Jaeseok Choi, Kwang Y. Lee

Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems (eBook, PDF)

• Format: PDF

Produktbeschreibung

Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems Discover how modern techniques have shaped complex power system expansion planning with this one-stop resource from two experts in the field Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems delivers a comprehensive collection of innovative approaches to the probabilistic planning of generation and transmission systems under uncertainties. The book includes renewables and energy storage calculations when using probabilistic and deterministic reliability techniques to assess system performance from a long-term expansion planning viewpoint. Divided into two sections, the book first covers topics related to Generation Expansion Planning, with chapters on cost assessment, methodology and optimization, and more. The second and final section provides information on Transmission System Expansion Planning, with chapters on reliability constraints, probabilistic production cost simulation, and more. Probabilistic Power System Expansion Planning compares the optimization and methodology across dynamic, linear, and integer programming and explores the branch and bound algorithm. Along with case studies to demonstrate how the techniques described within have been applied in complex power system expansion planning problems, readers will enjoy: * A thorough discussion of generation expansion planning, including cost assessment, methodology and optimization, and probabilistic production cost * An exploration of transmission system expansion planning, including the branch and bound algorithm, probabilistic production cost simulation for TEP, and TEP with reliability constraints * An examination of fuzzy decision making applied to transmission system expansion planning * A treatment of probabilistic reliability-based grid expansion planning of power systems including wind turbine generators Perfect for power and energy systems designers, planners, operators, consultants, practicing engineers, software developers, and researchers, Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems will also earn a place in the libraries of practicing engineers who regularly deal with optimization problems.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 512
• Erscheinungstermin: 22. September 2021
• Englisch
• ISBN-13: 9781119684152
• Artikelnr.: 62717760

Autorenporträt

JAESEOK CHOI, PHD, is Full Professor at Gyeongsang National University and is a Fellow of the Korean Institute of Electrical Engineers. He is a senior member of the IEEE Power Engineering Society and participates in the Reliability, Risk, and Probability Applications Subcommittee. KWANG Y. LEE, PHD, is Professor and Chair of Electrical and Computer Engineering at Baylor University and a Life Fellow of IEEE. He is a member of the Intelligent Systems Subcommittee and Station Control Subcommittee of the IEEE Power and Energy Society.

Inhaltsangabe

Author Biographies xvii

Preface xix

Acknowledgments xxv

Part I Generation Expansion Planning 1

1 Introduction 3

1.1 Electricity Outlook 3

1.2 Renewables 8

1.3 Power System Planning 12

2 Background on Generation Expansion Planning 15

2.1 Methodology and Issues 15

2.2 Formulation of the Least-Cost Generation Expansion Planning Problem 18

3 Cost Assessment and Methodologies in Generation Expansion Planning 21

3.1 Basic Cost Concepts 21

3.1.1 Annual Effective Discount Rate 22

3.1.2 Present Value 23

3.1.3 Relationship Between Salvage Value and Depreciation Cost 24

3.2 Methodologies 26

3.2.1 Dynamic Programming 26

3.2.2 Linear Programming 27

3.2.2.1 Investment Cost (Capital Cost) 27

3.2.2.2 Operating Cost 27

3.2.2.3 LP Formula 28

3.2.3 Integer Programming 28

3.2.4 Multi-objective Linear Programming 28

3.2.5 Genetic Algorithm 29

3.2.6 Game Theory 30

3.2.7 Reliability Worth 32

3.2.8 Maximum Principle 32

3.3 Conventional Approach for Load Modeling 34

3.3.1 Load Duration Curve 34

4 Load Model and Generation Expansion Planning 39

4.1 Introduction 39

4.2 Analytical Approach for Long-Term Generation Expansion Planning 40

4.2.1 Representation of Random Load Fluctuations 41

4.2.2 Available Generation Capacities 43

4.2.3 Expected Plant Outputs 44

4.2.4 Expected Annual Energy 47

4.2.5 Reliability Measures 47

4.2.5.1 Expected Annual Unserved Energy 47

4.2.5.2 Annual Loss-of-Load Probability 47

4.2.6 Expected Annual Cost 48

4.2.7 Expected Marginal Values 49

4.3 Optimal Utilization of Hydro Resources 50

4.3.1 Introduction 50

4.3.2 Conventional Peak-Shaving Operation and its Problems 51

4.3.3 Peak-Shaving Operation Based on Analytical Production Costing Model 52

4.3.3.1 Basic Concept 52

4.3.3.2 Peak-Shaving Operation Problem 53

4.3.4 Optimization Procedure for Peak-Shaving Operation 53

4.4 Long-Range Generation Expansion Planning 56

4.4.1 Statement of Long-Range Generation Expansion Planning Problem 56

4.4.1.1 Master Problem and Basic Subproblems 57

4.4.1.2 Hydro Subproblem 58

4.4.2 Optimization Procedures 59

4.5 Case Studies 60

4.5.1 Test for Accuracy of Formulas 60

4.5.2 Test for Solution Convergence and Computing Efficiency 62

4.6 Conclusion 65

5 Probabilistic Production Simulation Model 67

5.1 Introduction 67

5.2 Effective Load Distribution Curve 67

5.3 Case Studies 71

5.3.1 Case Study I: Sample System I With One 30MW Generator Only 71

5.3.2 Case Study II: Sample System II With One 10MW Generator Only 75

5.3.3 Case Study III: Sample System III With Two Generators - 30 and 10MW 78

5.4 Probabilistic Production Simulation Algorithm 82

5.4.1 Hartley Transform 82

5.5 Supply Reserve Rate 90

6 Decision Maker's Satisfaction Using Fuzzy Set Theory 95

6.1 Introduction 95

6.2 Fuzzy Dynamic Programming 96

6.3 Best Generation Mix 97

6.3.1 Problem Statement 97

6.3.2 Objective Functions 97

6.3.3 Constraints 99

6.3.4 Membership Functions 100

6.3.5 The Proposed Fuzzy Dynamic Programming-Based Solution Procedure 101

6.4 Case Study 102

6.4.1 Results and Discussion 104