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Dynamic Noncooperative Game Models for Deregulated Electricity Markets.

By: Contributor(s): Material type: TextTextPublisher: New York : Nova Science Publishers, Incorporated, 2009Copyright date: ©2009Edition: 1st edDescription: 1 online resource (121 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781611223439
Subject(s): Genre/Form: Additional physical formats: Print version:: Dynamic Noncooperative Game Models for Deregulated Electricity MarketsDDC classification:
  • 333.793/23
LOC classification:
  • HD9685.A2 -- C78 2009eb
Online resources:
Contents:
Intro -- DYNAMIC NONCOOPERATIVE GAMEMODELS FOR DEREGULATEDELECTRICITY MARKETS -- DYNAMICNONCOOPERATIVEGAMEMODELSFORDEREGULATEDELECTRICITYMARKETS -- Contents -- List of Tables -- List of Figures -- Preface -- Restructuring in the ElectricityIndustry -- 1.1. History of Restructuring in the Electricity Industry -- 1.1.1. Motivation and History -- 1.1.2. Structural Changes within the Industry -- 1.2. Prevailing Market Models -- 1.2.1. Pool Model -- 1.2.2. "Pure" Market Model -- 1.2.3. Mixed Pool/Bilateral Market -- 1.3. Electricity Deregulation in the U.S. -- 1.3.1. Power Crisis in California -- 1.3.2. Successful Reform in Pennsylvania -- 1.3.3. Encouraging Start in Texas -- 1.3.4. Transmission Restructuring -- 1.4. Future Trends -- 1.4.1. Distributed Generation (DG) -- 1.4.2. Renewable Energy -- 1.4.3. Smart Grid -- 1.4.4. Energy Efficiency -- Game Theory and Strategic Bidding -- 2.1. History of Game Theory -- 2.2. Strategic Bidding in the Competitive Electricity Market -- 2.3. Review of Adaptive Control -- Adaptation for N-Person Games -- 3.1. Introduction -- 3.2. Formulation of N-person Noncooperative Games -- 3.2.1. Mathematical Models -- 3.2.2. Steady State Control Strategies -- 3.3. One-Sided Adaptation Design for N-person Games -- 3.3.1. Adaptive Mechanism Design -- 3.3.2. Persistent Excitation and Parameter Convergence -- 3.3.3. Adaptation for Nash or Other Control Strategies -- 3.3.4. Computational Iterations -- 3.3.5. Numerical Examples -- 3.4. Two-sided Adaptation for Two-person Games -- 3.4.1. Adaptation Mechanism Design -- 3.5. Multiple-sided Adaptation for N-person Games -- 3.5.1. Adaptation Mechanism Design -- 3.6. Summary -- Sensitivity Analysis of Uncertainties -- 4.1. Introduction -- 4.2. Two-person Games with Uncertain Objectives -- 4.2.1. Multi-modeling Formulation with Unknown Parameters in the PerformanceIndices.
4.3. Sensitivity Analysis of the Uncertain Parameter -- 4.4. Numerical Examples of Strategic Bidding -- 4.4.1. Model Formulated by DM1 (Game A) -- 4.4.2. Model Formulated by DM2 (Game B) -- 4.5. Summary -- Price Strategies in Mixed-StrategySolutions -- 5.1. Introduction -- 5.2. Formulation of an N-person Nash-Bertrand Game -- 5.3. Bimatrix Game with Solutions in Mixed Strategy -- 5.3.1. Formation of Bimatrix Game -- 5.3.2. Solutions in Mixed Strategies -- 5.3.3. Computation of Solutions Using Bilinear Programming -- 5.4. Numerical Examples -- 5.4.1. Case 1: μ11= 0.1,μ21= 0.9 -- 5.4.2. Case 2: μ11= 0.3,μ21= 0.7 -- 5.4.3. Case 3: μ11= 0.5,μ21= 0.5 -- 5.5. Summary -- Appendix A -- Proof of Lemma 1 -- References -- Index.
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Intro -- DYNAMIC NONCOOPERATIVE GAMEMODELS FOR DEREGULATEDELECTRICITY MARKETS -- DYNAMICNONCOOPERATIVEGAMEMODELSFORDEREGULATEDELECTRICITYMARKETS -- Contents -- List of Tables -- List of Figures -- Preface -- Restructuring in the ElectricityIndustry -- 1.1. History of Restructuring in the Electricity Industry -- 1.1.1. Motivation and History -- 1.1.2. Structural Changes within the Industry -- 1.2. Prevailing Market Models -- 1.2.1. Pool Model -- 1.2.2. "Pure" Market Model -- 1.2.3. Mixed Pool/Bilateral Market -- 1.3. Electricity Deregulation in the U.S. -- 1.3.1. Power Crisis in California -- 1.3.2. Successful Reform in Pennsylvania -- 1.3.3. Encouraging Start in Texas -- 1.3.4. Transmission Restructuring -- 1.4. Future Trends -- 1.4.1. Distributed Generation (DG) -- 1.4.2. Renewable Energy -- 1.4.3. Smart Grid -- 1.4.4. Energy Efficiency -- Game Theory and Strategic Bidding -- 2.1. History of Game Theory -- 2.2. Strategic Bidding in the Competitive Electricity Market -- 2.3. Review of Adaptive Control -- Adaptation for N-Person Games -- 3.1. Introduction -- 3.2. Formulation of N-person Noncooperative Games -- 3.2.1. Mathematical Models -- 3.2.2. Steady State Control Strategies -- 3.3. One-Sided Adaptation Design for N-person Games -- 3.3.1. Adaptive Mechanism Design -- 3.3.2. Persistent Excitation and Parameter Convergence -- 3.3.3. Adaptation for Nash or Other Control Strategies -- 3.3.4. Computational Iterations -- 3.3.5. Numerical Examples -- 3.4. Two-sided Adaptation for Two-person Games -- 3.4.1. Adaptation Mechanism Design -- 3.5. Multiple-sided Adaptation for N-person Games -- 3.5.1. Adaptation Mechanism Design -- 3.6. Summary -- Sensitivity Analysis of Uncertainties -- 4.1. Introduction -- 4.2. Two-person Games with Uncertain Objectives -- 4.2.1. Multi-modeling Formulation with Unknown Parameters in the PerformanceIndices.

4.3. Sensitivity Analysis of the Uncertain Parameter -- 4.4. Numerical Examples of Strategic Bidding -- 4.4.1. Model Formulated by DM1 (Game A) -- 4.4.2. Model Formulated by DM2 (Game B) -- 4.5. Summary -- Price Strategies in Mixed-StrategySolutions -- 5.1. Introduction -- 5.2. Formulation of an N-person Nash-Bertrand Game -- 5.3. Bimatrix Game with Solutions in Mixed Strategy -- 5.3.1. Formation of Bimatrix Game -- 5.3.2. Solutions in Mixed Strategies -- 5.3.3. Computation of Solutions Using Bilinear Programming -- 5.4. Numerical Examples -- 5.4.1. Case 1: μ11= 0.1,μ21= 0.9 -- 5.4.2. Case 2: μ11= 0.3,μ21= 0.7 -- 5.4.3. Case 3: μ11= 0.5,μ21= 0.5 -- 5.5. Summary -- Appendix A -- Proof of Lemma 1 -- References -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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