(EBook PDF) Power Generation Operation and Control 3rd Edition by Allen Wood, Bruce Wollenberg, Gerald Sheblé 1119277507 9781119277507 full chapters

Power Generation, Operation, and Control 3rd Edition by Allen J. Wood, Bruce F. Wollenberg, Gerald B. Sheblé – Ebook PDF Instant Download/DeliveryISBN:  1119277507, 9781119277507 

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ISBN-10 ‏ : ‎1119277507 

ISBN-13 ‏ : ‎ 9781119277507

Author :  Allen Wood, Bruce Wollenberg, Gerald Sheblé 

A thoroughly revised new edition of the definitive work on power systems best practices

In this eagerly awaited new edition, Power Generation, Operation, and Control continues to provide engineers and academics with a complete picture of the techniques used in modern power system operation. Long recognized as the standard reference in the field, the book has been thoroughly updated to reflect the enormous changes that have taken place in the electric power industry since the Second Edition was published seventeen years ago.

Power Generation, Operation, and Control 3rd Table of contents:

1 Introduction
1.1 Purpose of the Course
1.2 Course Scope
1.3 Economic Importance
1.4 Deregulation: Vertical to Horizontal
1.5 Problems: New and Old
1.6 Characteristics of Steam Units
1.6.1 Variations in Steam Unit Characteristics
1.6.2 Combined Cycle Units
1.6.3 Cogeneration Plants
1.6.4 Light-Water Moderated Nuclear Reactor Units
1.6.5 Hydroelectric Units
1.6.6 Energy Storage
1.7 Renewable Energy
1.7.1 Wind Power
1.7.2 Cut-In Speed
1.7.3 Rated Output Power and Rated Output Wind Speed
1.7.4 Cut-Out Speed
1.7.5 Wind Turbine Efficiency or Power Coefficient
1.7.6 Solar Power
APPENDIX 1A Typical Generation Data
APPENDIX 1B Fossil Fuel Prices
APPENDIX 1C Unit Statistics
References for Generation Systems
Further Reading
2 Industrial Organization, Managerial Economics, and Finance
2.1 Introduction
2.2 Business Environments
2.2.1 Regulated Environment
2.2.2 Competitive Market Environment
2.3 Theory of the Firm
2.4 Competitive Market Solutions
2.5 Supplier Solutions
2.5.1 Supplier Costs
2.5.2 Individual Supplier Curves
2.5.3 Competitive Environments
2.5.4 Imperfect Competition
2.5.5 Other Factors
2.6 Cost of Electric Energy Production
2.7 Evolving Markets
2.7.1 Energy Flow Diagram
2.8 Multiple Company Environments
2.8.1 Leontief Model: Input–Output Economics
2.8.2 Scarce Fuel Resources
2.9 Uncertainty and Reliability
PROBLEMS
Reference
3 Economic Dispatch of Thermal Units and Methods of Solution
3.1 The Economic Dispatch Problem
3.2 Economic Dispatch with Piecewise Linear Cost Functions
3.3 LP Method
3.3.1 Piecewise Linear Cost Functions
3.3.2 Economic Dispatch with LP
3.4 The Lambda Iteration Method
3.5 Economic Dispatch Via Binary Search
3.6 Economic Dispatch Using Dynamic Programming
3.7 Composite Generation Production Cost Function
3.8 Base Point and Participation Factors
3.9 Thermal System Dispatching with Network Losses Considered
3.10 The Concept of Locational Marginal Price (LMP)
3.11 Auction Mechanisms
3.11.1 PJM Incremental Price Auction as a Graphical Solution
3.11.2 Auction Theory Introduction
3.11.3 Auction Mechanisms
3.11.4 English (First-Price Open-Cry = Ascending)
3.11.5 Dutch (Descending)
3.11.6 First-Price Sealed Bid
3.11.7 Vickrey (Second-Price Sealed Bid)
3.11.8 All Pay (e.g., Lobbying Activity)
APPENDIX 3A Optimization Within Constraints
APPENDIX 3B Linear Programming (LP)
APPENDIX 3C Non-Linear Programming
APPENDIX 3D Dynamic Programming (DP)
APPENDIX 3E Convex Optimization
PROBLEMS
References
4 Unit Commitment
4.1 Introduction
4.1.1 Economic Dispatch versus Unit Commitment
4.1.2 Constraints in Unit Commitment
4.1.3 Spinning Reserve
4.1.4 Thermal Unit Constraints
4.1.5 Other Constraints
4.2 Unit Commitment Solution Methods
4.2.1 Priority-List Methods
4.2.2 Lagrange Relaxation Solution
4.2.3 Mixed Integer Linear Programming
4.3 Security-Constrained Unit Commitment (SCUC)
4.4 Daily Auctions Using a Unit Commitment
APPENDIX 4A Dual Optimization on a Nonconvex Problem
APPENDIX 4B Dynamic-Programming Solution to Unit Commitment
PROBLEMS
5 Generation with Limited Energy Supply
5.1 Introduction
5.2 Fuel Scheduling
5.3 Take-or-Pay Fuel Supply Contract
5.4 Complex Take-or-Pay Fuel Supply Models
5.4.1 Hard Limits and Slack Variables
5.5 Fuel Scheduling by Linear Programming
5.6 Introduction to Hydrothermal Coordination
5.6.1 Long-Range Hydro-Scheduling
5.6.2 Short-Range Hydro-Scheduling
5.7 Hydroelectric Plant Models
5.8 Scheduling Problems
5.8.1 Types of Scheduling Problems
5.8.2 Scheduling Energy
5.9 The Hydrothermal Scheduling Problem
5.9.1 Hydro-Scheduling with Storage Limitations
5.9.2 Hydro-Units in Series (Hydraulically Coupled)
5.9.3 Pumped-Storage Hydroplants
5.10 Hydro-Scheduling using Linear Programming
APPENDIX 5A Dynamic-Programming Solution to Hydrothermal Scheduling
PROBLEMS
6 Transmission System Effects
6.1 Introduction
6.2 Conversion of Equipment Data to Bus and Branch Data
6.3 Substation Bus Processing
6.4 Equipment Modeling
6.5 Dispatcher Power Flow for Operational Planning
6.6 Conservation of Energy (Tellegen’s Theorem)
6.7 Existing Power Flow Techniques
6.8 The Newton–Raphson Method Using the Augmented Jacobian Matrix
6.8.1 Power Flow Statement
6.9 Mathematical Overview
6.10 AC System Control Modeling
6.11 Local Voltage Control
6.12 Modeling of Transmission Lines and Transformers
6.12.1 Transmission Line Flow Equations
6.12.2 Transformer Flow Equations
6.13 HVDC links
6.13.1 Modeling of HVDC Converters and FACT Devices
6.13.2 Definition of Angular Relationships in HVDC Converters
6.13.3 Power Equations for a Six-Pole HVDC Converter
6.14 Brief Review of Jacobian Matrix Processing
6.15 Example 6A: AC Power Flow Case
6.16 The Decoupled Power Flow
6.17 The Gauss–Seidel Method
6.18 The “DC” or Linear Power Flow
6.18.1 DC Power Flow Calculation
6.18.2 EXAMPLE 6B: DC Power Flow Example on the Six-Bus Sample System
6.19 Unified Eliminated Variable Hvdc Method
6.19.1 Changes to Jacobian Matrix Reduced
6.19.2 Control Modes
6.19.3 Analytical Elimination
6.19.4 Control Mode Switching
6.19.5 Bipolar and 12-Pulse Converters
6.20 Transmission Losses
6.20.1 A Two-Generator System Example
6.20.2 Coordination Equations, Incremental Losses, and Penalty Factors
6.21 Discussion of Reference Bus Penalty Factors
6.22 Bus Penalty Factors Direct from the AC Power Flow
PROBLEMS
7 Power System Security
7.1 Introduction
7.2 Factors Affecting Power System Security
7.3 Contingency Analysis: Detection of Network Problems
7.3.1 Generation Outages
7.3.2 Transmission Outages
7.4 An Overview of Security Analysis
7.4.1 Linear Sensitivity Factors
7.5 Monitoring Power Transactions Using “Flowgates”
7.6 Voltage Collapse
7.6.1 AC Power Flow Methods
7.6.2 Contingency Selection
7.6.3 Concentric Relaxation
7.6.4 Bounding
7.6.5 Adaptive Localization
APPENDIX 7A AC Power Flow Sample Cases
APPENDIX 7B Calculation of Network Sensitivity Factors
References
8 Optimal Power Flow
8.1 Introduction
8.2 The Economic Dispatch Formulation
8.3 The Optimal Power Flow Calculation Combining Economic Dispatch and the Power Flow
8.4 Optimal Power Flow Using the DC Power Flow
8.5 Example 8A: Solution of the DC Power Flow OPF
8.6 Example 8B: DCOPF with Transmission Line Limit Imposed
8.7 Formal Solution of the DCOPF
8.8 Adding Line Flow Constraints to the Linear Programming Solution
8.8.1 Solving the DCOPF Using Quadratic Programming
8.9 Solution of the ACOPF
8.10 Algorithms for Solution of the ACOPF
8.11 Relationship Between LMP, Incremental Losses, and Line Flow Constraints
8.11.1 Locational Marginal Price at a Bus with No Lines Being Held at Limit
8.11.2 Locational Marginal Price with a Line Held at Its Limit
8.12 Security-Constrained OPF
8.12.1 Security Constrained OPF Using the DC Power Flow and Quadratic Programming
8.12.2 DC Power Flow
8.12.3 Line Flow Limits
8.12.4 Contingency Limits
APPENDIX 8A Interior Point Method
APPENDIX 8B Data for the 12-Bus System
APPENDIX 8C Line Flow Sensitivity Factors
APPENDIX 8D Linear Sensitivity Analysis of the AC Power Flow
PROBLEMS
9 Introduction to State Estimation in Power Systems
9.1 Introdiction
9.2 Power System State Estimation
9.3 Maximum Likelihood Weighted Least-Squares Estimation
9.3.1 Introduction
9.3.2 Maximum Likelihood Concepts
9.3.3 Matrix Formulation
9.3.4 An Example of Weighted Least-Squares State Estimation
9.4 State Estimation of an AC Network
9.4.1 Development of Method
9.4.2 Typical Results of State Estimation on an AC Network
9.5 State Estimation by Orthogonal Decomposition
9.5.1 The Orthogonal Decomposition Algorithm
9.6 An Introduction to Advanced Topics in State Estimation
9.6.1 Sources of Error in State Estimation
9.6.2 Detection and Identification of Bad Measurements
9.6.3 Estimation of Quantities Not Being Measured
9.6.4 Network Observability and Pseudo-measurements
9.7 The Use of Phasor Measurement Units (PMUs)
9.8 Application of Power Systems State Estimation
9.9 Importance of Data Verification and Validation
9.10 Power System Control Centers
APPENDIX 9A Derivation of Least-Squares Equations
9A.1 The Overdetermined Case (Nm > Ns)
9A.2 The Fuly Determined Case (Nm = Ns)
9A.3 The Underdetermined Case (Nm < Ns)
PROBLEMS
10 Control of Generation
10.1 Introduction
10.2 Generator Model
10.3 Load Model
10.4 Prime-Mover Model
10.5 Governor Model
10.6 Tie-Line Model
10.7 Generation Control
10.7.1 Supplementary Control Action
10.7.2 Tie-Line Control
10.7.3 Generation Allocation
10.7.4 Automatic Generation Control (AGC) Implementation
10.7.5 AGC Features
10.7.6 NERC Generation Control Criteria
PROBLEMS
References
11 Interchange, Pooling, Brokers, and Auctions
11.1 Introduction
11.2 Interchange Contracts
11.2.1 Energy
11.2.2 Dynamic Energy
11.2.3 Contingent
11.2.4 Market Based
11.2.5 Transmission Use
11.2.6 Reliability
11.3 Energy Interchange between Utilities
11.4 Interutility Economy Energy Evaluation
11.5 Interchange Evaluation with Unit Commitment
11.6 Multiple Utility Interchange Transactions—Wheeling
11.7 Power Pools
11.8 The Energy-Broker System
11.9 Transmission Capability General Issues
11.10 Available Transfer Capability and Flowgates
11.10.1 Definitions
11.10.2 Process
11.10.3 Calculation ATC Methodology
11.11 Security Constrained Unit Commitment (SCUC)
11.11.1 Loads and Generation in a Spot Market Auction
11.11.2 Shape of the Two Functions
11.11.3 Meaning of the Lagrange Multipliers
11.11.4 The Day-Ahead Market Dispatch
11.12 Auction Emulation using Network LP
11.13 Sealed Bid Discrete Auctions
PROBLEMS
12 Short-Term Demand Forecasting
12.1 Perspective
12.2 Analytic Methods
12.3 Demand Models
12.4 Commodity Price Forecasting
12.5 Forecasting Errors
12.6 System Identification
12.7 Econometric Models
12.7.1 Linear Environmental Model
12.7.2 Weather-Sensitive Models
12.8 Time Series
12.8.1 Time Series Models Seasonal Component
12.8.2 Auto-Regressive (AR)
12.8.3 Moving Average (MA)
12.8.4 Auto-Regressive Moving Average (ARMA): Box-Jenkins
12.8.5 Auto-Regressive Integrated Moving-Average (ARIMA): Box-Jenkins
12.8.6 Others (ARMAX, ARIMAX, SARMAX, NARMA)
12.9 Time Series Model Development
12.9.1 Base Demand Models
12.9.2 Trend Models
12.9.3 Linear Regression Method
12.9.4 Seasonal Models
12.9.5 Stationarity
12.9.6 WLS Estimation Process
12.9.7 Order and Variance Estimation
12.9.8 Yule-Walker Equations
12.9.9 Durbin-Levinson Algorithm
12.9.10 Innovations Estimation for MA and ARMA Processes
12.9.11 ARIMA Overall Process
12.10 ARTIFICIAL NEURAL NETWORKSArtificial Neural Networks
12.10.1 Introduction to Artificial Neural Networks
12.10.2 Artificial Neurons
12.10.3 Neural network applications
12.10.4 Hopfield Neural Networks
12.10.5 Feed-Forward Networks
12.10.6 Back-Propagation Algorithm
12.10.7 Interior Point Linear Programming Algorithms
12.11 Model Integration
12.12 Demand Prediction
12.12.1 Hourly System Demand Forecasts
12.12.2 One-Step Ahead Forecasts
12.12.3 Hourly Bus Demand Forecasts
12.13 Conclusion

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