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E-raamat: Electricity Pricing: Regulated, Deregulated and Smart Grid Systems

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(IIEST, Shibpur, India), (University of Calcutta, Kolkata, India), (Academy of Technology, Hooghly, India)
  • Formaat: 244 pages
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351831031
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Electricity Pricing: Regulated, Deregulated and Smart Grid SystemsSuurem pilt
  • Formaat: 244 pages
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351831031
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Electricity is the prime mover of a modern society. Per capita consumption of electricity is an indicator of a countrys growth. This indicator depends on availability, quality and reliability of power and most particularly its overall cost. The cost ofelectricity governs its usage. Thus the electricity pricing is a major issue to consumers for its liberal use. But due to demand of social and technological advancements the changes in power networks and their mode of operation are inevitable. Revolutionize of electric power industries engross technical and also various non-technical and economic issues. Even though as an inevitable consequence, power networks have been undergoing changes from regulated to de-regulated structures, finally stepping into smart grid concept which in turns, involves the change in electricity price methodology-- This book provides proven methods for supplying uninterrupted, quality power at a reasonable price to the consumer. Taking into consideration operating constraints as well as generation cost, line overload, and congestion for expected and inadvertent loading stress, the text presents solutions based on stochastic optimization techniques--such as genetic algorithm, particle swarm optimization, and differential evolution--for improving the stability, reliability, and efficiency of LPS and deregulated multi-bus networks. It also proposes the use of stochastic techniques in optimizing utility management in the smart grid-- Electricity Pricing: Regulated, Deregulated and Smart Grid Systems presents proven methods for supplying uninterrupted, high-quality electrical power at a reasonable price to the consumer. Illustrating the evolution of the power market from a monopoly to an open access system, this essential text:Covers voltage stability analysis of longitudinal power supply systems using an artificial neural network (ANN)Explains how to improve performance using flexible alternating current transmission systems (FACTS) and high-voltage direct current (HVDC)Takes into account operating constraints as well as generation cost, line overload, and congestion for expected and inadvertent loading stressGoes beyond FACTS and HVDC to provide multi-objective optimization algorithms for the deregulated power marketProposes the use of stochastic optimization techniques in the smart grid, preparing the reader for future developmentElectricity Pricing: Regulated, Deregulated and Smart Grid Systems offers practical solutions for improving stability, reliability, and efficiency in real-time systems while optimizing electricity cost.
List of Figures
xi
List of Tables
xv
Preface xix
About the Authors xxi
List of Principal Symbols
xxiii
List of Abbreviations
xxv
1 Prologue
1(6)
1.1 Motivation of the Book
1(1)
1.2 Contributions of the Book
2(2)
1.3 Organization of the Book
4(3)
2 Background and Literature Survey
7(22)
2.1 Introduction
7(1)
2.2 Power Network Performance Evaluation
8(18)
2.2.1 Importance of Voltage Stability on Performance Evaluation
8(1)
2.2.1.1 Classical Methods of Ascertaining Stability
8(3)
2.2.1.2 Neo-Classical Methods of Ascertaining Stability
11(1)
2.2.2 Significance of Compensation Techniques
12(1)
2.2.2.1 Series and Shunt Compensation Employing FACTS Devices
13(2)
2.2.2.2 Employment of HVDC Link
15(3)
2.2.3 Optimization Methods with System Performance and Cost Emphasis
18(1)
2.2.3.1 Classical and Neo-Classical Optimization Methods
18(1)
2.2.3.2 Application of Optimization Methods in Regulated and Deregulated Power Networks
19(5)
2.2.4 Enrichment of Cost-Governed System Performance in Smart Grid Arena
24(2)
2.3 Concluding Remarks on Existing Efforts
26(3)
Annotating Outline
26(3)
3 Analysis of Voltage Stability of Longitudinal Power Supply System Using an Artificial Neural Network
29(38)
3.1 Introduction
29(1)
3.2 Theoretical Development of Voltage Stability and Voltage Collapse
30(16)
3.2.1 Theoretical Background of Voltage Instability and Its Causes
31(2)
3.2.2 Few Relevant Analytical Methods and Indices for Voltage Stability Assessment
33(2)
3.2.2.1 The PV and VQ Curves for the Small System
35(1)
3.2.2.2 Singular Values
36(2)
3.2.2.3 Eigenvalue Decomposition
38(1)
3.2.2.4 Modal Analysis
39(1)
3.2.2.5 Voltage Stability Index L
39(1)
3.2.2.6 Fast Voltage Stability Index (FVSI) and Line Quality Factor (LQF)
40(2)
3.2.2.7 Global Voltage Stability Indicator
42(1)
3.2.2.8 Voltage Collapse Proximity Indicator (VCPI)
43(1)
3.2.2.9 Proximity Indices of Voltage Collapse
43(1)
3.2.2.10 Identification of Weak Bus of Power Network
44(1)
3.2.2.11 Diagonal Element Ratio
44(1)
3.2.2.12 Line Voltage Stability Index
45(1)
3.2.2.13 Local Load Margin
45(1)
3.2.2.14 Voltage Ratio Index
46(1)
3.3 Theory of ANN
46(10)
3.3.1 Attributes of ANNs
47(1)
3.3.1.1 Building Block of ANNs
48(1)
3.3.1.2 Building Layers of ANNs
49(2)
3.3.1.3 Structures of Neural Networks
51(4)
3.3.1.4 Training Algorithms of Neural Networks
55(1)
3.4 Analysis of Voltage Stability of Multi-Bus Power Network
56(8)
3.4.1 Classical Analysis of Voltage Stability
56(2)
3.4.2 Application of ANN on Voltage Stability Analysis
58(6)
3.5 Summary
64(3)
Annotating Outline
64(3)
4 Improvement of System Performances Using FACTS and HVDC
67(34)
4.1 Introduction
67(1)
4.2 Development of FACTS Controllers
68(11)
4.2.1 Modeling of Shunt Compensating Device
71(1)
4.2.1.1 Conventional Model of SVC
72(1)
4.2.1.2 Shunt Variable Susceptance Model of SVC
73(1)
4.2.1.3 Firing Angle Model of SVC
74(1)
4.2.2 Modeling of Series Compensating Device
75(1)
4.2.2.1 Variable Series Impedance Power Flow Model of TCSC
75(2)
4.2.2.2 Firing Angle Power Flow Model of TCSC
77(2)
4.3 Prologue of High-Voltage Direct Current (HVDC) System
79(4)
4.3.1 Modeling of DC Link
82(1)
4.4 Improvement of System Performance Using FACTS and HVDC
83(15)
4.4.1 Improvement of Voltage Profile of Weak Bus Using SVC
84(5)
4.4.2 Application of ANN for the Improvement Voltage Profile Using SVC
89(3)
4.4.3 Application of TCSC and HVDC for Upgrading of Cost-Constrained System Performance
92(1)
4.4.3.1 Determination of the Weakest Link in the System under Stressed and Contingent Conditions
93(1)
4.4.3.2 Performance of TCSC and the HVDC Interconnection Link Separately in Stressed Conditions
94(2)
4.4.3.3 Performance of TCSC and the HVDC Interconnection Link during Line Contingency
96(1)
4.4.3.4 Cost Comparison of TCSC and the HVDC Link
97(1)
4.5 Summary
98(3)
Annotating Outline
98(3)
5 Multi-Objective Optimization Algorithms for Deregulated Power Market
101(50)
5.1 Introduction
101(1)
5.2 Deregulated Power Market Structure
102(3)
5.3 Soft Computing Methodologies for Power Network Optimizations
105(11)
5.3.1 Overview of Genetic Algorithm
106(4)
5.3.2 Overview of Particle Swarm Optimization
110(3)
5.3.3 Overview of Differential Evolution
113(3)
5.4 Algorithms for Utility Optimization with Cost and Operational Constraints
116(6)
5.4.1 Genetic Algorithm-Based Cost-Constrained Transmission Line Loss Optimization
116(4)
5.4.2 GA-Based Generation Cost-Constrained Redispatching Schedules of GENCOs
120(2)
5.5 Congestion Management Methodologies
122(27)
5.5.1 Generator Contribution-Based Congestion Management Using Multi-Objective GA
124(2)
5.5.2 DE- and PSO-Based Cost-Governed Multi-Objective Solutions in Contingent State
126(9)
5.5.3 Mitigation of Line Congestion and Cost Optimization Using Multi-Objective PSO
135(7)
5.5.4 Swarm Intelligence-Based Cost Optimization for Contingency Surveillance
142(1)
5.5.4.1 Development of Value of Lost Load (VOLL)
142(1)
5.5.4.2 Development of Value of Congestion Cost (VOCC)
143(1)
5.5.4.3 Development of Value of Excess Loss (VOEL)
143(6)
5.6 Summary
149(2)
Annotating Outline
150(1)
6 Application of Stochastic Optimization Techniques in the Smart Grid
151(24)
6.1 Introduction
151(1)
6.2 Smart Grid and Its Objectives
152(10)
6.2.1 Concept of the Smart Grid
152(1)
6.2.2 Elementary Objectives of the Smart Grid and Demand Response
153(2)
6.2.3 Demand Response-Based Architecture of the Smart Grid
155(2)
6.2.4 Effect of DR on the Smart Grid Scenario
157(1)
6.2.5 Cost Component of the Smart Grid
158(1)
6.2.5.1 Cost Components for the Smart Grid: Transmission Systems and Sub-Stations End
159(1)
6.2.5.2 Cost Components for the Smart Grid: Distribution End
159(1)
6.2.5.3 Cost Components of the Smart Grid: Consumer End
160(1)
6.2.6 Smart Grid: Cost-Benefit Analysis
160(2)
6.3 Swarm Intelligence-Based Utility and Cost Optimization
162(10)
6.3.1 Cost Objective and Operating Constraints of the Work
162(1)
6.3.2 Theory of Cost-Regulated Curtailment Index (CI)
163(2)
6.3.3 Cost Realization Methodology Implementation with Swarm Intelligence
165(2)
6.3.4 Implementation of the Cost-Effective Methodology with DR Connectivity
167(5)
6.4 Summary
172(3)
Annotating Outline
173(2)
7 Epilogue
175(6)
7.1 Summary and Conclusions
175(4)
7.2 Future Scope
179(2)
References 181(18)
Appendix A Description of Test Systems 199(4)
Appendix B Development of System Performance Indices 203(2)
Index 205
Sawan Sen holds a B.Sc, B.Tech, M.Tech, and Ph.D from the University of Calcutta, Kolkata, India. She is currently an associate professor in the Electrical Engineering Department of the Academy of Technology, Hooghly, India. Her main research interests include power system stability analysis, system performance enhancement, and different soft computing techniques for solving power system problems like congestion management, cost optimization, and electricity pricing under regulated, deregulated, and smart grid environments.

Samarjit Sengupta holds a B.Sc, B.Tech, M.Tech, and Ph.D from the University of Calcutta, Kolkata, India. He is currently a professor of electrical engineering in the Department of Applied Physics at the University of Calcutta. He has published 130 journal papers and eight books on various topics of electrical engineering. His main research interests include power quality instrumentation, power system stability, and security and power system protection. He is a fellow of IET and IETE, as well as a senior member of IEEE.

Abhijit Chakrabarti holds as Ph.D from the University of Calcutta, Kolkata, India. He is currently the vice chancellor of Jadavpur University, Kolkata, India and a professor at the Indian Institute of Engineering Science and Technology, Shibpur. Previously he served on the West Bengal State Council of Higher Education as vice chairman and chairman. A life fellow of IE, he has authored 123 research papers and 12 books, and received the Pandit Madan Mohan Malviya Power Medal. He is a member of the AICTE including the NBA, as well as a member of different expert and policymaking committees for various universities.
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