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E-raamat: Distributed Energy Management of Electrical Power Systems [Wiley Online]

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Distributed Energy Management of Electrical Power SystemsSuurem pilt
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119534938
"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"--

Go in-depth with this comprehensive discussion of distributed energy management

Distributed Energy Management of Electrical Power Systems provides the most complete analysis of fully distributed control approaches and their applications for electric power systems available today. Authored by four respected leaders in the field, the book covers the technical aspects of control, operation management, and optimization of electric power systems.

In each chapter, the book covers the foundations and fundamentals of the topic under discussion. It then moves on to more advanced applications. Topics reviewed in the book include:

  • System-level coordinated control
  • Optimization of active and reactive power in power grids
  • The coordinated control of distributed generation, elastic load and energy storage systems

Distributed Energy Management incorporates discussions of emerging and future technologies and their potential effects on electrical power systems. The increased impact of renewable energy sources is also covered.

Perfect for industry practitioners and graduate students in the field of power systems, Distributed Energy Management remains the leading reference for anyone with an interest in its fascinating subject matter.

About the Authors xiii
Preface xv
Acknowledgment xix
List of Figures
xxi
List of Tables
xxxi
1 Background
1(8)
1.1 Power Management
1(3)
1.2 Traditional Centralized vs. Distributed Solutions to Power Management
4(1)
1.3 Existing Distributed Control Approaches
5(4)
2 Algorithm Evaluation
9(14)
2.1 Communication Network Topology Configuration
9(7)
2.1.1 Communication Network Design for Distributed Applications
9(2)
2.1.2 N-1 Rule for Communication Network Design
11(2)
2.1.3 Convergence of Distributed Algorithms with Variant Communication Network Typologies
13(3)
2.2 Real-Time Digital Simulation
16(7)
2.2.1 Develop MAS Platform Using JADE
16(2)
2.2.2 Test-Distributed Algorithms Using MAS
18(1)
2.2.2.1 Three-Agent System on the Same Platform
18(1)
2.2.2.2 Two-Agent System with Different Platforms
19(1)
2.2.3 MAS-Based Real-Time Simulation Platform
20(2)
References
22(1)
3 Distributed Active Power Control
23(74)
3.1 Subgradient-Based Active Power Sharing
23(23)
3.1.1 Introduction
24(2)
3.1.2 Preliminaries - Conventional Droop Control Approach
26(1)
3.1.3 Proposed Subgradient-Based Control Approach
27(1)
3.1.3.1 Introduction of Utilization Level-Based Coordination
27(1)
3.1.3.2 Fully Distributed Subgradient-Based Generation Coordination Algorithm
28(3)
3.1.3.3 Application of the Proposed Algorithm
31(2)
3.1.4 Control of Multiple Distributed Generators
33(1)
3.1.4.1 DFIG Control Approach
33(1)
3.1.4.2 Converter Control Approach
34(1)
3.1.4.3 Pitch Angle Control Approach
35(1)
3.1.4.4 PV Generation Control Approach
36(1)
3.1.4.5 Synchronous Generator Control Approach
36(1)
3.1.5 Simulation Analyses
37(1)
3.1.5.1 Case 1 - Constant Maximum Available Renewable Generation and Load
38(3)
3.1.5.2 Case 2 - Variable Maximum Available Renewable Generation and Load
41(4)
3.1.6 Conclusion
45(1)
3.2 Distributed Dynamic Programming-Based Approach for Economic Dispatch in Smart Grids
46(19)
3.2.1 Introduction
46(3)
3.2.2 Preliminary
49(1)
3.2.3 Graph Theory
49(1)
3.2.4 Dynamic Programming
49(1)
3.2.5 Problem Formulation
49(1)
3.2.6 Economic Dispatch Problem
50(1)
3.2.7 Discrete Economic Dispatch Problem
50(1)
3.2.8 Proposed Distributed Dynamic Programming Algorithm
51(1)
3.2.9 Distributed Dynamic Programming Algorithm
52(1)
3.2.10 Algorithm Implementation
53(1)
3.2.11 Simulation Studies
54(1)
3.2.12 Four-generator System: Synchronous Iteration
54(1)
3.2.12.1 Minimum Generation Adjustment Ap; = 2.5 MW
54(3)
3.2.12.2 Minimum Generation Adjustment Apf = 1.25 MW
57(2)
3.2.13 Four-Generator System: Asynchronous Iteration
59(1)
3.2.13.1 Missing Communication with Probability
59(1)
3.2.13.2 Gossip Communication
60(1)
3.2.14 IEEE 162-Bus System
61(2)
3.2.15 Hardware Implementation
63(1)
3.2.16 Conclusion
64(1)
3.3 Constrained Distributed Optimal Active Power Dispatch
65(21)
3.3.1 Introduction
65(2)
3.3.2 Problem Formulation
67(1)
3.3.3 Distributed Gradient Algorithm
68(1)
3.3.4 Distributed Gradient Algorithm
68(2)
3.3.5 Inequality Constraint Handling
70(2)
3.3.6 Numerical Example
72(1)
3.3.6.1 Case 1
72(2)
3.3.6.2 Case 2
74(1)
3.3.7 Control Implementation
75(1)
3.3.8 Communication Network Design
76(1)
3.3.9 Generator Control Implementation
76(1)
3.3.10 Simulation Studies
77(1)
3.3.11 Real-Time Simulation Platform
78(1)
3.3.12 IEEE 30-Bus System
78(2)
3.3.12.1 Constant Loading Conditions
80(2)
3.3.12.2 Variable Loading Conditions
82(2)
3.3.12.3 With Communication Channel Loss
84(2)
3.3.13 Conclusion and Discussion
86(1)
3.4 Appendix
86(11)
References
87(10)
4 Distributed Reactive Power Control
97(50)
4.1 Q-Learning-Based Reactive Power Control
97(19)
4.1.1 Introduction
98(1)
4.1.2 Background
99(1)
4.1.3 Algorithm Used to Collect Global Information
99(2)
4.1.4 Reinforcement Learning
101(1)
4.1.5 MAS-Based RL Algorithm for ORPD
101(1)
4.1.6 RL Reward Function Definition
102(1)
4.1.7 Distributed Q-Learning for ORPD
103(1)
4.1.8 MASRL Implementation for ORPD
104(2)
4.1.9 Simulation Results
106(1)
4.1.10 Ward-Hale 6-Bus System
106(2)
4.1.10.1 Learning from Scratch
108(2)
4.1.10.2 Experience-Based Learning
110(2)
4.1.10.3 IEEE 30-Bus System
112(1)
4.1.10.4 IEEE 162-Bus System
113(2)
4.1.11 Conclusion
115(1)
4.2 Sub-gradient-Based Reactive Power Control
116(31)
4.2.1 Introduction
116(3)
4.2.2 Problem Formulation
119(1)
4.2.3 Distributed Sub-gradient Algorithm
120(2)
4.2.4 Sub-gradient Distribution Calculation
122(1)
4.2.4.1 Calculation of δf/δQci for Capacitor Banks
122(2)
4.2.4.2 Calculation of δf/δVhl for a Generator
124(1)
4.2.4.3 Calculation of δf/δtti for a Transformer
124(2)
4.2.5 Realization of Mas-Based Solution
126(1)
4.2.5.1 Computation of Voltage Phase Angle Difference
127(1)
4.2.5.2 Generation Control for ORPC
128(1)
4.2.6 Simulation and Tests
129(1)
4.2.6.1 Test of the 6-Bus Ward-Hale System
129(5)
4.2.6.2 Test of IEEE 30-Bus System
134(7)
4.2.7 Conclusion
141(1)
References
141(6)
5 Distributed Demand-Side Management
147(50)
5.1 Distributed Dynamic Programming-Based Solution for Load Management in Smart Grids
148(24)
5.1.1 System Description and Problem Formulation
150(1)
5.1.2 Problem Formulation
151(2)
5.1.3 Distributed Dynamic Programming
153(1)
5.1.3.1 Abstract Framework of Dynamic Programming (DP)
153(1)
5.1.3.2 Distributed Solution for Dynamic Programming Problem
154(3)
5.1.4 Numerical Example
157(1)
5.1.5 Implementation of the LM System
158(2)
5.1.6 Simulation Studies
160(1)
5.1.6.1 Test with IEEE 14-bus System
160(6)
5.1.6.2 Large Test Systems
166(2)
5.1.6.3 Variable Renewable Generation
168(2)
5.1.6.4 With Time Delay/Packet Loss
170(1)
5.1.7 Conclusion and Discussion
171(1)
5.2 Optimal Distributed Charging Rate Control of Plug-in Electric Vehicles for Demand Management
172(18)
5.2.1 Background
175(1)
5.2.2 Problem Formulation of the Proposed Control Strategy
175(5)
5.2.3 Proposed Cooperative Control Algorithm
180(1)
5.2.3.1 MAS Framework
180(1)
5.2.3.2 Design and Analysis of Distributed Algorithm
180(1)
5.2.3.3 Algorithm Implementation
181(2)
5.2.3.4 Simulation Studies
183(7)
5.3 Conclusion
190(7)
References
191(6)
6 Distributed Social Welfare Optimization
197(28)
6.1 Introduction
197(3)
6.2 Formulation of OEM Problem
200(7)
6.2.1 Social Welfare Maximization Model
200(3)
6.2.2 Market-Based Self-interest Motivation Model
203(1)
6.2.3 Relationship Between Two Models
204(3)
6.3 Fully Distributed MAS-Based OEM Solution
207(5)
6.3.1 Distributed Price Updating Algorithm
207(2)
6.3.2 Distributed Supply-Demand Mismatch Discovery Algorithm
209(1)
6.3.3 Implementation of MAS-Based OEM Solution
210(2)
6.4 Simulation Studies
212(9)
6.4.1 Tests with a 6-bus System
212(2)
6.4.1.1 Test Under the Constant Renewable Generation
214(3)
6.4.1.2 Test Under Variable Renewable Generation
217(1)
6.4.2 Test with IEEE 30-bus System
218(3)
6.5 Conclusion
221(4)
References
221(4)
7 Distributed State Estimation
225(46)
7.1 Distributed Approach for Multi-area State Estimation Based on Consensus Algorithm
225(8)
7.1.1 Problem Formulation of Multi-area Power System State Estimation
227(1)
7.1.2 Distributed State Estimation Algorithm
228(3)
7.1.3 Approximate Static State Estimation Model
231(2)
7.1 A Regarding Implementation of Distributed State Estimation
233(9)
7.1.5 Case Studies
234(1)
7.1.5.1 With the Accurate Model
235(3)
7.1.5.2 Comparisons Between Accurate Model and Approximate Model
238(1)
7.1.5.3 With Variable Loading Conditions
239(2)
7.1.6 Conclusion and Discussion
241(1)
7.2 Multi-agent System-Based Integrated Solution for Topology Identification and State Estimation
242(24)
7.2.1 Measurement Model of the Multi-area Power System
244(1)
7.2.2 Distributed Subgradient Algorithm for MAS-Based Optimization
245(3)
7.2.3 Distributed Topology Identification
248(1)
7.2.3.1 Measurement Modeling
248(3)
7.2.3.2 Distributed Topology Identification
251(1)
7.2.3.3 Statistical Test for Topology Error Identification
252(1)
7.2.4 Distributed State Estimation
253(1)
7.2.5 Implementation of the Integrated MAS-Based Solution for TI and SE
254(1)
7.2.6 Simulation Studies
255(1)
7.2.6.1 IEEE 14-bus System
255(8)
7.2.6.2 Large Test Systems
263(3)
7.3 Conclusion and Discussion
266(5)
References
267(4)
8 Hardware-Based Algorithms Evaluation
271(20)
8.1 Steps of Algorithm Evaluation
271(2)
8.2 Controller Hardware-In-the-Loop Simulation
273(6)
8.2.1 PC-Based C-HIL Simulation
274(3)
8.2.2 DSP-Based C-HIL Simulation
277(2)
8.3 Power Hardware-In-the-Loop Simulation
279(2)
8.4 Hardware Experimentation
281(7)
8.4.1 Test-bed Development
281(3)
8.4.2 Algorithm Implementation
284(4)
8.5 Future Work
288(3)
9 Discussion and Future Work
291(6)
References
296(1)
Index 297
YINLIANG XU, PHD, is now an Associate Professor with Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, P. R. China.

WEI ZHANG, PHD, is a Postdoc Resercher Associate with Department of Civil, Environmental, and Construction Engineering of College of Engineering & Computer Science, University of Central Florida, Orlando, Florida, USA.

WENXIN LIU, PHD, is an Associate Professor with the Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.

WEN YU, PHD, is a Professor with the Departamento de Control Automatico with the Centro de Investigación y de Estudios Avanzados, Instituto Politécnico Nacional (CINVESTAV-IPN), Mexico City, Mexico.
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