Distributed energy management of electrical power systems:
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
IEEE Press
[2021]
|
| Schriftenreihe: | IEEE Press series on power engineering
101 |
| Links: | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119534938 https://ieeexplore.ieee.org/servlet/opac?bknumber=9295059 https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6425039 https://doi.org/10.1002/9781119534938 |
| Beschreibung: | Description based on publisher supplied metadata and other sources. - Laut CIP im Impressum Band 100 der Serie, laut Aufstellung am Ende des Dokuments Band 101. |
| Umfang: | 1 Online-Ressource (xxxii, 299 Seiten) Illustrationen, Diagramme, Pläne |
| ISBN: | 9781119534891 9781119534877 9781119534938 |
| DOI: | 10.1002/9781119534938 |
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| 490 | 1 | |a IEEE Press series on power engineering |v 101 | |
| 500 | |a Description based on publisher supplied metadata and other sources. - Laut CIP im Impressum Band 100 der Serie, laut Aufstellung am Ende des Dokuments Band 101. | ||
| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- List of Figures -- List of Tables -- Chapter 1 Background -- 1.1 Power Management -- 1.2 Traditional Centralized vs. Distributed Solutions to Power Management -- 1.3 Existing Distributed Control Approaches -- Chapter 2 Algorithm Evaluation -- 2.1 Communication Network Topology Configuration -- 2.1.1 Communication Network Design for Distributed Applications -- 2.1.2 N − 1 Rule for Communication Network Design -- 2.1.3 Convergence of Distributed Algorithms with Variant Communication Network Typologies -- 2.2 Real‐Time Digital Simulation -- 2.2.1 Develop MAS Platform Using JADE -- 2.2.2 Test‐Distributed Algorithms Using MAS -- 2.2.2.1 Three‐Agent System on the Same Platform -- 2.2.2.2 Two‐Agent System with Different Platforms -- 2.2.3 MAS‐Based Real‐Time Simulation Platform -- References -- Chapter 3 Distributed Active Power Control -- 3.1 Subgradient‐Based Active Power Sharing -- 3.1.1 Introduction -- 3.1.2 Preliminaries ‐ Conventional Droop Control Approach -- 3.1.3 Proposed Subgradient‐Based Control Approach -- 3.1.3.1 Introduction of Utilization Level‐Based Coordination -- 3.1.3.2 Fully Distributed Subgradient‐Based Generation Coordination Algorithm -- 3.1.3.3 Application of the Proposed Algorithm -- 3.1.4 Control of Multiple Distributed Generators -- 3.1.4.1 DFIG Control Approach -- 3.1.4.2 Converter Control Approach -- 3.1.4.3 Pitch Angle Control Approach -- 3.1.4.4 PV Generation Control Approach -- 3.1.4.5 Synchronous Generator Control Approach -- 3.1.5 Simulation Analyses -- 3.1.5.1 Case 1 - Constant Maximum Available Renewable Generation and Load -- 3.1.5.2 Case 2 - Variable Maximum Available Renewable Generation and Load -- 3.1.6 Conclusion -- 3.2 Distributed Dynamic Programming‐Based Approach for Economic Dispatch in Smart Grids | |
| 505 | 8 | |a 3.2.1 Introduction -- 3.2.2 Preliminary -- 3.2.3 Graph Theory -- 3.2.4 Dynamic Programming -- 3.2.5 Problem Formulation -- 3.2.6 Economic Dispatch Problem -- 3.2.7 Discrete Economic Dispatch Problem -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm -- 3.2.9 Distributed Dynamic Programming Algorithm -- 3.2.10 Algorithm Implementation -- 3.2.11 Simulation Studies -- 3.2.12 Four‐generator System: Synchronous Iteration -- 3.2.12.1 Minimum Generation Adjustment Δpi & -- equals -- 2.5 MW -- 3.2.12.2 Minimum Generation Adjustment Δpi & -- equals -- 1.25 MW -- 3.2.13 Four‐Generator System: Asynchronous Iteration -- 3.2.13.1 Missing Communication with Probability -- 3.2.13.2 Gossip Communication -- 3.2.14 IEEE 162‐Bus System -- 3.2.15 Hardware Implementation -- 3.2.16 Conclusion -- 3.3 Constrained Distributed Optimal Active Power Dispatch -- 3.3.1 Introduction -- 3.3.2 Problem Formulation -- 3.3.3 Distributed Gradient Algorithm -- 3.3.4 Distributed Gradient Algorithm -- 3.3.5 Inequality Constraint Handling -- 3.3.6 Numerical Example -- 3.3.6.1 Case 1 -- 3.3.6.2 Case 2 -- 3.3.7 Control Implementation -- 3.3.8 Communication Network Design -- 3.3.9 Generator Control Implementation -- 3.3.10 Simulation Studies -- 3.3.11 Real‐Time Simulation Platform -- 3.3.12 IEEE 30‐Bus System -- 3.3.12.1 Constant Loading Conditions -- 3.3.12.2 Variable Loading Conditions -- 3.3.12.3 With Communication Channel Loss -- 3.3.13 Conclusion and Discussion -- 3.A Appendix -- References -- Chapter 4 Distributed Reactive Power Control -- 4.1 Q‐Learning‐Based Reactive Power Control -- 4.1.1 Introduction -- 4.1.2 Background -- 4.1.3 Algorithm Used to Collect Global Information -- 4.1.4 Reinforcement Learning -- 4.1.5 MAS‐Based RL Algorithm for ORPD -- 4.1.6 RL Reward Function Definition -- 4.1.7 Distributed Q‐Learning for ORPD -- 4.1.8 MASRL Implementation for ORPD. | |
| 505 | 8 | |a 4.1.9 Simulation Results -- 4.1.10 Ward-Hale 6‐Bus System -- 4.1.10.1 Learning from Scratch -- 4.1.10.2 Experience‐Based Learning -- 4.1.10.3 IEEE 30‐Bus System -- 4.1.10.4 IEEE 162‐Bus System -- 4.1.11 Conclusion -- 4.2 Sub‐gradient‐Based Reactive Power Control -- 4.2.1 Introduction -- 4.2.2 Problem Formulation -- 4.2.3 Distributed Sub‐gradient Algorithm -- 4.2.4 Sub‐gradient Distribution Calculation -- 4.2.4.1 Calculation of ∂f/∂Qci for Capacitor Banks -- 4.2.4.2 Calculation of ∂f/∂Vgi for a Generator -- 4.2.4.3 Calculation of ∂f/∂tti for a Transformer -- 4.2.5 Realization of Mas‐Based Solution -- 4.2.5.1 Computation of Voltage Phase Angle Difference -- 4.2.5.2 Generation Control for ORPC -- 4.2.6 Simulation and Tests -- 4.2.6.1 Test of the 6‐Bus Ward-Hale System -- 4.2.6.2 Test of IEEE 30‐Bus System -- 4.2.7 Conclusion -- References -- Chapter 5 Distributed Demand‐Side Management -- 5.1 Distributed Dynamic Programming‐Based Solution for Load Management in Smart Grids -- 5.1.1 System Description and Problem Formulation -- 5.1.2 Problem Formulation -- 5.1.3 Distributed Dynamic Programming -- 5.1.3.1 Abstract Framework of Dynamic Programming (DP) -- 5.1.3.2 Distributed Solution for Dynamic Programming Problem -- 5.1.4 Numerical Example -- 5.1.5 Implementation of the LM System -- 5.1.6 Simulation Studies -- 5.1.6.1 Test with IEEE 14‐bus System -- 5.1.6.2 Large Test Systems -- 5.1.6.3 Variable Renewable Generation -- 5.1.6.4 With Time Delay/Packet Loss -- 5.1.7 Conclusion and Discussion -- 5.2 Optimal Distributed Charging Rate Control of Plug‐in Electric Vehicles for Demand Management -- 5.2.1 Background -- 5.2.2 Problem Formulation of the Proposed Control Strategy -- 5.2.3 Proposed Cooperative Control Algorithm -- 5.2.3.1 MAS Framework -- 5.2.3.2 Design and Analysis of Distributed Algorithm -- 5.2.3.3 Algorithm Implementation | |
| 505 | 8 | |a 5.2.3.4 Simulation Studies -- 5.3 Conclusion -- References -- Chapter 6 Distributed Social Welfare Optimization -- 6.1 Introduction -- 6.2 Formulation of OEM Problem -- 6.2.1 Social Welfare Maximization Model -- 6.2.2 Market‐Based Self‐interest Motivation Model -- 6.2.3 Relationship Between Two Models -- 6.3 Fully Distributed MAS‐Based OEM Solution -- 6.3.1 Distributed Price Updating Algorithm -- 6.3.2 Distributed Supply-Demand Mismatch Discovery Algorithm -- 6.3.3 Implementation of MAS‐Based OEM Solution -- 6.4 Simulation Studies -- 6.4.1 Tests with a 6‐bus System -- 6.4.1.1 Test Under the Constant Renewable Generation -- 6.4.1.2 Test Under Variable Renewable Generation -- 6.4.2 Test with IEEE 30‐bus System -- 6.5 Conclusion -- References -- Chapter 7 Distributed State Estimation -- 7.1 Distributed Approach for Multi‐area State Estimation Based on Consensus Algorithm -- 7.1.1 Problem Formulation of Multi‐area Power System State Estimation -- 7.1.2 Distributed State Estimation Algorithm -- 7.1.3 Approximate Static State Estimation Model -- 7.1.4 Regarding Implementation of Distributed State Estimation -- 7.1.5 Case Studies -- 7.1.5.1 With the Accurate Model -- 7.1.5.2 Comparisons Between Accurate Model and Approximate Model -- 7.1.5.3 With Variable Loading Conditions -- 7.1.6 Conclusion and Discussion -- 7.2 Multi‐agent System‐Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of the Multi‐area Power System -- 7.2.2 Distributed Subgradient Algorithm for MAS‐Based Optimization -- 7.2.3 Distributed Topology Identification -- 7.2.3.1 Measurement Modeling -- 7.2.3.2 Distributed Topology Identification -- 7.2.3.3 Statistical Test for Topology Error Identification -- 7.2.4 Distributed State Estimation -- 7.2.5 Implementation of the Integrated MAS‐Based Solution for TI and SE -- 7.2.6 Simulation Studies | |
| 505 | 8 | |a 7.2.6.1 IEEE 14‐bus System -- 7.2.6.2 Large Test Systems -- 7.3 Conclusion and Discussion -- References -- Chapter 8 Hardware‐Based Algorithms Evaluation -- 8.1 Steps of Algorithm Evaluation -- 8.2 Controller Hardware‐In‐the‐Loop Simulation -- 8.2.1 PC‐Based C‐HIL Simulation -- 8.2.2 DSP‐Based C‐HIL Simulation -- 8.3 Power Hardware‐In‐the‐Loop Simulation -- 8.4 Hardware Experimentation -- 8.4.1 Test‐bed Development -- 8.4.2 Algorithm Implementation -- 8.5 Future Work -- Chapter 9 Discussion and Future Work -- References -- Index -- IEEE Press Series on Power Engineering -- EULA. | |
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| 700 | 1 | |a Liu, Wenxin |d 1978- |e Verfasser |0 (DE-588)143657437 |4 aut | |
| 700 | 1 | |a Yu, Wen |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |a Xu, Yinliang |t Distributed Energy Management of Electrical Power Systems |d Newark : John Wiley & Sons, Incorporated,c2021 |n Druck-Ausgabe, Hardcover |z 978-1-119-53488-4 |
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Datensatz im Suchindex
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| _version_ | 1821935656143683584 |
| any_adam_object | |
| author | Xu, Yinliang Zhang, Wei Liu, Wenxin 1978- Yu, Wen |
| author_GND | (DE-588)143657437 |
| author_facet | Xu, Yinliang Zhang, Wei Liu, Wenxin 1978- Yu, Wen |
| author_role | aut aut aut aut |
| author_sort | Xu, Yinliang |
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| building | Verbundindex |
| bvnumber | BV047442539 |
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| contents | Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- List of Figures -- List of Tables -- Chapter 1 Background -- 1.1 Power Management -- 1.2 Traditional Centralized vs. Distributed Solutions to Power Management -- 1.3 Existing Distributed Control Approaches -- Chapter 2 Algorithm Evaluation -- 2.1 Communication Network Topology Configuration -- 2.1.1 Communication Network Design for Distributed Applications -- 2.1.2 N − 1 Rule for Communication Network Design -- 2.1.3 Convergence of Distributed Algorithms with Variant Communication Network Typologies -- 2.2 Real‐Time Digital Simulation -- 2.2.1 Develop MAS Platform Using JADE -- 2.2.2 Test‐Distributed Algorithms Using MAS -- 2.2.2.1 Three‐Agent System on the Same Platform -- 2.2.2.2 Two‐Agent System with Different Platforms -- 2.2.3 MAS‐Based Real‐Time Simulation Platform -- References -- Chapter 3 Distributed Active Power Control -- 3.1 Subgradient‐Based Active Power Sharing -- 3.1.1 Introduction -- 3.1.2 Preliminaries ‐ Conventional Droop Control Approach -- 3.1.3 Proposed Subgradient‐Based Control Approach -- 3.1.3.1 Introduction of Utilization Level‐Based Coordination -- 3.1.3.2 Fully Distributed Subgradient‐Based Generation Coordination Algorithm -- 3.1.3.3 Application of the Proposed Algorithm -- 3.1.4 Control of Multiple Distributed Generators -- 3.1.4.1 DFIG Control Approach -- 3.1.4.2 Converter Control Approach -- 3.1.4.3 Pitch Angle Control Approach -- 3.1.4.4 PV Generation Control Approach -- 3.1.4.5 Synchronous Generator Control Approach -- 3.1.5 Simulation Analyses -- 3.1.5.1 Case 1 - Constant Maximum Available Renewable Generation and Load -- 3.1.5.2 Case 2 - Variable Maximum Available Renewable Generation and Load -- 3.1.6 Conclusion -- 3.2 Distributed Dynamic Programming‐Based Approach for Economic Dispatch in Smart Grids 3.2.1 Introduction -- 3.2.2 Preliminary -- 3.2.3 Graph Theory -- 3.2.4 Dynamic Programming -- 3.2.5 Problem Formulation -- 3.2.6 Economic Dispatch Problem -- 3.2.7 Discrete Economic Dispatch Problem -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm -- 3.2.9 Distributed Dynamic Programming Algorithm -- 3.2.10 Algorithm Implementation -- 3.2.11 Simulation Studies -- 3.2.12 Four‐generator System: Synchronous Iteration -- 3.2.12.1 Minimum Generation Adjustment Δpi & -- equals -- 2.5 MW -- 3.2.12.2 Minimum Generation Adjustment Δpi & -- equals -- 1.25 MW -- 3.2.13 Four‐Generator System: Asynchronous Iteration -- 3.2.13.1 Missing Communication with Probability -- 3.2.13.2 Gossip Communication -- 3.2.14 IEEE 162‐Bus System -- 3.2.15 Hardware Implementation -- 3.2.16 Conclusion -- 3.3 Constrained Distributed Optimal Active Power Dispatch -- 3.3.1 Introduction -- 3.3.2 Problem Formulation -- 3.3.3 Distributed Gradient Algorithm -- 3.3.4 Distributed Gradient Algorithm -- 3.3.5 Inequality Constraint Handling -- 3.3.6 Numerical Example -- 3.3.6.1 Case 1 -- 3.3.6.2 Case 2 -- 3.3.7 Control Implementation -- 3.3.8 Communication Network Design -- 3.3.9 Generator Control Implementation -- 3.3.10 Simulation Studies -- 3.3.11 Real‐Time Simulation Platform -- 3.3.12 IEEE 30‐Bus System -- 3.3.12.1 Constant Loading Conditions -- 3.3.12.2 Variable Loading Conditions -- 3.3.12.3 With Communication Channel Loss -- 3.3.13 Conclusion and Discussion -- 3.A Appendix -- References -- Chapter 4 Distributed Reactive Power Control -- 4.1 Q‐Learning‐Based Reactive Power Control -- 4.1.1 Introduction -- 4.1.2 Background -- 4.1.3 Algorithm Used to Collect Global Information -- 4.1.4 Reinforcement Learning -- 4.1.5 MAS‐Based RL Algorithm for ORPD -- 4.1.6 RL Reward Function Definition -- 4.1.7 Distributed Q‐Learning for ORPD -- 4.1.8 MASRL Implementation for ORPD. 4.1.9 Simulation Results -- 4.1.10 Ward-Hale 6‐Bus System -- 4.1.10.1 Learning from Scratch -- 4.1.10.2 Experience‐Based Learning -- 4.1.10.3 IEEE 30‐Bus System -- 4.1.10.4 IEEE 162‐Bus System -- 4.1.11 Conclusion -- 4.2 Sub‐gradient‐Based Reactive Power Control -- 4.2.1 Introduction -- 4.2.2 Problem Formulation -- 4.2.3 Distributed Sub‐gradient Algorithm -- 4.2.4 Sub‐gradient Distribution Calculation -- 4.2.4.1 Calculation of ∂f/∂Qci for Capacitor Banks -- 4.2.4.2 Calculation of ∂f/∂Vgi for a Generator -- 4.2.4.3 Calculation of ∂f/∂tti for a Transformer -- 4.2.5 Realization of Mas‐Based Solution -- 4.2.5.1 Computation of Voltage Phase Angle Difference -- 4.2.5.2 Generation Control for ORPC -- 4.2.6 Simulation and Tests -- 4.2.6.1 Test of the 6‐Bus Ward-Hale System -- 4.2.6.2 Test of IEEE 30‐Bus System -- 4.2.7 Conclusion -- References -- Chapter 5 Distributed Demand‐Side Management -- 5.1 Distributed Dynamic Programming‐Based Solution for Load Management in Smart Grids -- 5.1.1 System Description and Problem Formulation -- 5.1.2 Problem Formulation -- 5.1.3 Distributed Dynamic Programming -- 5.1.3.1 Abstract Framework of Dynamic Programming (DP) -- 5.1.3.2 Distributed Solution for Dynamic Programming Problem -- 5.1.4 Numerical Example -- 5.1.5 Implementation of the LM System -- 5.1.6 Simulation Studies -- 5.1.6.1 Test with IEEE 14‐bus System -- 5.1.6.2 Large Test Systems -- 5.1.6.3 Variable Renewable Generation -- 5.1.6.4 With Time Delay/Packet Loss -- 5.1.7 Conclusion and Discussion -- 5.2 Optimal Distributed Charging Rate Control of Plug‐in Electric Vehicles for Demand Management -- 5.2.1 Background -- 5.2.2 Problem Formulation of the Proposed Control Strategy -- 5.2.3 Proposed Cooperative Control Algorithm -- 5.2.3.1 MAS Framework -- 5.2.3.2 Design and Analysis of Distributed Algorithm -- 5.2.3.3 Algorithm Implementation 5.2.3.4 Simulation Studies -- 5.3 Conclusion -- References -- Chapter 6 Distributed Social Welfare Optimization -- 6.1 Introduction -- 6.2 Formulation of OEM Problem -- 6.2.1 Social Welfare Maximization Model -- 6.2.2 Market‐Based Self‐interest Motivation Model -- 6.2.3 Relationship Between Two Models -- 6.3 Fully Distributed MAS‐Based OEM Solution -- 6.3.1 Distributed Price Updating Algorithm -- 6.3.2 Distributed Supply-Demand Mismatch Discovery Algorithm -- 6.3.3 Implementation of MAS‐Based OEM Solution -- 6.4 Simulation Studies -- 6.4.1 Tests with a 6‐bus System -- 6.4.1.1 Test Under the Constant Renewable Generation -- 6.4.1.2 Test Under Variable Renewable Generation -- 6.4.2 Test with IEEE 30‐bus System -- 6.5 Conclusion -- References -- Chapter 7 Distributed State Estimation -- 7.1 Distributed Approach for Multi‐area State Estimation Based on Consensus Algorithm -- 7.1.1 Problem Formulation of Multi‐area Power System State Estimation -- 7.1.2 Distributed State Estimation Algorithm -- 7.1.3 Approximate Static State Estimation Model -- 7.1.4 Regarding Implementation of Distributed State Estimation -- 7.1.5 Case Studies -- 7.1.5.1 With the Accurate Model -- 7.1.5.2 Comparisons Between Accurate Model and Approximate Model -- 7.1.5.3 With Variable Loading Conditions -- 7.1.6 Conclusion and Discussion -- 7.2 Multi‐agent System‐Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of the Multi‐area Power System -- 7.2.2 Distributed Subgradient Algorithm for MAS‐Based Optimization -- 7.2.3 Distributed Topology Identification -- 7.2.3.1 Measurement Modeling -- 7.2.3.2 Distributed Topology Identification -- 7.2.3.3 Statistical Test for Topology Error Identification -- 7.2.4 Distributed State Estimation -- 7.2.5 Implementation of the Integrated MAS‐Based Solution for TI and SE -- 7.2.6 Simulation Studies 7.2.6.1 IEEE 14‐bus System -- 7.2.6.2 Large Test Systems -- 7.3 Conclusion and Discussion -- References -- Chapter 8 Hardware‐Based Algorithms Evaluation -- 8.1 Steps of Algorithm Evaluation -- 8.2 Controller Hardware‐In‐the‐Loop Simulation -- 8.2.1 PC‐Based C‐HIL Simulation -- 8.2.2 DSP‐Based C‐HIL Simulation -- 8.3 Power Hardware‐In‐the‐Loop Simulation -- 8.4 Hardware Experimentation -- 8.4.1 Test‐bed Development -- 8.4.2 Algorithm Implementation -- 8.5 Future Work -- Chapter 9 Discussion and Future Work -- References -- Index -- IEEE Press Series on Power Engineering -- EULA. |
| ctrlnum | (ZDB-30-PQE)EBC6425039 (ZDB-30-PAD)EBC6425039 (ZDB-89-EBL)EBL6425039 (OCoLC)1227393488 (DE-599)BVBBV047442539 |
| dewey-full | 621.31213 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.31213 |
| dewey-search | 621.31213 |
| dewey-sort | 3621.31213 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik Energietechnik |
| doi_str_mv | 10.1002/9781119534938 |
| format | Electronic eBook |
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ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">IEEE Press series on power engineering</subfield><subfield code="v">101</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources. - Laut CIP im Impressum Band 100 der Serie, laut Aufstellung am Ende des Dokuments Band 101.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- List of Figures -- List of Tables -- Chapter 1 Background -- 1.1 Power Management -- 1.2 Traditional Centralized vs. Distributed Solutions to Power Management -- 1.3 Existing Distributed Control Approaches -- Chapter 2 Algorithm Evaluation -- 2.1 Communication Network Topology Configuration -- 2.1.1 Communication Network Design for Distributed Applications -- 2.1.2 N − 1 Rule for Communication Network Design -- 2.1.3 Convergence of Distributed Algorithms with Variant Communication Network Typologies -- 2.2 Real‐Time Digital Simulation -- 2.2.1 Develop MAS Platform Using JADE -- 2.2.2 Test‐Distributed Algorithms Using MAS -- 2.2.2.1 Three‐Agent System on the Same Platform -- 2.2.2.2 Two‐Agent System with Different Platforms -- 2.2.3 MAS‐Based Real‐Time Simulation Platform -- References -- Chapter 3 Distributed Active Power Control -- 3.1 Subgradient‐Based Active Power Sharing -- 3.1.1 Introduction -- 3.1.2 Preliminaries ‐ Conventional Droop Control Approach -- 3.1.3 Proposed Subgradient‐Based Control Approach -- 3.1.3.1 Introduction of Utilization Level‐Based Coordination -- 3.1.3.2 Fully Distributed Subgradient‐Based Generation Coordination Algorithm -- 3.1.3.3 Application of the Proposed Algorithm -- 3.1.4 Control of Multiple Distributed Generators -- 3.1.4.1 DFIG Control Approach -- 3.1.4.2 Converter Control Approach -- 3.1.4.3 Pitch Angle Control Approach -- 3.1.4.4 PV Generation Control Approach -- 3.1.4.5 Synchronous Generator Control Approach -- 3.1.5 Simulation Analyses -- 3.1.5.1 Case 1 - Constant Maximum Available Renewable Generation and Load -- 3.1.5.2 Case 2 - Variable Maximum Available Renewable Generation and Load -- 3.1.6 Conclusion -- 3.2 Distributed Dynamic Programming‐Based Approach for Economic Dispatch in Smart Grids</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.2.1 Introduction -- 3.2.2 Preliminary -- 3.2.3 Graph Theory -- 3.2.4 Dynamic Programming -- 3.2.5 Problem Formulation -- 3.2.6 Economic Dispatch Problem -- 3.2.7 Discrete Economic Dispatch Problem -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm -- 3.2.9 Distributed Dynamic Programming Algorithm -- 3.2.10 Algorithm Implementation -- 3.2.11 Simulation Studies -- 3.2.12 Four‐generator System: Synchronous Iteration -- 3.2.12.1 Minimum Generation Adjustment Δpi &amp -- equals -- 2.5 MW -- 3.2.12.2 Minimum Generation Adjustment Δpi &amp -- equals -- 1.25 MW -- 3.2.13 Four‐Generator System: Asynchronous Iteration -- 3.2.13.1 Missing Communication with Probability -- 3.2.13.2 Gossip Communication -- 3.2.14 IEEE 162‐Bus System -- 3.2.15 Hardware Implementation -- 3.2.16 Conclusion -- 3.3 Constrained Distributed Optimal Active Power Dispatch -- 3.3.1 Introduction -- 3.3.2 Problem Formulation -- 3.3.3 Distributed Gradient Algorithm -- 3.3.4 Distributed Gradient Algorithm -- 3.3.5 Inequality Constraint Handling -- 3.3.6 Numerical Example -- 3.3.6.1 Case 1 -- 3.3.6.2 Case 2 -- 3.3.7 Control Implementation -- 3.3.8 Communication Network Design -- 3.3.9 Generator Control Implementation -- 3.3.10 Simulation Studies -- 3.3.11 Real‐Time Simulation Platform -- 3.3.12 IEEE 30‐Bus System -- 3.3.12.1 Constant Loading Conditions -- 3.3.12.2 Variable Loading Conditions -- 3.3.12.3 With Communication Channel Loss -- 3.3.13 Conclusion and Discussion -- 3.A Appendix -- References -- Chapter 4 Distributed Reactive Power Control -- 4.1 Q‐Learning‐Based Reactive Power Control -- 4.1.1 Introduction -- 4.1.2 Background -- 4.1.3 Algorithm Used to Collect Global Information -- 4.1.4 Reinforcement Learning -- 4.1.5 MAS‐Based RL Algorithm for ORPD -- 4.1.6 RL Reward Function Definition -- 4.1.7 Distributed Q‐Learning for ORPD -- 4.1.8 MASRL Implementation for ORPD.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.1.9 Simulation Results -- 4.1.10 Ward-Hale 6‐Bus System -- 4.1.10.1 Learning from Scratch -- 4.1.10.2 Experience‐Based Learning -- 4.1.10.3 IEEE 30‐Bus System -- 4.1.10.4 IEEE 162‐Bus System -- 4.1.11 Conclusion -- 4.2 Sub‐gradient‐Based Reactive Power Control -- 4.2.1 Introduction -- 4.2.2 Problem Formulation -- 4.2.3 Distributed Sub‐gradient Algorithm -- 4.2.4 Sub‐gradient Distribution Calculation -- 4.2.4.1 Calculation of ∂f/∂Qci for Capacitor Banks -- 4.2.4.2 Calculation of ∂f/∂Vgi for a Generator -- 4.2.4.3 Calculation of ∂f/∂tti for a Transformer -- 4.2.5 Realization of Mas‐Based Solution -- 4.2.5.1 Computation of Voltage Phase Angle Difference -- 4.2.5.2 Generation Control for ORPC -- 4.2.6 Simulation and Tests -- 4.2.6.1 Test of the 6‐Bus Ward-Hale System -- 4.2.6.2 Test of IEEE 30‐Bus System -- 4.2.7 Conclusion -- References -- Chapter 5 Distributed Demand‐Side Management -- 5.1 Distributed Dynamic Programming‐Based Solution for Load Management in Smart Grids -- 5.1.1 System Description and Problem Formulation -- 5.1.2 Problem Formulation -- 5.1.3 Distributed Dynamic Programming -- 5.1.3.1 Abstract Framework of Dynamic Programming (DP) -- 5.1.3.2 Distributed Solution for Dynamic Programming Problem -- 5.1.4 Numerical Example -- 5.1.5 Implementation of the LM System -- 5.1.6 Simulation Studies -- 5.1.6.1 Test with IEEE 14‐bus System -- 5.1.6.2 Large Test Systems -- 5.1.6.3 Variable Renewable Generation -- 5.1.6.4 With Time Delay/Packet Loss -- 5.1.7 Conclusion and Discussion -- 5.2 Optimal Distributed Charging Rate Control of Plug‐in Electric Vehicles for Demand Management -- 5.2.1 Background -- 5.2.2 Problem Formulation of the Proposed Control Strategy -- 5.2.3 Proposed Cooperative Control Algorithm -- 5.2.3.1 MAS Framework -- 5.2.3.2 Design and Analysis of Distributed Algorithm -- 5.2.3.3 Algorithm Implementation</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.2.3.4 Simulation Studies -- 5.3 Conclusion -- References -- Chapter 6 Distributed Social Welfare Optimization -- 6.1 Introduction -- 6.2 Formulation of OEM Problem -- 6.2.1 Social Welfare Maximization Model -- 6.2.2 Market‐Based Self‐interest Motivation Model -- 6.2.3 Relationship Between Two Models -- 6.3 Fully Distributed MAS‐Based OEM Solution -- 6.3.1 Distributed Price Updating Algorithm -- 6.3.2 Distributed Supply-Demand Mismatch Discovery Algorithm -- 6.3.3 Implementation of MAS‐Based OEM Solution -- 6.4 Simulation Studies -- 6.4.1 Tests with a 6‐bus System -- 6.4.1.1 Test Under the Constant Renewable Generation -- 6.4.1.2 Test Under Variable Renewable Generation -- 6.4.2 Test with IEEE 30‐bus System -- 6.5 Conclusion -- References -- Chapter 7 Distributed State Estimation -- 7.1 Distributed Approach for Multi‐area State Estimation Based on Consensus Algorithm -- 7.1.1 Problem Formulation of Multi‐area Power System State Estimation -- 7.1.2 Distributed State Estimation Algorithm -- 7.1.3 Approximate Static State Estimation Model -- 7.1.4 Regarding Implementation of Distributed State Estimation -- 7.1.5 Case Studies -- 7.1.5.1 With the Accurate Model -- 7.1.5.2 Comparisons Between Accurate Model and Approximate Model -- 7.1.5.3 With Variable Loading Conditions -- 7.1.6 Conclusion and Discussion -- 7.2 Multi‐agent System‐Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of the Multi‐area Power System -- 7.2.2 Distributed Subgradient Algorithm for MAS‐Based Optimization -- 7.2.3 Distributed Topology Identification -- 7.2.3.1 Measurement Modeling -- 7.2.3.2 Distributed Topology Identification -- 7.2.3.3 Statistical Test for Topology Error Identification -- 7.2.4 Distributed State Estimation -- 7.2.5 Implementation of the Integrated MAS‐Based Solution for TI and SE -- 7.2.6 Simulation Studies</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.2.6.1 IEEE 14‐bus System -- 7.2.6.2 Large Test Systems -- 7.3 Conclusion and Discussion -- References -- Chapter 8 Hardware‐Based Algorithms Evaluation -- 8.1 Steps of Algorithm Evaluation -- 8.2 Controller Hardware‐In‐the‐Loop Simulation -- 8.2.1 PC‐Based C‐HIL Simulation -- 8.2.2 DSP‐Based C‐HIL Simulation -- 8.3 Power Hardware‐In‐the‐Loop Simulation -- 8.4 Hardware Experimentation -- 8.4.1 Test‐bed Development -- 8.4.2 Algorithm Implementation -- 8.5 Future Work -- Chapter 9 Discussion and Future Work -- References -- Index -- IEEE Press Series on Power Engineering -- EULA.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Wei</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Wenxin</subfield><subfield code="d">1978-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)143657437</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Wen</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="a">Xu, Yinliang</subfield><subfield code="t">Distributed Energy Management of Electrical Power Systems</subfield><subfield code="d">Newark : John Wiley & Sons, Incorporated,c2021</subfield><subfield code="n">Druck-Ausgabe, 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| id | DE-604.BV047442539 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:19:41Z |
| institution | BVB |
| isbn | 9781119534891 9781119534877 9781119534938 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844691 |
| oclc_num | 1227393488 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-Aug4 DE-573 |
| owner_facet | DE-91 DE-BY-TUM DE-Aug4 DE-573 |
| physical | 1 Online-Ressource (xxxii, 299 Seiten) Illustrationen, Diagramme, Pläne |
| psigel | ZDB-30-PQE ZDB-35-WIC ZDB-35-WEL ZDB-35-WIC FHA_PDA_WIC_Kauf ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | IEEE Press |
| record_format | marc |
| series | IEEE Press series on power engineering |
| series2 | IEEE Press series on power engineering |
| spellingShingle | Xu, Yinliang Zhang, Wei Liu, Wenxin 1978- Yu, Wen Distributed energy management of electrical power systems IEEE Press series on power engineering Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- List of Figures -- List of Tables -- Chapter 1 Background -- 1.1 Power Management -- 1.2 Traditional Centralized vs. Distributed Solutions to Power Management -- 1.3 Existing Distributed Control Approaches -- Chapter 2 Algorithm Evaluation -- 2.1 Communication Network Topology Configuration -- 2.1.1 Communication Network Design for Distributed Applications -- 2.1.2 N − 1 Rule for Communication Network Design -- 2.1.3 Convergence of Distributed Algorithms with Variant Communication Network Typologies -- 2.2 Real‐Time Digital Simulation -- 2.2.1 Develop MAS Platform Using JADE -- 2.2.2 Test‐Distributed Algorithms Using MAS -- 2.2.2.1 Three‐Agent System on the Same Platform -- 2.2.2.2 Two‐Agent System with Different Platforms -- 2.2.3 MAS‐Based Real‐Time Simulation Platform -- References -- Chapter 3 Distributed Active Power Control -- 3.1 Subgradient‐Based Active Power Sharing -- 3.1.1 Introduction -- 3.1.2 Preliminaries ‐ Conventional Droop Control Approach -- 3.1.3 Proposed Subgradient‐Based Control Approach -- 3.1.3.1 Introduction of Utilization Level‐Based Coordination -- 3.1.3.2 Fully Distributed Subgradient‐Based Generation Coordination Algorithm -- 3.1.3.3 Application of the Proposed Algorithm -- 3.1.4 Control of Multiple Distributed Generators -- 3.1.4.1 DFIG Control Approach -- 3.1.4.2 Converter Control Approach -- 3.1.4.3 Pitch Angle Control Approach -- 3.1.4.4 PV Generation Control Approach -- 3.1.4.5 Synchronous Generator Control Approach -- 3.1.5 Simulation Analyses -- 3.1.5.1 Case 1 - Constant Maximum Available Renewable Generation and Load -- 3.1.5.2 Case 2 - Variable Maximum Available Renewable Generation and Load -- 3.1.6 Conclusion -- 3.2 Distributed Dynamic Programming‐Based Approach for Economic Dispatch in Smart Grids 3.2.1 Introduction -- 3.2.2 Preliminary -- 3.2.3 Graph Theory -- 3.2.4 Dynamic Programming -- 3.2.5 Problem Formulation -- 3.2.6 Economic Dispatch Problem -- 3.2.7 Discrete Economic Dispatch Problem -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm -- 3.2.9 Distributed Dynamic Programming Algorithm -- 3.2.10 Algorithm Implementation -- 3.2.11 Simulation Studies -- 3.2.12 Four‐generator System: Synchronous Iteration -- 3.2.12.1 Minimum Generation Adjustment Δpi & -- equals -- 2.5 MW -- 3.2.12.2 Minimum Generation Adjustment Δpi & -- equals -- 1.25 MW -- 3.2.13 Four‐Generator System: Asynchronous Iteration -- 3.2.13.1 Missing Communication with Probability -- 3.2.13.2 Gossip Communication -- 3.2.14 IEEE 162‐Bus System -- 3.2.15 Hardware Implementation -- 3.2.16 Conclusion -- 3.3 Constrained Distributed Optimal Active Power Dispatch -- 3.3.1 Introduction -- 3.3.2 Problem Formulation -- 3.3.3 Distributed Gradient Algorithm -- 3.3.4 Distributed Gradient Algorithm -- 3.3.5 Inequality Constraint Handling -- 3.3.6 Numerical Example -- 3.3.6.1 Case 1 -- 3.3.6.2 Case 2 -- 3.3.7 Control Implementation -- 3.3.8 Communication Network Design -- 3.3.9 Generator Control Implementation -- 3.3.10 Simulation Studies -- 3.3.11 Real‐Time Simulation Platform -- 3.3.12 IEEE 30‐Bus System -- 3.3.12.1 Constant Loading Conditions -- 3.3.12.2 Variable Loading Conditions -- 3.3.12.3 With Communication Channel Loss -- 3.3.13 Conclusion and Discussion -- 3.A Appendix -- References -- Chapter 4 Distributed Reactive Power Control -- 4.1 Q‐Learning‐Based Reactive Power Control -- 4.1.1 Introduction -- 4.1.2 Background -- 4.1.3 Algorithm Used to Collect Global Information -- 4.1.4 Reinforcement Learning -- 4.1.5 MAS‐Based RL Algorithm for ORPD -- 4.1.6 RL Reward Function Definition -- 4.1.7 Distributed Q‐Learning for ORPD -- 4.1.8 MASRL Implementation for ORPD. 4.1.9 Simulation Results -- 4.1.10 Ward-Hale 6‐Bus System -- 4.1.10.1 Learning from Scratch -- 4.1.10.2 Experience‐Based Learning -- 4.1.10.3 IEEE 30‐Bus System -- 4.1.10.4 IEEE 162‐Bus System -- 4.1.11 Conclusion -- 4.2 Sub‐gradient‐Based Reactive Power Control -- 4.2.1 Introduction -- 4.2.2 Problem Formulation -- 4.2.3 Distributed Sub‐gradient Algorithm -- 4.2.4 Sub‐gradient Distribution Calculation -- 4.2.4.1 Calculation of ∂f/∂Qci for Capacitor Banks -- 4.2.4.2 Calculation of ∂f/∂Vgi for a Generator -- 4.2.4.3 Calculation of ∂f/∂tti for a Transformer -- 4.2.5 Realization of Mas‐Based Solution -- 4.2.5.1 Computation of Voltage Phase Angle Difference -- 4.2.5.2 Generation Control for ORPC -- 4.2.6 Simulation and Tests -- 4.2.6.1 Test of the 6‐Bus Ward-Hale System -- 4.2.6.2 Test of IEEE 30‐Bus System -- 4.2.7 Conclusion -- References -- Chapter 5 Distributed Demand‐Side Management -- 5.1 Distributed Dynamic Programming‐Based Solution for Load Management in Smart Grids -- 5.1.1 System Description and Problem Formulation -- 5.1.2 Problem Formulation -- 5.1.3 Distributed Dynamic Programming -- 5.1.3.1 Abstract Framework of Dynamic Programming (DP) -- 5.1.3.2 Distributed Solution for Dynamic Programming Problem -- 5.1.4 Numerical Example -- 5.1.5 Implementation of the LM System -- 5.1.6 Simulation Studies -- 5.1.6.1 Test with IEEE 14‐bus System -- 5.1.6.2 Large Test Systems -- 5.1.6.3 Variable Renewable Generation -- 5.1.6.4 With Time Delay/Packet Loss -- 5.1.7 Conclusion and Discussion -- 5.2 Optimal Distributed Charging Rate Control of Plug‐in Electric Vehicles for Demand Management -- 5.2.1 Background -- 5.2.2 Problem Formulation of the Proposed Control Strategy -- 5.2.3 Proposed Cooperative Control Algorithm -- 5.2.3.1 MAS Framework -- 5.2.3.2 Design and Analysis of Distributed Algorithm -- 5.2.3.3 Algorithm Implementation 5.2.3.4 Simulation Studies -- 5.3 Conclusion -- References -- Chapter 6 Distributed Social Welfare Optimization -- 6.1 Introduction -- 6.2 Formulation of OEM Problem -- 6.2.1 Social Welfare Maximization Model -- 6.2.2 Market‐Based Self‐interest Motivation Model -- 6.2.3 Relationship Between Two Models -- 6.3 Fully Distributed MAS‐Based OEM Solution -- 6.3.1 Distributed Price Updating Algorithm -- 6.3.2 Distributed Supply-Demand Mismatch Discovery Algorithm -- 6.3.3 Implementation of MAS‐Based OEM Solution -- 6.4 Simulation Studies -- 6.4.1 Tests with a 6‐bus System -- 6.4.1.1 Test Under the Constant Renewable Generation -- 6.4.1.2 Test Under Variable Renewable Generation -- 6.4.2 Test with IEEE 30‐bus System -- 6.5 Conclusion -- References -- Chapter 7 Distributed State Estimation -- 7.1 Distributed Approach for Multi‐area State Estimation Based on Consensus Algorithm -- 7.1.1 Problem Formulation of Multi‐area Power System State Estimation -- 7.1.2 Distributed State Estimation Algorithm -- 7.1.3 Approximate Static State Estimation Model -- 7.1.4 Regarding Implementation of Distributed State Estimation -- 7.1.5 Case Studies -- 7.1.5.1 With the Accurate Model -- 7.1.5.2 Comparisons Between Accurate Model and Approximate Model -- 7.1.5.3 With Variable Loading Conditions -- 7.1.6 Conclusion and Discussion -- 7.2 Multi‐agent System‐Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of the Multi‐area Power System -- 7.2.2 Distributed Subgradient Algorithm for MAS‐Based Optimization -- 7.2.3 Distributed Topology Identification -- 7.2.3.1 Measurement Modeling -- 7.2.3.2 Distributed Topology Identification -- 7.2.3.3 Statistical Test for Topology Error Identification -- 7.2.4 Distributed State Estimation -- 7.2.5 Implementation of the Integrated MAS‐Based Solution for TI and SE -- 7.2.6 Simulation Studies 7.2.6.1 IEEE 14‐bus System -- 7.2.6.2 Large Test Systems -- 7.3 Conclusion and Discussion -- References -- Chapter 8 Hardware‐Based Algorithms Evaluation -- 8.1 Steps of Algorithm Evaluation -- 8.2 Controller Hardware‐In‐the‐Loop Simulation -- 8.2.1 PC‐Based C‐HIL Simulation -- 8.2.2 DSP‐Based C‐HIL Simulation -- 8.3 Power Hardware‐In‐the‐Loop Simulation -- 8.4 Hardware Experimentation -- 8.4.1 Test‐bed Development -- 8.4.2 Algorithm Implementation -- 8.5 Future Work -- Chapter 9 Discussion and Future Work -- References -- Index -- IEEE Press Series on Power Engineering -- EULA. |
| title | Distributed energy management of electrical power systems |
| title_auth | Distributed energy management of electrical power systems |
| title_exact_search | Distributed energy management of electrical power systems |
| title_full | Distributed energy management of electrical power systems Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu |
| title_fullStr | Distributed energy management of electrical power systems Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu |
| title_full_unstemmed | Distributed energy management of electrical power systems Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu |
| title_short | Distributed energy management of electrical power systems |
| title_sort | distributed energy management of electrical power systems |
| url | https://doi.org/10.1002/9781119534938 |
| volume_link | (DE-604)BV045212694 |
| work_keys_str_mv | AT xuyinliang distributedenergymanagementofelectricalpowersystems AT zhangwei distributedenergymanagementofelectricalpowersystems AT liuwenxin distributedenergymanagementofelectricalpowersystems AT yuwen distributedenergymanagementofelectricalpowersystems |