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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
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| Stevenage : , : Institution of Engineering & Technology, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Power System Strength : Evaluation Methods, Best Practice, Case Studies, and Applications
| Power System Strength : Evaluation Methods, Best Practice, Case Studies, and Applications |
| Autore | Alhelou Hassan Haes |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2023 |
| Descrizione fisica | 1 online resource (225 pages) |
| Disciplina | 621.31 |
| Altri autori (Persone) |
HosseinzadehNasser
BahraniBehrooz |
| Collana | Energy Engineering Series |
| Soggetto topico |
Smart power grids - Technological innovations
Electric power systems Réseaux électriques (Énergie) |
| ISBN |
1-83724-439-1
1-5231-6318-6 1-83953-808-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title -- Copyright -- Contents -- About the editors -- Foreword -- Introduction -- 1 Power system strength assessment with high penetration of inverter-based resources - a conceptual approach -- 1.1 Introduction -- 1.2 Work in progress for determination of power system strength in a large grid -- 1.2.1 Method of assessing power system strength -- 1.2.2 Relationship between SCR and power system voltage stability -- 1.2.3 Effect of IBR dynamics on power system strength assessment -- 1.2.4 Outline of a new method for assessing power system strength -- 1.2.5 Summary and future directions -- References -- 2 Power system strength assessment with inverter-based resources: challenges and solutions -- 2.1 Introduction -- 2.2 Power system strength with grid-following inverter and grid-forming inverter and its relation to weak grids -- 2.3 Power system strength definitions -- 2.4 System strength metrics -- 2.4.1 SCR index -- 2.4.2 Weighted short circuit ratio -- 2.4.3 Composite SCR -- 2.4.4 Effective SCR -- 2.4.5 SCR with interaction factors -- 2.4.6 Site-dependent SCR (SDSCR) index -- 2.4.7 Inverter interaction level SCR (IILSCR) -- 2.4.8 Attributes of power system strength assessment methodologies -- 2.5 Impact of power system components on power system strength -- 2.5.1 Impact of phase-locked loops on the system strength -- 2.5.2 Impact of flexible alternating current transmission system devices on the power system strength -- 2.5.3 Impact of synchronous condensers on the system strength -- 2.6 Applicability of SCR index: case study -- 2.6.1 EMT simulations on SCR index -- 2.6.2 Discussion of simulation results -- 2.7 Research gaps and new research directions -- References -- 3 Voltage sensitivity-based system strength metric -- 3.1 Introduction -- 3.2 System description -- 3.3 Power transfer limit of IBR -- 3.3.1 Angle stability limit.
3.3.2 Voltage stability limit -- 3.3.3 Impact of the local load -- 3.3.4 Impact of synchronous condenser -- 3.3.5 Discussion -- 3.4 Simulation results -- 3.5 Discussion -- 3.6 Conclusion -- References -- 4 Dynamic model reduction of power networks for fast assessment of power system strength - part 1: classical techniques -- 4.1 Introduction to system strength -- 4.2 Model reduction strategies -- 4.2.1 Background -- 4.2.2 Overview -- 4.2.3 Classical reduction techniques -- 4.2.4 Classical dynamic equivalent techniques -- 4.2.5 Limitations of classical reduction techniques -- 4.2.6 Research gaps and conclusions -- References -- 5 Dynamic model reduction of IBRs-rich power networks for fast assessment of power system strength - part 2: data-driven techniques -- 5.1 Data-driven techniques -- 5.2 Black-box identification of the ES -- 5.2.1 Non-parametric techniques -- 5.2.2 Parameter estimation techniques -- 5.3 Application of measurement-based techniques to IBR-integrated networks -- 5.3.1 Measurement-based coherency identification -- 5.4 Case study - identification of the ES coherent generators in the AU14G system using the dynamic time warping technique -- 5.5 Measurement-based reduction of wind power plants, solar power plants, microgrids, and ADNs -- 5.5.1 Wind farms -- 5.5.2 Solar farms -- 5.5.3 Microgrids -- 5.5.4 ADNs -- 5.6 Case study: dynamic model reduction of the ES using LSTM recurrent neural networks -- 5.7 Research gaps and conclusions -- References -- 6 Inverter-based resources and their impact on power system inertia and system strength -- 6.1 Introduction -- 6.1.1 What is inertia, and why is it important in the power system? -- 6.1.2 Historical perspectives -- 6.1.3 How IBRs impact power system inertia? -- 6.1.4 How IBRs impact power system strength? -- 6.2 Frequency response and inertia -- 6.3 Inertia requirement. 6.4 Estimation methods of power system inertia -- 6.5 Power system inertia estimation -- 6.6 Case study of a power system with integrated wind energy plant -- 6.6.1 Frequency response to different IBR integration levels -- 6.6.2 System inertia estimation at different IBR integration levels -- 6.7 Research gaps, industry challenges, and future research directions -- 6.7.1 Research gaps -- 6.7.2 Industry challenges -- 6.7.3 Future research directions -- 6.8 Conclusions -- References -- 7 The effect of power system strength on the calculation of available transmission capacity -- 7.1 Introduction -- 7.1.1 The basics of power systems strength -- 7.1.2 Concepts and definitions of ATC -- 7.1.3 Static ATC -- 7.1.4 Dynamic ATC -- 7.2 DATC and holomorphic approach -- 7.2.1 DATC and holomorphic hybrid method -- 7.2.2 Example network -- 7.2.3 Simulation and comparison -- 7.2.4 Wind farms In Iran -- 7.2.5 Approximate NRS algorithm -- 7.2.6 Developed DH algorithm -- 7.2.7 Revised method of holomorphic embedded load flow -- 7.2.8 APEBS method -- 7.2.9 Conclusion -- 7.3 DATC and DELF -- 7.3.1 SATC and DELF -- 7.3.2 DELF -- 7.3.3 AMD method -- 7.4 DATC and HVDC and wind -- 7.4.1 Importance of HVDC network -- 7.4.2 Mathematical model of AC/DC network -- 7.4.3 Solving the AC/DC load flow equation -- 7.4.4 SATC and holomorphic method -- 7.4.5 Conclusion -- 7.5 ATC and state estimation -- 7.6 ATC and cyber security -- 7.6.1 Power system cybersecurity -- 7.6.2 WLS method -- 7.6.3 The suggested algorithm -- 7.6.4 With/without cyberattacks in ATC -- 7.7 Conclusion -- References -- 8 Advanced control approach for providing system strength -- 8.1 Introduction -- 8.2 Fuzzy approximation controller for MIMO system -- 8.2.1 Input-output feedback linearization -- 8.2.2 General MIMO system fuzzy approximation controller -- 8.2.3 Stability of the closed-loop. 8.3 PV grid-connected inverter adaptive fuzzy controller -- 8.3.1 PV grid-connected inverter system model -- 8.3.2 Input-output feedback linearization for PV grid-connected inverter system -- 8.3.3 PV grid-connected inverter adaptive fuzzy controller -- 8.4 Simulation situations and results -- 8.4.1 Situation I: unity power factor -- 8.4.2 Situation II: tracking of reactive current changes -- 8.4.3 Situation III: tracking of active current changes -- 8.4.4 Situation IV: robust tracking -- 8.5 Research gaps and future work -- 8.6 Conclusions -- References -- 9 The impact of renewable energy on voltage stability and fault level -- 9.1 Introduction -- 9.2 Highlights -- 9.3 Power system strength -- 9.4 Short-circuit analysis and converters -- 9.5 Reference grid codes -- 9.6 Iterative short-circuit analysis -- 9.7 The Sicilian grid: a real case study -- 9.8 Procedure testing and dynamic simulations -- 9.9 Fault level of Sicilian power system -- 9.10 Fault level of 100% RES power system -- 9.11 Comparison of grid forming and grid following operation -- 9.12 Future research -- 9.13 Conclusions -- Bibliography -- 10 New smart devices-based strategies for optimal planning and operation of active electric distribution networks -- 10.1 Introduction -- 10.1.1 The AEDN concept -- 10.1.2 The basics of power systems strength -- 10.1.3 Original contributions -- 10.2 Technologies integrated into the AEDNs -- 10.2.1 Advanced meter infrastructure -- 10.2.2 Distributed energy resources -- 10.2.3 Demand response -- 10.2.4 Electric mobility -- 10.3 Smart devices-based strategy in the optimal planning and operation of the AEDNs -- 10.3.1 Database module -- 10.3.2 Decision-making module -- 10.4 Testing the strategy -- 10.5 Research gaps, challenges, and future research directions -- 10.6 Conclusions -- References -- Index. |
| Record Nr. | UNINA-9911007178703321 |
|
Alhelou Hassan Haes
|
||
| Stevenage : , : Institution of Engineering & Technology, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||