Details

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

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

<p><b>POWER SYSTEM OPTIMIZATION</b></br> LARGE-SCALE COMPLEX SYSTEMS APPROACHES <p>A consolidation of recent advances and research, this book addresses the issues of power system optimization based on large-scale complex systems approaches. This book gathers approaches from different disciplines such as systems engineering, operations research, and microeconomics. The vast topics of power system optimization are presented in a unified manner, which include: power system planning, operation, reactive power optimization, and electricity markets. <ul> <li>Presents a new and systematic viewpoint of large-scale complex systems approaches for power system optimization</li> <li>Provides timely and important insights that can be used for smart grids</li> <li>Covers a range of topics and applications from different disciplines like systems engineering, systems operations and optimization, and microeconomics</li> </ul> <p>Written by a pioneer of large-scale complex systems approaches, <i>Power System Optimization: Large-scale Complex Systems Approaches</i> is a timely reference for power system planners and operators, as well as advanced students of power engineering.

SG