Power System Optimization
Large-scale Complex Systems Approaches
Inbunden, Engelska, 2016
2 179 kr
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Fri frakt för medlemmar vid köp för minst 249 kr.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 theoryA timely and important advanced reference with the fast growth of smart gridsProfessor Chen is a pioneer of applying experimental economics to the electricity market trading mechanism, and this work brings together the latest researchA companion website is available Edit
Produktinformation
- Utgivningsdatum2016-08-16
- Mått175 x 252 x 23 mm
- Vikt767 g
- FormatInbunden
- SpråkEngelska
- Antal sidor392
- FörlagJohn Wiley & Sons Inc
- ISBN9781118724743
Tillhör följande kategorier
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
- Foreword xviiPreface xixAcknowledgments xxvList of Figures xxviiList of Tables xxxiAcronyms xxxvSymbols xxxix1 Introduction 11.1 Power System Optimal Planning 21.1.1 Generation Expansion Planning 31.1.2 Transmission Expansion Planning 51.1.3 Distribution System Planning 71.2 Power System Optimal Operation 81.2.1 Unit Commitment and Hydrothermal Scheduling 81.2.2 Economic Dispatch 121.2.3 Optimal Load Flow 141.3 Power System Reactive Power Optimization 161.4 Optimization in Electricity Markets 181.4.1 Strategic Participants’ Bids 181.4.2 Market Clearing Model 201.4.3 Market Equilibrium Problem 212 Theories and Approaches of Large-Scale Complex Systems Optimization 222.1 Basic Theories of Large-scale Complex Systems 232.1.1 Hierarchical Structures of Large-scale Complex Systems 242.1.2 Basic Principles of Coordination 272.1.3 Decomposition and Coordination of Large-scale Systems 282.2 Hierarchical Optimization Approaches 302.3 Lagrangian Relaxation Method 362.4 Cooperative Coevolutionary Approach for Large-scale Complex System Optimization 402.4.1 Framework of Cooperative Coevolution 412.4.2 Cooperative Coevolutionary Genetic Algorithms and the Numerical Experiments 432.4.3 Basic Theories of CCA 452.4.4 CCA’s Potential Applications in Power Systems 463 Optimization Approaches in Microeconomics and Game Theory 493.1 General Equilibrium Theory 513.1.1 Basic Model of a Competitive Economy 523.1.2 Walrasian Equilibrium 533.1.3 First and Second Fundamental Theorems of Welfare Economics 543.2 Noncooperative Game Theory 553.2.1 Representation of Games 553.2.2 Existence of Equilibrium 603.3 Mechanism Design 613.3.1 Principles of Mechanism Design 613.3.2 Optimization of a Single Commodity Auction 633.4 Duality Principle and Its Economic Implications 663.4.1 Economic Implication of Linear Programming Duality 663.4.2 Economic Implication of Duality in Nonlinear Programming 683.4.3 Economic Implication of Lagrangian Relaxation Method 714 Power System Planning 764.1 Generation Planning Based on Lagrangian Relaxation Method 764.1.1 Problem Formulation 784.1.2 Lagrangian Relaxation for Generation Investment Decision 804.1.3 Probabilistic Production Simulation 854.1.4 Example 874.1.5 Summary 914.2 Transmission Planning Based on Improved Genetic Algorithm 914.2.1 Mathematical Model 934.2.2 Improvements of Genetic Algorithm 954.2.3 Example 964.2.4 Summary 1014.3 Transmission Planning Based on Ordinal Optimization 1034.3.1 Introduction 1034.3.2 Transmission Expansion Planning Problem 1044.3.3 Ordinal Optimization 1074.3.4 Crude Model for Transmission Planning Problem 1114.3.5 Example 1124.3.6 Summary 1204.4 Integrated Planning of Distribution Systems Based on Hybrid Intelligent Algorithm 1214.4.1 Mathematical Model of Integrated Planning Based on DG and DSR 1224.4.2 Hybrid Intelligent Algorithm 1244.4.3 Example 1254.4.4 Summary 1295 Power System Operation 1315.1 Unit Commitment Based on Cooperative Coevolutionary Algorithm 1315.1.1 Problem Formulation 1325.1.2 Cooperative Coevolutionary Algorithm 1335.1.3 Form Primal Feasible Solution Based on the Dual Results 1385.1.4 Dynamic Economic Dispatch 1405.1.5 Example 1465.1.6 Summary 1485.2 Security-Constrained Unit Commitment with Wind Power Integration Based on Mixed Integer Programming 1495.2.1 Suitable SCUC Model for MIP 1515.2.2 Selection of St and the Significance of Extreme Scenarios 1545.2.3 Example 1565.2.4 Summary 1605.3 Optimal Power Flow with Discrete Variables Based on Hybrid Intelligent Algorithm 1605.3.1 Formulation of OPF Problem 1625.3.2 Modern Interior Point Algorithm (MIP) 1635.3.3 Genetic Algorithm with Annealing Selection (AGA) 1675.3.4 Flow of Presented Algorithm 1695.3.5 Example 1695.3.6 Summary 1725.4 Optimal Power Flow with Discrete Variables Based on Interior Point Cutting Plane Method 1735.4.1 IPCPM and Its Analysis 1755.4.2 Improvement of IPCPM 1805.4.3 Example 1855.4.4 Summary 1876 Power System Reactive Power Optimization 1896.1 Space Decoupling for Reactive Power Optimization 1896.1.1 Multi-agent System-based Volt/VAR Control 1906.1.2 Coordination Optimization Method 1936.2 Time Decoupling for Reactive Power Optimization 1986.2.1 Cost Model of Adjusting the Control Devices of Volt/VAR Control 2026.2.2 Time-Decoupling Model for Reactive Power Optimization Based upon Cost of Adjusting the Control Devices 2076.3 Game Theory Model of Multi-agent Volt/VAR Control 2156.3.1 Game Mechanism of Volt/VAR Control During Multi-level Power Dispatch 2176.3.2 Payoff Function Modeling of Multi-agent Volt/VAR Control 2246.4 Volt/VAR Control in Distribution Systems Using an Approach Based on Time Interval 2316.4.1 Problem Formulation 2336.4.2 Load Level Division 2346.4.3 Optimal Dispatch of OLTC and Capacitors Using Genetic Algorithm 2366.4.4 Example 2386.4.5 Summary 2447 Modeling and Analysis of Electricity Markets 2477.1 Oligopolistic Electricity Market Analysis Based on Coevolutionary Computation 2477.1.1 Market Model Formulation 2497.1.2 Electricity Market Analysis Based on Coevolutionary Computation 2527.1.3 Example 2587.1.4 Summary 2657.2 Supply Function Equilibrium Analysis Based on Coevolutionary Computation 2657.2.1 Market Model Formulation 2677.2.2 Coevolutionary Approach to Analyzing SFE Model 2717.2.3 Example 2737.2.4 Summary 2837.3 Searching for Electricity Market Equilibrium with Complex Constraints Using Coevolutionary Approach 2847.3.1 Market Model Formulation 2867.3.2 Coevolutionary Computation 2907.3.3 Example 2927.3.4 Summary 3017.4 Analyzing Two-Settlement Electricity Market Equilibrium by Coevolutionary Computation Approach 3017.4.1 Market Model Formulation 3037.4.2 Coevolutionary Approach to Analyzing Market Model 3077.4.3 Example 3097.4.4 Summary 3188 Future Developments 3198.1 New Factors in Power System Optimization 3208.1.1 Planning and Investment Decision Under New Paradigm 3208.1.2 Scheduling/Dispatch of Renewable Energy Sources 3218.1.3 Energy Storage Problems 3228.1.4 Environmental Impact 3238.1.5 Novel Electricity Market 3238.2 Challenges and Possible Solutions in Power System Optimization 324Appendix 328A.1 Header File 328A.2 Species Class 329A.3 Ecosystem Class 335A.4 Main Function 336References 338Index 353
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