Haoyong Chen, Honwing Ngan, Yongjun Zhang

Power System Optimization

Large-Scale Complex Systems Approaches

• Gebundenes Buch

Produktbeschreibung

An original look from a microeconomic perspective for power system optimization and its application to electricity markets * Presents a new and systematic viewpoint for power system optimization inspired by microeconomics and game theory * A timely and important advanced reference with the fast growth of smart grids * Professor Chen is a pioneer of applying experimental economics to the electricity market trading mechanism, and this work brings together the latest research * A companion website is available Edit

Produktdetails

• Verlag: John Wiley & Sons / Wiley
• Seitenzahl: 392
• Erscheinungstermin: 29. August 2016
• Englisch
• Abmessung: 244mm x 165mm x 23mm
• Gewicht: 748g
• ISBN-13: 9781118724743
• ISBN-10: 1118724747
• Artikelnr.: 45075692

Autorenporträt

Haoyong Chen, South China University of Technology, P. R. China Honwing Ngan, Asia-Pacific Research Institute of Smart Grid and Renewable Energy, Hong Kong Yongjun Zhang, South China University of Technology, P. R. China

Inhaltsangabe

Foreword xvii Preface xix Acknowledgments xxv List of Figures xxvii List of
Tables xxxi Acronyms xxxv Symbols xxxix 1 Introduction 1 1.1 Power System
Optimal Planning 2 1.1.1 Generation Expansion Planning 3 1.1.2 Transmission
Expansion Planning 5 1.1.3 Distribution System Planning 7 1.2 Power System
Optimal Operation 8 1.2.1 Unit Commitment and Hydrothermal Scheduling 8
1.2.2 Economic Dispatch 12 1.2.3 Optimal Load Flow 14 1.3 Power System
Reactive Power Optimization 16 1.4 Optimization in Electricity Markets 18
1.4.1 Strategic Participants' Bids 18 1.4.2 Market Clearing Model 20 1.4.3
Market Equilibrium Problem 21 2 Theories and Approaches of Large-Scale
Complex Systems Optimization 22 2.1 Basic Theories of Large-scale Complex
Systems 23 2.1.1 Hierarchical Structures of Large-scale Complex Systems 24
2.1.2 Basic Principles of Coordination 27 2.1.3 Decomposition and
Coordination of Large-scale Systems 28 2.2 Hierarchical Optimization
Approaches 30 2.3 Lagrangian Relaxation Method 36 2.4 Cooperative
Coevolutionary Approach for Large-scale Complex System Optimization 40
2.4.1 Framework of Cooperative Coevolution 41 2.4.2 Cooperative
Coevolutionary Genetic Algorithms and the Numerical Experiments 43 2.4.3
Basic Theories of CCA 45 2.4.4 CCA's Potential Applications in Power
Systems 46 3 Optimization Approaches in Microeconomics and Game Theory 49
3.1 General Equilibrium Theory 51 3.1.1 Basic Model of a Competitive
Economy 52 3.1.2 Walrasian Equilibrium 53 3.1.3 First and Second
Fundamental Theorems of Welfare Economics 54 3.2 Noncooperative Game Theory
55 3.2.1 Representation of Games 55 3.2.2 Existence of Equilibrium 60 3.3
Mechanism Design 61 3.3.1 Principles of Mechanism Design 61 3.3.2
Optimization of a Single Commodity Auction 63 3.4 Duality Principle and Its
Economic Implications 66 3.4.1 Economic Implication of Linear Programming
Duality 66 3.4.2 Economic Implication of Duality in Nonlinear Programming
68 3.4.3 Economic Implication of Lagrangian Relaxation Method 71 4 Power
System Planning 76 4.1 Generation Planning Based on Lagrangian Relaxation
Method 76 4.1.1 Problem Formulation 78 4.1.2 Lagrangian Relaxation for
Generation Investment Decision 80 4.1.3 Probabilistic Production Simulation
85 4.1.4 Example 87 4.1.5 Summary 91 4.2 Transmission Planning Based on
Improved Genetic Algorithm 91 4.2.1 Mathematical Model 93 4.2.2
Improvements of Genetic Algorithm 95 4.2.3 Example 96 4.2.4 Summary 101 4.3
Transmission Planning Based on Ordinal Optimization 103 4.3.1 Introduction
103 4.3.2 Transmission Expansion Planning Problem 104 4.3.3 Ordinal
Optimization 107 4.3.4 Crude Model for Transmission Planning Problem 111
4.3.5 Example 112 4.3.6 Summary 120 4.4 Integrated Planning of Distribution
Systems Based on Hybrid Intelligent Algorithm 121 4.4.1 Mathematical Model
of Integrated Planning Based on DG and DSR 122 4.4.2 Hybrid Intelligent
Algorithm 124 4.4.3 Example 125 4.4.4 Summary 129 5 Power System Operation
131 5.1 Unit Commitment Based on Cooperative Coevolutionary Algorithm 131
5.1.1 Problem Formulation 132 5.1.2 Cooperative Coevolutionary Algorithm
133 5.1.3 Form Primal Feasible Solution Based on the Dual Results 138 5.1.4
Dynamic Economic Dispatch 140 5.1.5 Example 146 5.1.6 Summary 148 5.2
Security-Constrained Unit Commitment with Wind Power Integration Based on
Mixed Integer Programming 149 5.2.1 Suitable SCUC Model for MIP 151 5.2.2
Selection of St and the Significance of Extreme Scenarios 154 5.2.3 Example
156 5.2.4 Summary 160 5.3 Optimal Power Flow with Discrete Variables Based
on Hybrid Intelligent Algorithm 160 5.3.1 Formulation of OPF Problem 162
5.3.2 Modern Interior Point Algorithm (MIP) 163 5.3.3 Genetic Algorithm
with Annealing Selection (AGA) 167 5.3.4 Flow of Presented Algorithm 169
5.3.5 Example 169 5.3.6 Summary 172 5.4 Optimal Power Flow with Discrete
Variables Based on Interior Point Cutting Plane Method 173 5.4.1 IPCPM and
Its Analysis 175 5.4.2 Improvement of IPCPM 180 5.4.3 Example 185 5.4.4
Summary 187 6 Power System Reactive Power Optimization 189 6.1 Space
Decoupling for Reactive Power Optimization 189 6.1.1 Multi-agent
System-based Volt/VAR Control 190 6.1.2 Coordination Optimization Method
193 6.2 Time Decoupling for Reactive Power Optimization 198 6.2.1 Cost
Model of Adjusting the Control Devices of Volt/VAR Control 202 6.2.2
Time-Decoupling Model for Reactive Power Optimization Based upon Cost of
Adjusting the Control Devices 207 6.3 Game Theory Model of Multi-agent
Volt/VAR Control 215 6.3.1 Game Mechanism of Volt/VAR Control During
Multi-level Power Dispatch 217 6.3.2 Payoff Function Modeling of
Multi-agent Volt/VAR Control 224 6.4 Volt/VAR Control in Distribution
Systems Using an Approach Based on Time Interval 231 6.4.1 Problem
Formulation 233 6.4.2 Load Level Division 234 6.4.3 Optimal Dispatch of
OLTC and Capacitors Using Genetic Algorithm 236 6.4.4 Example 238 6.4.5
Summary 244 7 Modeling and Analysis of Electricity Markets 247 7.1
Oligopolistic Electricity Market Analysis Based on Coevolutionary
Computation 247 7.1.1 Market Model Formulation 249 7.1.2 Electricity Market
Analysis Based on Coevolutionary Computation 252 7.1.3 Example 258 7.1.4
Summary 265 7.2 Supply Function Equilibrium Analysis Based on
Coevolutionary Computation 265 7.2.1 Market Model Formulation 267 7.2.2
Coevolutionary Approach to Analyzing SFE Model 271 7.2.3 Example 273 7.2.4
Summary 283 7.3 Searching for Electricity Market Equilibrium with Complex
Constraints Using Coevolutionary Approach 284 7.3.1 Market Model
Formulation 286 7.3.2 Coevolutionary Computation 290 7.3.3 Example 292
7.3.4 Summary 301 7.4 Analyzing Two-Settlement Electricity Market
Equilibrium by Coevolutionary Computation Approach 301 7.4.1 Market Model
Formulation 303 7.4.2 Coevolutionary Approach to Analyzing Market Model 307
7.4.3 Example 309 7.4.4 Summary 318 8 Future Developments 319 8.1 New
Factors in Power System Optimization 320 8.1.1 Planning and Investment
Decision Under New Paradigm 320 8.1.2 Scheduling/Dispatch of Renewable
Energy Sources 321 8.1.3 Energy Storage Problems 322 8.1.4 Environmental
Impact 323 8.1.5 Novel Electricity Market 323 8.2 Challenges and Possible
Solutions in Power System Optimization 324 Appendix 328 A.1 Header File 328
A.2 Species Class 329 A.3 Ecosystem Class 335 A.4 Main Function 336
References 338 Index 353