Verfügbarkeit: Distributed energy management of electrical power systems

Distributed energy management of electrical power systems:

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Hauptverfasser:	Xu, Yinliang (VerfasserIn), Zhang, Wei (VerfasserIn), Liu, Wenxin 1978- (VerfasserIn), Yu, Wen (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Hoboken, NJ IEEE Press [2021]
Schriftenreihe:	IEEE Press series on power engineering 101
Online-Zugang:	FHA01 FHI01 TUM01 Volltext
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.
Beschreibung:	1 Online-Ressource (xxxii, 299 Seiten) Illustrationen, Diagramme, Pläne
ISBN:	9781119534891 9781119534877 9781119534938
DOI:	10.1002/9781119534938

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MARC


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245	1	0	\|a Distributed energy management of electrical power systems \|c Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu
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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 &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.
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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author	Xu, Yinliang Zhang, Wei Liu, Wenxin 1978- Yu, Wen
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author_facet	Xu, Yinliang Zhang, Wei Liu, Wenxin 1978- Yu, Wen
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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 &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. 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.
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dewey-full	621.31213
dewey-hundreds	600 - Technology (Applied sciences)
dewey-ones	621 - Applied physics
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dewey-search	621.31213
dewey-sort	3621.31213
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discipline	Energietechnik, Energiewirtschaft Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik
discipline_str_mv	Energietechnik, Energiewirtschaft Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik
doi_str_mv	10.1002/9781119534938
format	Electronic eBook
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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	Not Illustrated
index_date	2024-07-03T18:01:24Z
indexdate	2024-07-10T09:12:16Z
institution	BVB
isbn	9781119534891 9781119534877 9781119534938
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-032844691
oclc_num	1227393488
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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
spelling	Xu, Yinliang Verfasser aut Distributed energy management of electrical power systems Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu Hoboken, NJ IEEE Press [2021] © 2021 1 Online-Ressource (xxxii, 299 Seiten) Illustrationen, Diagramme, Pläne txt rdacontent c rdamedia cr rdacarrier IEEE Press series on power engineering 101 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. 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 &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. 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. Zhang, Wei Verfasser aut Liu, Wenxin 1978- Verfasser (DE-588)143657437 aut Yu, Wen Verfasser aut Erscheint auch als Xu, Yinliang Distributed Energy Management of Electrical Power Systems Newark : John Wiley & Sons, Incorporated,c2021 Druck-Ausgabe, Hardcover 978-1-119-53488-4 IEEE Press series on power engineering 101 (DE-604)BV045212694 101 https://doi.org/10.1002/9781119534938 Verlag Volltext
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 &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. 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_exact_search_txtP	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

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