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AI for Status Monitoring of Utility Scale Batteries
| AI for Status Monitoring of Utility Scale Batteries |
| Autore | Wang Shunli |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2023 |
| Descrizione fisica | 1 online resource (385 pages) |
| Disciplina | 006.31 |
| Altri autori (Persone) |
LiuKailong
WangYujie StroeDaniel-Ioan FernándezCarlos (Lecturer in Analytical Chemistry) GuerreroJosep M |
| Collana | Energy Engineering |
| Soggetto topico | Machine learning |
| ISBN |
1-83724-508-8
1-5231-5354-7 1-83953-739-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Halftitle Page -- Series Page -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- List of contributors -- 1 Introduction -- 1.1 Motivation for utility-scale battery deployment -- 1.2 Definition of AI in the context of battery management -- 1.3 Advantages of using AI for battery management -- 2 Utility-scale lithium-ion battery system characteristics -- 2.1 Overview of lithium-ion batteries -- 2.1.1 Battery working principle -- 2.1.2 Principles of status monitoring of utility-scale batteries -- 2.2 Lithium-ion batteries -- 2.2.1 Lithium iron phosphate batteries -- 2.2.2 Lithium cobaltate oxide batteries -- 2.2.3 Lithium manganese oxide batteries -- 2.3 Large capacity lithium-ion batteries -- 2.3.1 Application areas of utility-scale batteries -- 2.3.2 Characteristics of utility-scale battery systems -- 2.3.3 Operational challenges of utility-scale battery systems -- 3 AI-based equivalent modeling and parameter identification -- 3.1 Overview of battery equivalent circuit modeling -- 3.2 Modeling types and concepts -- 3.3 Equivalent circuit modeling methods |
| Record Nr. | UNINA-9911007155703321 |
Wang Shunli
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| Stevenage : , : Institution of Engineering & Technology, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Arasteh Hamidreza
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Pal Madhumita
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Microgrids and Virtual Power Plants / / edited by Farhad Shahnia, Josep M. Guerrero
| Microgrids and Virtual Power Plants / / edited by Farhad Shahnia, Josep M. Guerrero |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (XVIII, 538 p. 315 illus., 293 illus. in color.) |
| Disciplina | 321.319 |
| Collana | Power Systems |
| Soggetto topico |
Electric power distribution
Energy policy Electric power-plants Internet of things Energy Grids and Networks Energy Policy, Economics and Management Power Stations Internet of Things |
| ISBN | 981-9766-23-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Holistic Data-Driven Approach for Sizing and Energy Management of an Urban Islanded Microgrid -- Probabilistic Microgrid Investment Planning with Integrated Game-Theoretic Demand Response Management -- Design and Modelling of Microgrids Operated at Constant Frequency and with a Power Level of Megawatt -- Design, sizing, and simulation of a DC microgrid for real implementation -- Microgrid Control Assessment Using Advanced Hardware in the Loop Technologies -- Stability and control of hybrid AC/DC microgrids -- Quantifying Transient Dynamics for Microgrid’s Inverter-based Resources -- Machine Learning and Internet-of-Things Solutions for Microgrid Resilient Operation -- Deep learning-based microgrid protection -- Cyber-attacks Detection and Mitigation in Microgrids -- Peer-to-Peer Trading among Microgrid Prosumers in Local Energy Markets -- Embedding Regulatory Frameworks in Microgrids Management -- Virtual Power Plant participation in Australian wholesale electricity markets -- Interconnected Microgrid Clusters through Various Provisional Power Exchange Links -- Cooperative and Transactive Integration of Multiple Microgrids -- Practical Aspects of Pre-Engineering Design of Clustered Microgrids -- Military Microgrids with Renewable Energy and 5G Communication. |
| Record Nr. | UNINA-9910903789003321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Power Electronics for Next-Generation Drives and Energy Systems : Converters and Control for Drives, Volume 1
| Power Electronics for Next-Generation Drives and Energy Systems : Converters and Control for Drives, Volume 1 |
| Autore | Kumar Nayan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Paris : , : Institution of Engineering & Technology, , 2023 |
| Descrizione fisica | 1 online resource (322 pages) |
| Disciplina | 621.31 |
| Altri autori (Persone) |
GuerreroJosep M
KasthaDebaprasad SahaTapas Kumar |
| Collana | Energy Engineering Series |
| Soggetto topico |
Power electronics
Electronic control Electric current converters |
| ISBN |
9781523153404
1523153407 9781839534706 1839534702 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- About the editors -- 1. Characteristics and modeling of wide band gap (WBG) power semiconductor | S. Toumi -- 2. Reliability of smart modern power electronic converter systems | Rupa Mishra, Nayan Kumar, Dibyendu Sen and Tapas Kumar Saha -- 3. Next-generation electrification of transportation systems: EV, ship, and rail transport | Carlos Reusser, Hector Young and Marcelo A. Perez -- 4. Multilevel inverter topologies and their applications | Faramarz Faraji, Amir Abbas Aghajani, Mojtaba Eldoromi, Ali Akbar Moti Birjandi, Amer M.Y.M. Ghias and Honnyong Cha -- 5. Multilevel inverters: topologies and optimization | Ebrahim Babaei and Mohammadamin Aalami -- 6. GaN oscillator-based DC-AC converter for wireless power transfer applications | Anwar Jarndal -- 7. Partial power processing and its emerging applications | Naser Hassanpour, Andrii Chub, Andrei Blinov and Samir Kouro -- 8. Matrix converters -- topologies, control methods, and applications | Ebrahim Babaei and Mohammadamin Aalami -- 9. Modelling, simulation and validation of average current and constant voltage operations in non-ideal buck and boost converters | Sumukh Surya, S. Mohan Krishna and Sheldon Williamson -- 10. Artificial intelligent-based modified direct torque control strategy: enhancing the dynamic torque response of permanent magnet electric traction | Dattatraya kalel, Harshit Mohan and R. Raja Sing -- 11. Non-parametric auto-tuning of PID controllers for DC-DC converters | Ahmed Shehada, Abdul R. Beig and Igor Boiko -- 12. Sliding mode control for DC-DC buck and boost converters | Igor Boiko and Ayman Ismail Al Zawaideh -- 13. Fractional-order controllers in power electronic converters | Allan G. S. Sanchez, Francisco J. Perez-Pinal and Martin A. Rodriguez-Licea.
14. Adjustable speed drive systems for industrial applications | Apparao Dekka, Deepak Ronanki, Ricardo Lizana Fuentes and Venkata Yaramasu -- Index. |
| Record Nr. | UNINA-9911006989803321 |
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Kumar Nayan
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| Paris : , : Institution of Engineering & Technology, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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