000			09826nam a2200661 i 4500
001			9295059
003			IEEE
005			20220712210036.0
006			m o d
007			cr |n|||||||||
008			210105s2020 nju ob 001 eng d
010			_z 2020024731 (print)
020			_a9781119534877 _qadobe pdf
020			_z1119534933 _qelectronic bk. : oBook
020			_z9781119534938 _qelectronic bk. : oBook
020			_z9781119534891 _qePub
020			_z1119534895 _qePub
020			_z1119534879 _qadobe pdf
020			_z9781119534884 _qhardback
024	7		_a10.1002/9781119534938 _2doi
035			_a(CaBNVSL)mat09295059
035			_a(IDAMS)0b0000648d5cf5fd
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050	0	0	_aTK1006
082	0	0	_a621.31/213 _223
100	1		_aXu, Yinliang, _eauthor. _929787
245	1	0	_aDistributed energy management of electrical power systems / _cYinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu.
250			_aFirst edition.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc., _c[2020]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2020]
300			_a1 PDF.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE Press series on power engineering ; _v100
504			_aIncludes bibliographical references and index.
505	0		_a1 Background 1 -- 1.1 Power Management 1 -- 1.2 Traditional centralized vs. distributed solutions to power management 2 -- 1.3 Existing distributed control approaches 3 -- 2 Algorithm Evaluation 5 -- 2.1 Communication Network Topology Configuration 5 -- 2.1.1 Communication Network Design for Distributed Applications 5 -- 2.1.2 N-1 Rule for communication network design 6 -- 2.1.3 Convergence of distributed algorithms with variant communication network typologies -- 2.2 Real-Time Digital Simulation 10 -- 2.2.1 Develop MAS Platform Using JADE 11 -- 2.2.2 Test distributed algorithms using MAS 12 -- 2.2.3 MAS based Real-time Simulation Platform 13 -- References 15 -- 3 Distributed Active Power Control 17 -- 3.1 Subgradient-based Active Power Sharing 17 -- 3.1.1 Introduction 17 -- 3.1.2 Preliminaries-Conventional Droop Control Approach 18 -- 3.1.3 The proposed Subgradient-Based Control Approach 19 -- 3.1.4 Control of Multiple Distributed Generators 24 -- 3.1.5 Simulation analyses 27 -- 3.1.6 Conclusion 33 -- 3.2 Distributed Dynamic Programming Based Approach for Economic Dispatch in Smart Grids 35 -- 3.2.1 Introduction 35 -- 3.2.2 Preliminary 36 -- 3.2.3 Graph theory 36 -- 3.2.4 Dynamic Programming 37 -- 3.2.5 Problem Formulation 37 -- 3.2.6 Economic Dispatch Problem 37 -- 3.2.7 Discrete Economic Dispatch Problem 38 -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm 39 -- 3.2.9 Distributed Dynamic Programming Algorithm 39 -- 3.2.10 Algorithm Implementation 40 -- 3.2.11 Simulation Studies 41 -- 3.2.12 Four-generator system: synchronous iteration 41 -- 3.2.13 Four-generator system: Asynchronous iteration 44 -- 3.2.14 IEEE 162-bus system 46 -- 3.2.15 Hardware Implementation 47 -- 3.2.16 Conclusion 48 -- 3.3 Constrained Distributed Optimal Active Power Dispatch 48 -- 3.3.1 Problem Formulation 49 -- 3.3.2 Distributed Gradient Algorithm 50 -- 3.3.3 Distributed gradient algorithm 50 -- 3.3.4 Inequality Constraints Handling 52 -- 3.3.5 Numerical Example 53 -- 3.3.6 Control Implementation 56.
505	8		_a3.3.7 Communication Network Design 57 -- 3.3.8 Generator Control Implementation 58 -- 3.3.9 Simulation Studies 59 -- 3.3.10 Real-time Simulation Platform 59 -- 3.3.11 IEEE-30 Bus System 59 -- 3.3.12 Conclusion and Discussion 66 -- References 67 -- 4 Distributed Reactive Power Control 73 -- 4.1 Q-Learning based reactive power control 73 -- 4.1.1 Introduction 73 -- 4.1.2 Background 74 -- 4.1.3 Algorithm used to collect global information 74 -- 4.1.4 Reinforcement learning 75 -- 4.1.5 MAS based RL Algorithm for ORPD 75 -- 4.1.6 RL Reward Function Definition 76 -- 4.1.7 Distributed Q-Learning for ORPD 76 -- 4.1.8 MASRL implementation for ORPD 78 -- 4.1.9 Simulation Results 79 -- 4.1.10 The Ward-Hale 6-bus system 79 -- 4.1.11 Conclusion 86 -- 4.2 Sub-gradient Based Reactive Power Control 87 -- 4.2.1 Introduction 87 -- 4.2.2 Problem Formulation 89 -- 4.2.3 The Distributed Sub-gradient Algorithm 90 -- 4.2.4 Subgradient distribution Calculation 91 -- 4.2.5 Realization of Mas-based Solution 94 -- 4.2.6 Simulation and Tests 96 -- 4.2.7 CONCLUSION 105 -- References -- 5 Distributed Demand-Side Management 111 -- 5.1 System Design and Problem Formulation 112 -- 5.1.1 System description and problem formulation 112 -- 5.1.2 Problem Formulation 113 -- 5.1.3 Distributed Dynamic Programming 114 -- 5.1.4 Abstract Framework of Dynamic Programming (DP) 114 -- 5.1.5 Distributed Solution for Dynamic Programming Problem 115 -- 5.1.6 Numerical Example 117 -- 5.1.7 Implementation of the LM system 118 -- 5.1.8 Simulation Studies 119 -- 5.1.9 Test with IEEE 14-bus System 119 -- 5.1.10 Large test Systems 124 -- 5.1.11 Variable Renewable Generation 125 -- 5.1.12 With Time-delay/Packet Loss 126 -- 5.1.13 Conclusion and discussion 127 -- 5.2 Optimal Distributed Charging Rate Control of Plug-in Electric Vehicles for Demand -- Management 128 -- 5.2.1 Background 129 -- 5.2.2 Problem Formulation Of The Proposed Control Strategy 130 -- 5.2.3 Proposed Cooperative Control Algorithm 133 -- 5.2.4 MAS Framework 133.
505	8		_a5.2.5 The design and analysis of distributed algorithm 134 -- 5.2.6 Algorithm Implementation 134 -- 5.2.7 Simulation Studies 136 -- 5.2.8 Case Study 1 136 -- 5.2.9 Case Study 2 138 -- 5.2.10 Case Study 3 139 -- 5.2.11 Conclusion 140 -- References -- 6 Distributed Social Welfare Optimization 145 -- 6.1 Formulation of OEM Problem 146 -- 6.1.1 Social Welfare Maximization Model 147 -- 6.1.2 Market-based Self-interest Motivation Mode 148 -- 6.1.3 Relationship between two models 149 -- 6.2 Fully distributed MAS-Based OEM Solution 151 -- 6.2.1 Distributed Price Updating Algorithm 151 -- 6.2.2 Distributed Supply-demand Mismatch Discovery Algorithm 153 -- 6.2.3 Implementation of MAS-based OEM Solution 153 -- 6.3 Simulation Studies 154 -- 6.3.1 Tests with a 6-bus System 155 -- 6.3.2 Test with IEEE 30-bus System 161 -- 6.4 Conclusion 162 -- References -- 7 Distributed State Estimation 165 -- 7.1 A Distributed Approach for Multi-area State Estimation Based on Consensus Algorithm 165 -- 7.1.1 The Implementation for Distributed State Estimation 171 -- 7.1.2 Case Studies 172 -- 7.1.3 Conclusion and Discussion 175 -- 7.2 Multi-agent System Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of Multi-Area Power System 180 -- 7.2.2 Distributed Subgradient Algorithm for MAS-Based Optimization 181 -- 7.2.3 Distributed Topology Identification 184 -- 7.2.4 Distributed State Estimation 187 -- 7.2.5 Implementation of the Integrated MAS Based Solution for TI and SE188 -- 7.2.6 Simulation Studies 189 -- 7.2.7 Conclusion and Discussion 197 -- References -- 8 Test through Power Hardware in the Loop Experimentation till Real-World Implementation -- 8.1 Steps of Algorithm Evaluation 201 -- 8.2 C-HIL Simulations 202 -- 8.2.1 PC-based C-HIL simulation 203 -- 8.2.2 DSP Control Board based Controller HIL Simulation 205 -- 8.2.3 Power Hardware-In-the-Loop Simulation 207 -- 8.2.4 Hardware Experimentation 209 -- 8.3 Development of Microgrid Test-beds 209.
505	8		_a8.3.1 Development of Modular 3-phase AC microgrid testbed 209 -- 8.3.2 Development of Modular Single-phase AC/DC microgrid testbed 210 -- 8.3.3 Control Algorithm Implementation with the New Microgrid Test-beds 211 -- 8.4 Hardware Experimental Studies 212 -- 8.4.1 Algorithm Design 212 -- 8.4.2 Algorithm Test 213 -- References -- 9 Conclusion and Future Work 217 -- 9.1 Consider More Constraints and Details 217 -- 9.2 Future work 220 References.
506			_aRestricted to subscribers or individual electronic text purchasers.
520			_a"The ever-growing demand, rising penetration level of renewable generation, and increasing complexity of electric power systems, pose new challenges to control, operation, management and optimization of power grids. Conventional centralized control structure requires a complex communication network with two-way communication links and a powerful central controller to process large amount of data, which reduces overall system reliability and increases its sensitivity to failures, thus it may not be able to operate under the increased number of distributed renewable generation units. Distributed control strategy enables easier scalability, simpler communication network, and faster distributed data processing, which can facilitate highly efficient information sharing and decision making"-- _cProvided by publisher.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
650		0	_aDistributed generation of electric power. _913669
650		0	_aElectric power systems _xManagement. _919252
650		0	_aDistributed parameter systems. _97006
655		4	_aElectronic books. _93294
700	1		_aZhang, Wei _c(Engineer), _eauthor. _929788
700	1		_aLiu, Wenxin, _d1978- _eauthor. _929789
700	1		_aYu, Wen, _d1977- _eauthor. _929790
710	2		_aIEEE Xplore (Online Service), _edistributor. _929791
710	2		_aWiley, _epublisher. _929792
776	0	8	_iPrint version: _aXu, Yinliang. _tDistributed energy management of electrical power systems _bFirst edition. _dHoboken, NJ : John Wiley & Sons, Inc., [2020] _z9781119534884 _w(DLC) 2020024730
830		0	_aIEEE Press series on power engineering ; _v100 _97125
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=9295059
942			_cEBK
999			_c74678 _d74678