UID:
almahu_9948026449102882
Format:
1 online resource (588 pages)
ISBN:
0-12-812442-3
,
0-12-812441-5
Note:
Front Cover -- Classical and Recent Aspects of Power System Optimization -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- Chapter 1: Optimization Methods Applied to Power Systems: Current Practices and Challenges -- 1. Introduction -- 2. Key Scheduling Problems in Power System Operation -- 2.1. Unit Commitment -- 2.2. Economic Dispatch -- 2.3. Optimal Power Flow -- 2.4. Fuel Scheduling -- 3. Optimization Methods -- 3.1. Linear Programming -- 3.2. Mixed-Integer Programming -- 3.3. Decomposition Methods -- 3.4. Stochastic Approaches -- 3.5. Artificial Intelligence Methods -- 4. Applications of Optimization Methods on Emerging Challenges -- 4.1. Smart Grid Applications -- 4.2. Distributed Energy Resources and Massive Amount of Renewable Energy Resources -- 4.3. Energy Efficiency -- 5. Current Challenges for Optimization Methods Applicable to Power Systems -- 5.1. Time-Coupling Constraints -- 5.2. Network Constraints -- 5.3. Size and Variation of Control Variables -- 5.4. Stochastic Nature of Renewable Resources -- 6. Summary -- References -- Chapter 2: Application of Robust Optimization Method to Power System Problems -- 1. Introduction -- 2. Different Approaches Implemented to Handle Uncertainty Optimization -- 2.1. Information Gap Decision Theory (IGDT) Technique -- 2.2. Probabilistic Methods -- 2.3. Interval Based Analysis -- 2.4. Possibilistic Procedure -- 2.5. Hybrid Probabilistic and Possibilistic Methods -- 2.6. RO Method -- 3. Robust Optimization Method -- 4. Classification of Problems Handled by Robust Optimization -- 4.1. Unit Commitment (UC) Problem -- 4.2. Transmission Network Expansion Planning (TNEP) -- 4.3. Strategic Bidding of Energy Management System in Electricity Markets -- 4.4. Energy Generation Scheduling in Micro-Grids -- 4.5. Economic Dispatch Problem in Hybrid Energy Systems -- 5. Case Study.
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6. Conclusion -- References -- Chapter 3: Application of New Fast, Efficient-Self adjusting PSO-Search Algorithms in Power Systems' Studies -- 1. Introduction -- 2. Overview of PSO Algorithm -- 3. The Description of Proposed SAPSO Algorithms -- 3.1. Description of Self-Adjusting Algorithms -- 3.2. Verification of the Proposed Algorithms -- 3.2.1. Sphere Test Case -- 3.2.2. Ackley Test Case -- 3.2.3. Griewank Test Case -- 4. Comparison and Selection of the Best Algorithm Among Other Proposed Algorithms -- 5. Application of Proposed Algorithms to Reactive Power Market Problem -- 5.1. A Brief Description of Reactive Market -- 5.1.1. 0 to QBase,i -- 5.1.2. (QBase,i to QA,i) and (QC,i to 0) -- 5.1.3. (QA,i to QB,i) and (Qmin,i to QC,i) -- 5.2. Numerical Results for Reactive Market -- 6. Conclusion and Extended Work -- References -- Further Reading -- Chapter 4: Implementation of Radial Movement Optimization (RMO) Algorithm for Solving Economic Dispatch and Fixed Head Hy ... -- Nomenclature -- 1. Introduction -- 2. Radial Movement Optimization (RMO) -- 2.1. Step-I: Initialization -- 2.2. Step-II: Particle Movement -- 2.3. Step-III: Evaluation of the Fitness of the Particle -- 3. Economic Dispatch -- 3.1. Problem Formulation -- 3.1.1. Objective Function -- 3.1.2. ED With Valve Point Effect -- 3.1.3. Optimization Constraints -- 3.1.4. ED with Consideration of Prohibited Operating Zones (POZ) -- 3.1.5. ED with Considerations of Ramp Rate Limits -- 3.2. Radial Movement Optimization Algorithm for ED Problem -- 3.2.1. Step-1: Initialization -- 3.2.2. Step-2: Generate the Initial Velocity Vector -- 3.2.3. Step-3: Locate the Center Point -- 3.2.4. Step-4: Find the Radial Movement of the Particle -- 3.2.5. Step-5: Evaluate the Fitness of the Particles -- 3.2.6. Step-6: Search Around Gbest -- 3.2.7. Step-7: Determine the New Center Location.
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3.3. Results and Discussion -- 3.3.1. Test System-I -- 3.3.2. Test System-II -- 3.3.3. Test system-III -- 3.3.4. Test System-IV -- 4. Fixed Head Hydro Thermal Scheduling -- 4.1. Problem Formulation -- 4.1.1. System Model -- 4.2. RMO Algorithm for Solving Fixed Head Hydro-Thermal Scheduling Problem -- 4.2.1. Step-I: Initialization -- 4.2.2. Step-2: Generate the Velocity Vector -- 4.2.3. Step-3: Locate the Center Point -- 4.2.4. Step-4: Find the Radial Movement of the Particle -- 4.2.5. Step-5: Evaluate the Fitness of the Particles -- 4.2.6. Step-6: Search Around Gbest -- 4.2.7. Step-7: Determine the New Center Location -- 4.3. Results and Discussion -- 4.3.1. Test System-I -- 4.3.2. Test System-II -- 4.3.3. Test System-III -- 4.3.4. Test System-IV -- 5. Conclusions -- References -- Chapter 5: An Intelligent Approach Based on Metaheuristic for Generator Maintenance Scheduling -- 1. Introduction -- 1.1. Significance of Generator Maintenance Scheduling -- 1.2. Preventive GMS Approaches -- 1.2.1. Objective Functions -- Reliability Criteria -- Economic Criteria -- 1.3. Solution Methods -- 1.4. Objective of the Chapter -- 2. Problem Formulation -- 2.1. Objective Function -- 2.2. Maintenance Constraints -- 2.2.1. Window Constraint -- 2.2.2. Crew Constraint -- 2.2.3. Duration Constraint -- 2.2.4. Reliability Constraint -- 2.2.5. Demand Constraint -- 3. Probabilistic Modeling -- 3.1. Methods to Calculate Reliability Indices -- 3.2. Equivalent Energy Function Method -- 3.2.1. Illustration of EEF Method -- 4. Application of Genetic Algorithm for GMS -- 4.1. Need for Evolutionary Algorithms -- 4.1.1. Drawbacks of MPT -- 4.1.2. Metaheuristic Technique -- 4.1.3. Genetic Algorithm -- 4.2. Application of GA for GMS -- 4.2.1. Genetic Representation -- 4.2.2. Initialization -- 4.2.3. Fitness Function -- 4.2.4. Uniform Integer Crossover and Integer Mutation.
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4.3. Estimation of Fitness Function from a Chromosome -- 5. Results and Discussion -- 5.1. Test System with Three Units -- 5.2. Test System with 5 Units -- 5.3. Test System with 21 Units -- 6. Conclusion -- References -- Chapter 6: Decomposition Methods for Distributed Optimal Power Flow: Panorama and Case Studies of the DC Model -- 1. Introduction -- 2. DC Optimal Power Flow Formulation -- 3. Lagrangian Decomposition Method -- 3.1. Formulation of the Decomposed System -- 3.2. Algorithm [58,59] -- 3.3. Subgradient Method for Multiplier Update -- 4. Augmented Lagrangian Decomposition Method -- 5. Distributed Optimization for Economic Dispatch of Microgrids -- 6. Conclusion and Future Trends -- References -- Further Reading -- Chapter 7: Optimal Power Flow Using Recent Optimization Techniques -- Nomenclature -- 1. Introduction -- 2. OPF Overview -- 3. Optimal Power Flow Problem Formulation -- 3.1. Objective Functions -- 3.1.1. Single Objective Functions -- 3.1.2. Multiobjective Functions -- 3.1.3. Constraints -- 4. Conventional Optimization Methods for Optimal Power Flow -- 4.1. Linear Programming Method -- 4.2. Nonlinear Programming Method -- 4.3. Quadratic Programming Method -- 4.4. Newton's Method -- 4.5. Interior Point Method -- 5. Recent Optimization Methods for Optimal Power Flow -- 5.1. Swarm and Bio-inspired Optimization Techniques -- 5.2. Human-Inspired Optimization Techniques -- 5.3. Physics-Inspired Optimization Techniques -- 5.4. Evolutionary-Inspired Optimization Techniques -- 5.5. Hybrid Optimization Techniques -- 5.6. Artificial Neural Networks (ANN) and Fuzzy Logic Approach -- 6. Conclusions -- References -- Chapter 8: Optimal Conductor Selection of Radial Distribution Feeders: An Overview and New Application Using Grasshopper ... -- 1. Introduction -- 2. Problem Formulation -- 2.1. Objective Function -- 2.2. Constraints.
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2.3. Load Growth -- 3. Overview of Grasshopper Optimization Algorithm -- 4. Simulation Results and Discussions -- 4.1. 26-Bus System -- 4.2. 85-Bus System -- 5. Conclusions -- References -- Chapter 9: Classical and Recent Aspects of Active Power Filters for Power Quality Improvement -- 1. Introduction -- 2. Types of Active Power Filters -- 3. Power Circuits for Realization of Active Power Filters -- 4. Active Power Filters Control Systems -- 4.1. Definition of the Reference Curve of the Active Power Filter Current iFREF or of the Source Current iSREF -- 4.2. Forming of Curve iFREF of the Active Power Filter Current or iSREF of the Source Current, Corresponding to the Refer ... -- 4.3. Stabilization of the Voltage Ud of the Energy Storage Capacitor Cd -- References -- Chapter 10: Optimization-Based Power Capacitor Model Parameterization for Decision Support in Power Distribution Systems -- Chapter Points -- 1. Introduction -- 2. Capacitor Models -- 2.1. Capacitor Model 1 -- 2.2. Capacitor Model 2 -- 2.3. Capacitor Model 3 -- 3. Formulation of the Capacitor Model Parameterization Problem -- 3.1. Capacitor Model 1: Parameterization Problem -- 3.2. Capacitor Model 2: Parameterization Problem -- 3.3. Capacitor Model 3: Parameterization Problem -- 4. Laboratory Setup and Data Acquisition -- 5. Optimization-Based Power Capacitor Model Parameterization Methodology -- 6. Summary -- Problems -- Acknowledgments -- References -- Chapter 11: Two-Level Multidimensional Enhanced Melody Search Algorithm for Dynamic Planning of MV Open-Loop Distribution ... -- Nomenclature -- Note -- 1. Introduction -- 1.1. Background and Motivation -- 1.2. Literature Review and Contributions -- 1.3. Reading Map -- 2. The Music-Inspired Optimization Algorithms: Concepts and Computational Structures -- 2.1. Overview of the Concepts -- 2.2. Computational Structure.
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2.2.1. Single-Level Computational, One-Dimensional Harmony Search Algorithm.
Language:
English
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