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E-raamat: Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems [Wiley Online]

(Pennsylvania State University),
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Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage SystemsSuurem pilt
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Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119819042
"This book presents innovative approaches to the probabilistic planning of generation and transmission systems under uncertainties. It includes renewables and energy storage calculations in using probabilistic and deterministic reliability techniques to assess system performance from a long-term expansion planning viewpoint. It is divided into two sections. The first covers topics related to Generation Expansion Planning (GEP). This includes chapters on cost assessment, methodology and optimization, renewable energy generation, and more. The second part provides information on Transmission System Expansion Planning (TEP). This part explores TEP with reliability constraints, probabilistic production cost simulation for TEP, optimal reliability criteria, and more"--

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.

Author Biographies xvii
Preface xix
Acknowledgments xxv
Part I Generation Expansion Planning 1(212)
1 Introduction
3(12)
1.1 Electricity Outlook
3(5)
1.2 Renewables
8(4)
1.3 Power System Planning
12(3)
2 Background on Generation Expansion Planning
15(6)
2.1 Methodology and Issues
15(3)
2.2 Formulation of the Least-Cost Generation Expansion Planning Problem
18(3)
3 Cost Assessment and Methodologies in Generation Expansion Planning
21(18)
3.1 Basic Cost Concepts
21(5)
3.1.1 Annual Effective Discount Rate
22(1)
3.1.2 Present Value
23(1)
3.1.3 Relationship Between Salvage Value and Depreciation Cost
24(2)
3.2 Methodologies
26(8)
3.2.1 Dynamic Programming
26(1)
3.2.2 Linear Programming
27(1)
3.2.2.1 Investment Cost (Capital Cost)
27(1)
3.2.2.2 Operating Cost
27(1)
3.2.2.3 LP Formula
28(1)
3.2.3 Integer Programming
28(1)
3.2.4 Multi-objective Linear Programming
28(1)
3.2.5 Genetic Algorithm
29(1)
3.2.6 Game Theory
30(2)
3.2.7 Reliability Worth
32(1)
3.2.8 Maximum Principle
32(2)
3.3 Conventional Approach for Load Modeling
34(5)
3.3.1 Load Duration Curve
34(5)
4 Load Model and Generation Expansion Planning
39(28)
4.1 Introduction
39(1)
4.2 Analytical Approach for Long-Term Generation Expansion Planning
40(10)
4.2.1 Representation of Random Load Fluctuations
41(2)
4.2.2 Available Generation Capacities
43(1)
4.2.3 Expected Plant Outputs
44(3)
4.2.4 Expected Annual Energy
47(1)
4.2.5 Reliability Measures
47(1)
4.2.5.1 Expected Annual Unserved Energy
47(1)
4.2.5.2 Annual Loss-of-Load Probability
47(1)
4.2.6 Expected Annual Cost
48(1)
4.2.7 Expected Marginal Values
49(1)
4.3 Optimal Utilization of Hydro Resources
50(6)
4.3.1 Introduction
50(1)
4.3.2 Conventional Peak-Shaving Operation and its Problems
51(1)
4.3.3 Peak-Shaving Operation Based on Analytical Production Costing Model
52(1)
4.3.3.1 Basic Concept
52(1)
4.3.3.2 Peak-Shaving Operation Problem
53(1)
4.3.4 Optimization Procedure for Peak-Shaving Operation
53(3)
4.4 Long-Range Generation Expansion Planning
56(4)
4.4.1 Statement of Long-Range Generation Expansion Planning Problem
56(3)
4.4.1.1 Master Problem and Basic Subproblems
57(1)
4.4.1.2 Hydro Subproblem
58(1)
4.4.2 Optimization Procedures
59(1)
4.5 Case Studies
60(5)
4.5.1 Test for Accuracy of Formulas
60(2)
4.5.2 Test for Solution Convergence and Computing Efficiency
62(3)
4.6 Conclusion
65(2)
5 Probabilistic Production Simulation Model
67(28)
5.1 Introduction
67(1)
5.2 Effective Load Distribution Curve
67(4)
5.3 Case Studies
71(11)
5.3.1 Case Study I: Sample System I With One 30 MW Generator Only
71(4)
5.3.2 Case Study II: Sample System II With One 10 MW Generator Only
75(3)
5.3.3 Case Study III: Sample System III With Two Generators - 30 and 10 MW
78(4)
5.4 Probabilistic Production Simulation Algorithm
82(8)
5.4.1 Hartley Transform
82(8)
5.5 Supply Reserve Rate
90(5)
6 Decision Maker's Satisfaction Using Fuzzy Set Theory
95(16)
6.1 Introduction
95(1)
6.2 Fuzzy Dynamic Programming
96(1)
6.3 Best Generation Mix
97(5)
6.3.1 Problem Statement
97(1)
6.3.2 Objective Functions
97(2)
6.3.3 Constraints
99(1)
6.3.4 Membership Functions
100(1)
6.3.5 The Proposed Fuzzy Dynamic Programming-Based Solution Procedure
101(1)
6.4 Case Study
102(6)
6.4.1 Results and Discussion
104(4)
6.5 Conclusion
108(3)
7 Best Generation Mix Considering Air Pollution Constraints
111(16)
7.1 Introduction
111(1)
7.2 Concept of Flexible Planning
111(1)
7.3 LP Formulation of the Best Generation Mix
112(4)
7.3.1 Problem Statement
112(1)
7.3.2 Objective Functions
113(3)
7.4 Fuzzy LP Formulation of Flexible Generation Mix
116(2)
7.4.1 The Optimal Decision Theory by Fuzzy Set Theory
116(1)
7.4.2 The Function of Fuzzy Linear Programming
117(1)
7.5 Case Studies
118(6)
7.5.1 Results by Non-Fuzzy Model
120(2)
7.5.2 Results by Fuzzy Model
122(2)
7.6 Conclusion
124(3)
8 Generation System Expansion Planning with Renewable Energy
127(14)
8.1 Introduction
127(1)
8.2 LP Formulation of the Best Generation Mix
128(4)
8.2.1 Problem Statement
128(1)
8.2.2 Objective Function and Constraints
129(3)
8.3 Fuzzy LP Formulation of Flexible Generation Mix
132(2)
8.3.1 The Optimal Decision Theory by Fuzzy Set Theory
132(1)
8.3.2 The Function of Fuzzy Linear Programming
133(1)
8.4 Case Studies
134(6)
8.4.1 Test Results
134(1)
8.4.2 Sensitivity Analysis
134(9)
8.4.2.1 Capacity Factor of WTG and SCG
134(6)
8.5 Conclusion
140(1)
9 Reliability Evaluation for Power System Planning with Wind Generators and Multi-Energy Storage Systems
141(36)
9.1 Introduction
141(2)
9.2 Probabilistic Reliability Evaluation by Monte Carlo Simulation
143(2)
9.2.1 Probabilistic Operation Model of Generator 1
143(1)
9.2.2 Probabilistic Operation Model of Generator 2
144(1)
9.3 Probabilistic Output Prediction Model of WTG
145(2)
9.4 Multi-Energy Storage System Operational Model
147(3)
9.4.1 Constraints of ESS control (EUi,k)
149(1)
9.5 Multi-ESS Operation Rule
150(1)
9.5.1 Discharging Mode
150(1)
9.5.2 Charging Mode
151(1)
9.6 Reliability Evaluation with Energy Storage System
151(1)
9.7 Case Studies
152(11)
9.7.1 Power System of Jeju Island
152(4)
9.7.2 Reliability Evaluation of Single-ESS
156(3)
9.7.3 Reliability Evaluation of Multi-ESS
159(3)
9.7.4 Comparison of System A and System B
162(1)
9.8 Conclusion
163(1)
9.A Appendices
164(13)
9.A.1 Single-ESS Model
164(3)
9.A.2 Multi-ESS Model
167(1)
9.A.3 Operation of Multi-ESS Models
168(7)
Method 1: Energy Rate Dispatch Method (ERDM)
173(1)
Method 2: Maximum First Priority Method (MFPM)
173(2)
9.A.4 A Comparative Analysis of Single-ESS and Multi-ESS Models
175(2)
10 Genetic Algorithm for Generation Expansion Planning and Reactive Power Planning
177(26)
10.1 Introduction
177(1)
10.2 Generation Expansion Planning
178(1)
10.3 The Least-Cost GEP Problem
179(1)
10.4 Simple Genetic Algorithm
180(2)
10.4.1 String Representation
181(1)
10.4.2 Genetic Operations
181(1)
10.5 Improved GA for the Least-Cost GEP
182(4)
10.5.1 String Structure
182(1)
10.5.2 Fitness Function
182(1)
10.5.3 Creation of an Artificial Initial Population
183(2)
10.5.4 Stochastic Crossover, Elitism, and Mutation
185(1)
10.6 Case Studies
186(6)
10.6.1 Test Systems' Description
186(1)
10.6.2 Parameters for GEP and IGA
187(2)
10.6.3 Numerical Results
189(3)
10.6.4 Summary
192(1)
10.7 Reactive Power Planning
192(2)
10.8 Decomposition of Reactive Power Planning Problem
194(2)
10.8.1 Investment-Operation Problem
194(1)
10.8.2 Benders Decomposition Formulation
195(1)
10.9 Solution Algorithm for VAR Planning
196(2)
10.10 Simulation Results
198(3)
10.10.1 The 6-bus System
198(1)
10.10.2 IEEE 30-bus System
199(1)
10.10.3 Summary
200(1)
10.11 Conclusion
201(2)
References
203(10)
Part II Transmission System Expansion Planning 213(252)
11 Transmission Expansion Planning Problem
215(20)
11.1 Introduction
215(1)
11.2 Long-Term Transmission Expansion Planning
216(2)
11.3 Yearly Transmission Expansion Planning
218(6)
11.3.1 Power Flow Model
218(2)
11.3.2 Optimal Operation Cost Model
220(2)
11.3.3 Probability of Line Failures
222(1)
11.3.4 Expected Operation Cost
223(1)
11.3.5 Annual Expected Operation Cost
224(1)
11.4 Long-Term Transmission Planning Problem
224(3)
11.4.1 Long-Term Transmission Planning Model
225(1)
11.4.2 Solution Technique for the Planning Problem
226(1)
11.5 Case Study
227(5)
11.6 Conclusion
232(3)
12 Models and Methodologies
235(22)
12.1 Introduction
235(1)
12.2 Transmission System Expansion Planning Problem
235(1)
12.3 Cost Evaluation for TEP Considering Electricity Market
236(1)
12.4 Model Development History for TEP Problem
237(1)
12.5 General DC Power Flow-Based Formulation of TEP Problem
238(8)
12.5.1 Linear Programming
239(1)
12.5.2 Dynamic Programming
240(2)
12.5.3 Integer Programming (IP)
242(3)
12.5.4 Genetic Algorithm by Mixed Integer Programming (MIP)
245(1)
12.6 Branch and Bound Algorithm
246(11)
12.6.1 Branch and Bound Algorithm and Flow Chart
246(2)
12.6.2 Sample System Study by Branch and Bound
248(9)
13 Probabilistic Production Cost Simulation for TEP
257(34)
13.1 Introduction
257(2)
13.2 Modeling of Extended Effective Load for Composite Power System
259(4)
13.3 Probability Distribution Function of the Synthesized Fictitious Equivalent Generator
263(2)
13.4 Reliability Evaluation and Probabilistic Production Cost Simulation at Load Points
265(1)
13.5 Case Studies
266(22)
13.5.1 Numerical Calculation of a Simple Example
266(8)
13.5.2 Case Study: Modified Roy Billinton Test System
274(14)
13.6 Conclusion
288(3)
14 Reliability Constraints
291(84)
14.1 Deterministic Reliability Constraint Using Contingency Constraints
291(31)
14.1.1 Introduction
291(1)
14.1.2 Transmission Expansion Planning Problem
292(5)
14.1.3 Maximum Flow Under Contingency Analysis for Security Constraint
297(1)
14.1.4 Alternative Types of Contingency Criteria
298(1)
14.1.5 Solution Algorithm
299(1)
14.1.6 Case Studies
300(16)
14.1.7 Conclusion
316(3)
Appendix
319(3)
14.2 Deterministic Reliability Constraints
322(11)
14.2.1 Introduction
322(1)
14.2.2 Transmission System Expansion Planning Problem
323(2)
14.2.3 Maximum Flow Under Contingency Analysis for Security Constraint
325(1)
14.2.4 Solution Algorithm
325(1)
14.2.5 Case Studies
326(5)
14.2.6 Conclusion
331(2)
14.3 Probabilistic Reliability Constraints
333(24)
14.3.1 Introduction
333(5)
14.3.2 Transmission System Expansion Planning Problem
338(2)
14.3.3 Composite Power System Reliability Evaluation
340(3)
14.3.4 Solution Algorithm
343(1)
14.3.5 Case Study
344(13)
14.3.6 Conclusion
357(1)
14.4 Outage Cost Constraints
357(16)
14.4.1 Introduction
357(1)
14.4.2 The Objective Function
358(1)
14.4.3 Constraints
359(1)
14.4.4 Outage Cost Assessment of Transmission System
360(3)
14.4.5 Reliability Evaluation of Transmission System
363(1)
14.4.6 Outage Cost Assessment
363(1)
14.4.7 Solution Algorithm
364(1)
14.4.8 Case Study
365(4)
14.4.9 Conclusion
369(4)
14.5 Deterministic-Probabilistic (D-P) Criteria
373(2)
15 Fuzzy Decision Making for TEP
375(26)
15.1 Introduction
375(2)
15.2 Fuzzy Transmission Expansion Planning Problem
377(2)
15.3 Equivalent Crisp Integer Programming and Branch and Bound Method
379(1)
15.4 Membership Functions
380(1)
15.5 Solution Algorithm
381(1)
15.6 Testing
382(8)
15.6.1 Discussion of Results
384(3)
15.6.2 Solution Sensitivity to Reliability Criterion
387(2)
15.6.3 Sensitivity to Budget for Construction Cost
389(1)
15.7 Case Study
390(6)
15.8 Conclusion
396(1)
15.A Appendix
396(5)
15.A.1 Network Modeling of Power System
396(1)
15.A.2 Definition
397(1)
15.A.3 Fuzzy Integer Programming (FIP)
398(3)
16 Optimal Reliability Criteria for TEP
401(32)
16.1 Introduction
401(1)
16.2 Probabilistic Optimal Reliability Criterion
401(15)
16.2.1 Introduction
401(2)
16.2.2 Optimal Reliability Criterion Determination
403(1)
16.2.3 Optimal Composite Power System Expansion Planning
403(3)
16.2.3.1 The Objective Function
403(2)
16.2.3.2 Constraints
405(1)
16.2.4 Composite Power System Reliability Evaluation and Outage Cost Assessment
406(4)
16.2.4.1 Reliability Evaluation at HLI
406(1)
16.2.4.2 Reliability Evaluation at HUH (Composite Power System)
407(2)
16.2.4.3 Flow Chart of the Proposed Methodology for Optimal Reliability Criterion Determination in Transmission System Expansion Planning
409(1)
16.2.5 Case Study
410(6)
16.2.6 Conclusion
416(1)
16.3 Deterministic Reliability Criterion for Composite Power System Expansion Planning
416(17)
16.3.1 Introduction
416(3)
16.3.2 Optimal Reliability Criterion Determination
419(1)
16.3.3 Optimal Composite Power System Expansion Planning
419(2)
16.3.3.1 Composite Power System Expansion Planning Formulation in CmExpP.For
419(2)
16.3.3.2 Flow Chart
421(1)
16.3.4 Composite Power System Reliability Evaluation
421(3)
16.3.4.1 Reliability Indices at Load Points
422(1)
16.3.4.2 Reliability Indices of the Bulk System
423(1)
16.3.5 DMR Evaluation using Maximum Flow Method
424(1)
16.3.6 Flow Chart of Optimal Reliability Criterion Determination
424(1)
16.3.7 Case Study
425(6)
16.3.7.1 Basic Input Data
425(3)
16.3.7.2 Results of Construction Costs of Cases
428(1)
16.3.7.3 Reliability Evaluation
428(3)
16.3.8 Conclusion
431(2)
17 Probabilistic Reliability-Based Expansion Planning with Wind Turbine Generators
433(16)
17.1 Introduction
433(1)
17.2 The Multistate Operation Model of WTG
434(4)
17.2.1 WTG Power Output Model
434(1)
17.2.2 Wind Speed Model
435(1)
17.2.3 The Multistate Model of WTG using Normal Probability Distribution Function
435(3)
17.3 Reliability Evaluation of a Composite Power System with WTG
438(3)
17.3.1 Reliability Indices at Load Buses
440(1)
17.3.2 System Reliability Indices
440(1)
17.4 Case Study
441(7)
17.5 Conclusion
448(1)
17.A Appendix
448(1)
18 Probabilistic Reliability-Based HVDC Expansion Planning with Wind Turbine Generators
449(16)
18.1 The Status of HVDC
449(2)
18.2 HVDC Technology for Energy Efficiency and Grid Reliability
451(4)
18.3 HVDC Impacts on Transmission System Reliability
455(1)
18.4 Case Study
455(10)
References 465(4)
Index 469
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.
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