Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management.

Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management.

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Bibliographic Details
Author / Creator:Shahidehpour, Mohammad.
Imprint:Hoboken : Wiley, 2003.
Description:1 online resource (547 pages)
Language:English
Subject:
Electric power systems.
Electric utilities.
Electric utilities -- Deregulation.
United States.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/10369146
Hidden Bibliographic Details
Other authors / contributors:Yamin, Hatim.
Li, Zuyi.
ISBN:9780471463948 174 (NL)
Notes:Description based upon print version of record.
Other form:Print version: Shahidehpour, Mohammad Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management Hoboken : Wiley,c2003 9780471443377
Table of Contents:
  • Preface
  • 1. Market Overview in Electric Power Systems
  • 1.1. Introduction
  • 1.2. Market Structure and Operation
  • 1.2.1. Objective of Market Operation
  • 1.2.2. Electricity Market Models
  • 1.2.3. Market Structure
  • 1.2.4. Power Market Types
  • 1.2.5. Market Power
  • 1.2.6. Key Components in Market Operation
  • 1.3. Overview of the Book
  • 1.3.1. Information Forecasting
  • 1.3.2. Unit Commitment in Restructured Markets
  • 1.3.3. Arbitrage in Electricity Markets
  • 1.3.4. Market Power and Gaming
  • 1.3.5. Asset Valuation and Risk Management
  • 1.3.6. Ancillary Services Auction
  • 1.3.7. Transmission Congestion Management and Pricing
  • 2. Short-Term Load Forecasting
  • 2.1. Introduction
  • 2.1.1. Applications of Load Forecasting
  • 2.1.2. Factors Affecting Load Patterns
  • 2.1.3. Load Forecasting Categories
  • 2.2. Short-Term Load Forecasting with ANN
  • 2.2.1. Introduction to ANN
  • 2.2.2. Application of ANN to STLF
  • 2.2.3. STLF using MATLAB'S ANN Toolbox
  • 2.3. ANN Architecture for STLF
  • 2.3.1. Proposed ANN Architecture
  • 2.3.2. Seasonal ANN
  • 2.3.3. Adaptive Weight
  • 2.3.4. Multiple-Day Forecast
  • 2.4. Numerical Results
  • 2.4.1. Training and Test Data
  • 2.4.2. Stopping Criteria for Training Process
  • 2.4.3. ANN Models for Comparison
  • 2.4.4. Performance of One-Day Forecast
  • 2.4.5. Performance of Multiple-Day Forecast
  • 2.5. Sensitivity Analysis
  • 2.4.1. Possible Models
  • 2.4.2. Sensitivity to Input Factors
  • 2.4.3. Inclusion of Temperature Implicitly
  • 3. Electricity Price Forecasting
  • 3.1. Introduction
  • 3.2. Issues of Electricity Pricing and Forecasting
  • 3.2.1. Electricity Price Basics
  • 3.2.2. Electricity Price Volatility
  • 3.2.3. Categorization of Price Forecasting
  • 3.2.4. Factors Considered in Price Forecasting
  • 3.3. Electricity Price Simulation Module
  • 3.3.1. A Sample of Simulation Strategies
  • 3.3.2. Simulation Example
  • 3.4. Price Forecasting Module based on ANN
  • 3.4.1. ANN Factors in Price Forecasting
  • 3.4.2. 118-Bus System Price Forecasting with ANN
  • 3.5. Performance Evaluation of Price Forecasting
  • 3.5.1. Alternative Methods
  • 3.5.2. Alternative MAPE Definition
  • 3.6. Practical Case Studies
  • 3.6.1. Impact of Data Pre-Processing
  • 3.6.2. Impact of Quantity of Training Vectors
  • 3.6.3. Impact of Quantity of Input Factors
  • 3.6.4. Impact of Adaptive Forecasting
  • 3.6.5. Comparison of ANN Method with Alternative Methods
  • 3.7. Price Volatility Analysis Module
  • 3.7.1. Price Spikes Analysis
  • 3.7.2. Probability Distribution of Electricity Price
  • 3.8. Applications of Price Forecasting
  • 3.8.1. Application of Point Price Forecast to Making Generation Schedule
  • 3.8.2. Application of Probability Distribution of Price to Asset Valuation and Risk Analysis
  • 3.8.3. Application of Probability Distribution of Price to Options Valuation
  • 3.8.4. Application of Conditional Probability Distribution of Price on Load to Forward Price Forecasting
  • 4. Price-Based Unit Commitment
  • 4.1. Introduction
  • 4.2. PBUC Formulation
  • 4.2.1. System Constraints
  • 4.2.2. Unit Constraints
  • 4.3. PBUC Solution
  • 4.3.1. Solution without Emission or Fuel Constraints
  • 4.3.2. Solution with Emission and Fuel Constraints
  • 4.4. Discussion on Solution Methodology
  • 4.4.1. Energy Purchase
  • 4.4.2. Derivation of Steps for Updating Multipliers
  • 4.4.3. Optimality Condition
  • 4.5. Additional Features of PBUC
  • 4.5.1. Different Prices among Buses
  • 4.5.2. Variable Fuel Price as a Function of Fuel Consumption
  • 4.5.3. Application of Lagrangian Augmentation
  • 4.5.4. Bidding Strategy based on PBUC
  • 4.6. Case Studies
  • 4.5.1. Case Study of 5-Unit System
  • 4.5.2. Case Study of 36-Unit System
  • 4.7. Conclusions
  • 5. Arbitrage in Electricity Markets
  • 5.1. Introduction
  • 5.2. Concept of Arbitrage
  • 5.2.1. What is Arbitrage
  • 5.2.2. Usefulness of Arbitrage
  • 5.3. Arbitrage in a Power Market
  • 5.3.1. Same-Commodity Arbitrage
  • 5.3.2. Cross-Commodity Arbitrage
  • 5.3.3. Spark Spread and Arbitrage
  • 5.3.4. Applications of Arbitrage Based on PBUC
  • 5.4. Arbitrage Examples in Power Market
  • 5.4.1. Arbitrage between Energy and Ancillary Service
  • 5.4.2. Arbitrage of Bilateral Contract
  • 5.4.3. Arbitrage between Gas and Power
  • 5.4.4. Arbitrage of Emission Allowance
  • 5.4.5. Arbitrage between Steam and Power
  • 5.5. Conclusions
  • 6. Market Power Analysis Based on Game Theory
  • 6.1. Introduction
  • 6.2. Game Theory
  • 6.2.1. An Instructive Example
  • 6.2.2. Game Methods in Power Systems
  • 6.3. Power Transactions Game
  • 6.3.1. Coalitions among Participants
  • 6.3.2. Generation Cost for Participants
  • 6.3.3. Participant's Objective
  • 6.4. Nash Bargaining Problem
  • 6.4.1. Nash Bargaining Model for Transaction Analysis
  • 6.4.2. Two-Participant Problem Analysis
  • 6.4.3. Discussion on Optimal Transaction and Its Price
  • 6.4.4. Test Results
  • 6.5. Market Competition with Incomplete Information
  • 6.5.1. Participants and Bidding Information
  • 6.5.2. Basic Probability Distribution of the Game
  • 6.5.3. Conditional Probabilities and Expected Payoff
  • 6.5.4. Gaming Methodology
  • 6.6. Market Competition for Multiple Electricity Products
  • 6.6.1. Solution Methodology
  • 6.6.2. Study System
  • 6.6.3. Gaming Methodology
  • 6.7. Conclusions
  • 7. Generation Asset Valuation and Risk Analysis
  • 7.1. Introduction
  • 7.1.1. Asset Valuation
  • 7.1.2. Value at Risk (VaR)
  • 7.1.3. Application of VaR to Asset Valuation in Power Markets
  • 7.2. VaR for Generation Asset Valuation
  • 7.2.1. Framework of the VaR Calculation
  • 7.2.2. Spot Market Price Simulation
  • 7.2.3. A Numerical Example
  • 7.2.4. A Practical Example
  • 7.2.5. Sensitivity Analysis
  • 7.3. Generation Capacity Valuation
  • 7.3.1. Framework of VaR Calculation
  • 7.3.2. An Example
  • 7.3.3. Sensitivity Analysis
  • 7.4. Conclusions
  • 8. Security-Constrained Unit Commitment
  • 8.1. Introduction
  • 8.2. SCUC Problem Formulation
  • 8.2.1. Discussion on Ramping Constraints
  • 8.3. Benders Decomposition Solution of SCUC
  • 8.3.1. Benders Decomposition
  • 8.3.2. Application of Benders Decomposition to SCUC
  • 8.3.3. Master Problem Formulation
  • 8.4. SCUC to Minimize Network Violation
  • 8.4.1. Linearization of Network Constraints
  • 8.4.2. Subproblem Formulation
  • 8.4.3. Benders Cuts Formulation
  • 8.4.4. Case Study
  • 8.5. SCUC Application to Minimize EUE - Impact of Reliability
  • 8.5.1. Subproblem Formulation and Solution
  • 8.5.2. Case Study
  • 8.6. Conclusions
  • 9. Ancillary Services Auction Market Design
  • 9.1. Introduction
  • 9.2. Ancillary Services for Restructuring
  • 9.3. Forward Ancillary Services Auction--Sequential Approach
  • 9.3.1. Two Alternatives in Sequential Ancillary Services Auction
  • 9.3.2. Ancillary Services Scheduling
  • 9.3.3. Design of the Ancillary Services Auction Market
  • 9.3.4. Case Study
  • 9.3.5. Discussions
  • 9.4. Forward Ancillary Services Auction--Simultaneous Approach
  • 9.4.1. Design Options for Simultaneous Auction of Ancillary Services
  • 9.4.2. Rational Buyer Auction
  • 9.4.3. Marginal Pricing Auction
  • 9.4.4. Discussions
  • 9.5. Automatic Generation Control (AGC)
  • 9.5.1. AGC Functions
  • 9.5.2. AGC Response
  • 9.5.3. AGC Units Revenue Adequacy
  • 9.5.4. AGC Pricing
  • 9.5.5. Discussions
  • 9.6. Conclusions
  • 10. Transmission Congestion Management and Pricing
  • 10.1. Introduction
  • 10.2. Transmission Cost Allocation Methods
  • 10.2.1. Postage-Stamp Rate Method
  • 10.2.2. Contract Path Method
  • 10.2.3. MW-Mile Method
  • 10.2.4. Unused Transmission Capacity Method
  • 10.2.5. MVA-Mile Method
  • 10.2.6. Counter-Flow Method
  • 10.2.7. Distribution Factors Method
  • 10.2.8. AC Power Flow Method
  • 10.2.9. Tracing Methods
  • 10.2.10. Comparison of Cost Allocation Methods
  • 10.3. Examples for Transmission Cost Allocation Methods
  • 10.3.1. Cost Allocation Using Distribution Factors Method
  • 10.3.2. Cost Allocation Using Bialek's Tracing Method
  • 10.3.3. Cost Allocation Using Kirschen's Tracing Method
  • 10.3.4. Comparing the Three Cost Allocation Methods
  • 10.4. LMP, FTR, and Congestion Management
  • 10.4.1. Locational Marginal Price (LMP)
  • 10.4.2. LMP Application in Determining Zonal Boundaries
  • 10.4.3. Firm Transmission Right (FTR)
  • 10.4.4. FTR Auction
  • 10.4.5. Zonal Congestion Management
  • 10.5. A Comprehensive Transmission Pricing Scheme
  • 10.5.1. Outline of the Proposed Transmission Pricing Scheme
  • 10.5.2. Prioritization of Transmission Dispatch
  • 10.5.3. Calculation of Transmission Usage and Congestion Charges and FTR Credits
  • 10.5.4. Numerical Example
  • 10.6. Conclusions
  • Appendix
  • A. List of Symbols
  • B. Mathematical Derivation
  • B.1. Derivation of Probability Distribution
  • B.2. Lagrangian Augmentation with Inequality Constraints
  • C. RTS Load Data
  • D. Example Systems Data
  • D.1. 5-Unit System
  • D.2. 36-Unit System
  • D.3. 6-Unit System
  • D.4. Modified IEEE 30-Bus System
  • D.5. 118-Bus System
  • E. Game Theory Concepts
  • E.1. Equilibrium in Non-Cooperative Games
  • E.2. Characteristics Function
  • E.3. N-Players Cooperative Games
  • E.4. Games with Incomplete Information
  • F. Congestion Charges Calculation
  • F.1. Calculations of Congestion Charges using Contributions of Generators
  • F.2. Calculations of Congestion Charges using Contributions of Loads
  • References
  • Index

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