panels -- 8.4 Importance of PV Power Forecasting -- 8.5 Proposed Algorithm for PV Power Prediction -- 8.6 Numerical Results and Discussions -- 8.7 Concluding Remarks -- References -- Chapter 9 Power System Resilience Evaluation Data Challenges and Solutions -- 9.1 Introduction -- 9.2 A Review of Power System Resilience Metrics -- 9.3 The General Framework for the Resilience Assessment of the Power System -- 9.4 Data Required for Power System Resilience Studies. 9.4.1 Data of Natural Origin -- 9.4.2 Basic Data of the Power System -- 9.4.3 Data on Failure and Restoration Rates -- 9.5 Data Analysis and Correction -- 9.6 Disaster Forecasting in Power System Resilience Studies -- 9.7 Modeling the Impact of Disaster on Power System Performance -- 9.8 Static Model in Machine Learning -- 9.9 Spatiotemporal Random Process -- 9.9.1 Dynamic Model for Chain Failures -- 9.9.2 Nonstationary Failure‐Recovery‐Impact Processes -- 9.10 Lessons Learned and Concluding Remarks -- 9.11 Future Work -- References -- Chapter 10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach -- 10.1 Introduction -- 10.2 Deep Learning Neural Networks -- 10.2.1 RNN -- 10.2.2 LSTM -- 10.2.3 CNN -- 10.2.4 Convolutional Layer -- 10.2.5 Pooling Layer -- 10.2.6 Fully Connected Layer -- 10.3 The Proposed Method -- 10.3.1 Pre‐Processing and Preparing Data -- 10.3.2 Proposed Method Architecture -- 10.3.3 Proposed Method's Parameters -- 10.3.4 Performance Evaluation -- 10.4 Results and Discussion -- 10.5 Challenges and Future Trends -- 10.6 Conclusion -- References -- Chapter 11 Power System Cyber‐Physical Security and Resiliency Based on Data‐Driven Methods -- 11.1 Introduction -- 11.2 Fundamental Concepts -- 11.2.1 Cyber‐Physical Power System (CPPS) -- 11.2.2 Security and Resiliency -- 11.3 Role of Data Analytics -- 11.3.1 Basic Methods -- 11.3.1.1 Supervised Learning (SL) -- 11.3.1.2 Unsupervised Learning (UL) -- 11.3.2 Advanced Techniques -- 11.3.2.1 Dimensionality Reduction (DR) -- 11.3.2.2 Feature Engineering -- 11.3.2.3 Reinforcement Learning -- 11.3.2.4 Integrated Models -- 11.4 Interdependency Modeling -- 11.4.1 Direct Modeling -- 11.4.2 Testbeds -- 11.4.3 Game‐Theoretic -- 11.4.4 Machine Learning -- 11.5 Cyber‐Physical Threats -- 11.5.1 Physical Attacks -- 11.5.2 Cyberattacks -- 11.5.2.1 Confidentiality. 11.5.2.2 Availability -- 11.5.2.3 Integrity -- 11.5.3 Coordinated Attacks -- 11.6 Defense Framework -- 11.6.1 Preventive Measures -- 11.6.1.1 Supply Chain Security -- 11.6.1.2 Access Control -- 11.6.1.3 Personnel Training -- 11.6.1.4 Resource Allocation -- 11.6.1.5 Infrastructure Hardening -- 11.6.1.6 Moving Target Defense -- 11.6.2 Mitigation Actions -- 11.6.2.1 Attack Detection -- 11.6.2.2 Data Recovery -- 11.6.2.3 Reconfiguration and Restoration -- 11.6.2.4 Forensic Analysis -- 11.7 Conclusion -- References -- Chapter 12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems -- 12.1 Introduction -- 12.2 Load‐Shedding Strategies -- 12.2.1 Conventional LS -- 12.2.2 Adaptive LS -- 12.2.3 AI‐Based LS -- 12.3 Principles of UVLS -- 12.3.1 Amount of Load Shed -- 12.3.2 Location for LS -- 12.3.3 Application of VSI for UVLS -- 12.4 AI‐Based Methods -- 12.5 Case Study -- 12.5.1 Database Generation -- 12.5.2 Offline Training -- 12.5.3 Online Application -- 12.6 Future Challenges and Transfer Learning -- 12.7 Conclusion -- References -- Index -- EULA. |
| Record Nr. | UNINA-9911042410503321 |
|
Arasteh Hamidreza
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial Demand Response : Methods, Best Practices, Case Studies, and Applications
| Industrial Demand Response : Methods, Best Practices, Case Studies, and Applications |
| Autore | Alhelou Hassan Haes |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2022 |
| Descrizione fisica | 1 online resource (426 pages) |
| Disciplina | 621.3 |
| Altri autori (Persone) |
Moreno-MuñozAntonio
SianoPierluigi |
| Collana | Energy Engineering |
| Soggetto topico | Electric power consumption - Forecasting |
| ISBN |
1-83724-504-5
1-5231-5348-2 1-5231-4674-5 1-83953-562-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: A comprehensive review on industrial demand response strategies and applicationsChapter 2: Demand response cybersecurity for power systems with high renewable power shareChapter 3: Recurrent neural networks for electrical load forecasting to use in demand responseChapter 4: Optimal demand response strategy of an industrial customerChapter 5: Price-based demand response for thermostatically controlled loadsChapter 6: Electric vehicle massive resources mining and demand response applicationChapter 7: Demand response measurement and verification approaches: analyses and guidelinesChapter 8: Transactive energy industry demand response management marketChapter 9: Industrial demand response opportunities with residential appliances in smart gridsChapter 10: Modelling and optimal scheduling of flexibility in energy-intensive industryChapter 11: Industrial demand response: coordination with asset managementChapter 12: A machine learning-based approach for industrial demand responseChapter 13: Feasibility assessment of industrial demand responseChapter 14: Measurement and verification of demand response: the customer load baselineChapter 15: Modeling and optimizing the value of flexible industrial processes in the UK electricity marketChapter 16: Case study of Aran Islands: optimal demand response control of heat pumps and appliancesChapter 17: Use case of artificial intelligence, and neural networks in energy consumption markets, and industrial demand response. |
| Record Nr. | UNINA-9911004738803321 |
|
Alhelou Hassan Haes
|
||
| Stevenage : , : Institution of Engineering & Technology, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Innovations in electrical and electronic engineering . Volume 1 : proceedings of ICEEE 2022 / / Saad Mekhilef, Rabindra Nath Shaw, and Pierluigi Siano
| Innovations in electrical and electronic engineering . Volume 1 : proceedings of ICEEE 2022 / / Saad Mekhilef, Rabindra Nath Shaw, and Pierluigi Siano |
| Autore | Mekhilef Saad |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (568 pages) |
| Disciplina | 621.3 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electrical engineering
Electronics |
| ISBN | 981-19-1742-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910568276403321 |
|
Mekhilef Saad
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Innovations in Electrical and Electronic Engineering : Proceedings of ICEEE 2023, Volume 1 / / edited by Rabindra Nath Shaw, Pierluigi Siano, Saad Makhilef, Ankush Ghosh, S. L. Shimi
| Innovations in Electrical and Electronic Engineering : Proceedings of ICEEE 2023, Volume 1 / / edited by Rabindra Nath Shaw, Pierluigi Siano, Saad Makhilef, Ankush Ghosh, S. L. Shimi |
| Autore | Shaw Rabindra Nath |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (652 pages) |
| Disciplina | 621.31 |
| Altri autori (Persone) |
SianoPierluigi
MakhilefSaad GhoshAnkush ShimiS. L |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electric power production
Telecommunication Automatic control Robotics Automation Artificial intelligence Electrical Power Engineering Microwaves, RF Engineering and Optical Communications Control, Robotics, Automation Artificial Intelligence |
| ISBN |
9789819982899
9819982898 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Bidding Strategies for Generation Companies and Large Consumers in Carbon Emission Market Considering Electricity Spot Market Clearing Outcomes -- NSTLBO Based Approach for Optimal Scheduling of Hydro-Thermal Generating Units in Regulated Environment -- Chaos and Multistability in Fractional Order Power System: Dynamic Analysis and Implications -- Identification of Critical Nodes using Granger Causality for Strengthening Network Resilience in Electrical Distribution System -- A Bald Eagle Search Optimization Approach For Congestion Alleviation In Power System Transmission Channels -- Performance Evaluation on Developed FRP Rods Used in Composite Insulators Using Rotating Wheel and Dip Facility -- Prosumer peer-to-peer Transaction Model Considering Franchise Rights of Distribution Companies -- Photovoltaic with Battery and Super-Capacitor Energy Storage System for Better Performance Devices and Modeling -- Optimal Planning of Hydrogen Refueling Stations Considering Balanced Utilization of Resources -- Performance Analysis of Wind Power Forecasting via System Advisor Model Software -- Enhancing Photovoltaic Connector Reliability: A Comparative Review of Studies with Practical Recommendations -- A Comprehensive Study of Power Quality Improvement Techniques in Smart Grids with Renewable Energy Systems -- Powering the Future: IoT-Enabled Smart Grids for Sustainable Energy Systems -- Solar charging station for electric vehicles -- Theoretical analysis of Tandem Solar Cell Doped with MASnl3 with P3HT: PCBM Active Layer -- A Comprehensive Review on Electric Vehicle Battery Swapping Stations -- Comparison Between PID and SMC Controller To Control the Speed of DC Separately Excited Motor -- Active Disturbance Rejection Control of a SEPIC Converter -- Designing a Map less Navigation Mobile robot using Deep Q Learning -- Implications of Location of Strain Gauges and Excitation Voltage over the Metrological Performance of Trapezoidal-shaped Force Transducer -- Studying the effect of type of surfacepassivation layer on performance parameters of AlGaN MSM Detector -- Performance Analysis of Multi-user Cooperative Non-Orthogonal Multiple Access on Time Sharing Basis -- An Intelligent LoRa-Based Wireless Sensor Network Mesh Architecture to improve Precision Agriculture -- A Comprehensive Review of Conventional to Modern Algorithms of Speech Enhancement -- Gain Improvement of PIFA Antenna using Glass Substrate in comparison with FR4 Substrate. |
| Record Nr. | UNINA-9910806199203321 |
|
Shaw Rabindra Nath
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Innovations in Energy Management and Renewable Resources : Select Proceedings of IEMRE 2023 / / edited by Madhumita Pal, Josep M. Guerrero, Pierluigi Siano, Debapriya Das, Swati Chowdhuri
| Innovations in Energy Management and Renewable Resources : Select Proceedings of IEMRE 2023 / / edited by Madhumita Pal, Josep M. Guerrero, Pierluigi Siano, Debapriya Das, Swati Chowdhuri |
| Autore | Pal Madhumita |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 333.7 |
| Altri autori (Persone) |
GuerreroJosep M
SianoPierluigi DasDebapriya ChowdhuriSwati |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Energy policy
Electric power production Electric power distribution Energy Policy, Economics and Management Mechanical Power Engineering Energy Grids and Networks |
| ISBN |
9789819763900
9819763908 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Solar photovoltaic and Solar Thermal -- Wind, Hydro, Biomass and other renewable based Energy Systems -- Energy Storage and Management -- Smart Grid Technologies -- Renewable Energy Expansion and Policy Making. |
| Record Nr. | UNINA-9910896188503321 |
|
Pal Madhumita
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
