{"product_id":"probabilistic-power-system-expansion-planning-with-renewable-energy-resources-and-energy-storage-systems-isbn-9781119684138","title":"Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems","description":"Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems \u003cp\u003e\u003cb\u003eDiscover how modern techniques have shaped complex power system expansion planning with this one-stop resource from two experts in the field\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eProbabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems delivers\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eDivided 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eProbabilistic Power System Expansion Planning\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough discussion of generation expansion planning, including cost assessment, methodology and optimization, and probabilistic production cost\u003c\/li\u003e \u003cli\u003eAn exploration of transmission system expansion planning, including the branch and bound algorithm, probabilistic production cost simulation for TEP, and TEP with reliability constraints\u003c\/li\u003e \u003cli\u003eAn examination of fuzzy decision making applied to transmission system expansion planning\u003c\/li\u003e \u003cli\u003eA treatment of probabilistic reliability-based grid expansion planning of power systems including wind turbine generators\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for power and energy systems designers, planners, operators, consultants, practicing engineers, software developers, and researchers, \u003ci\u003eProbabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems\u003c\/i\u003e will also earn a place in the libraries of practicing engineers who regularly deal with optimization problems. \u003c\/p\u003e\u003cp\u003eAuthor Biographies xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Generation Expansion Planning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Electricity Outlook 3\u003c\/p\u003e \u003cp\u003e1.2 Renewables 8\u003c\/p\u003e \u003cp\u003e1.3 Power System Planning 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Background on Generation Expansion Planning 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Methodology and Issues 15\u003c\/p\u003e \u003cp\u003e2.2 Formulation of the Least-Cost Generation Expansion Planning Problem 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Cost Assessment and Methodologies in Generation Expansion Planning  21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Basic Cost Concepts 21\u003c\/p\u003e \u003cp\u003e3.1.1 Annual Effective Discount Rate 22\u003c\/p\u003e \u003cp\u003e3.1.2 Present Value 23\u003c\/p\u003e \u003cp\u003e3.1.3 Relationship Between Salvage Value and Depreciation Cost 24\u003c\/p\u003e \u003cp\u003e3.2 Methodologies 26\u003c\/p\u003e \u003cp\u003e3.2.1 Dynamic Programming 26\u003c\/p\u003e \u003cp\u003e3.2.2 Linear Programming 27\u003c\/p\u003e \u003cp\u003e3.2.2.1 Investment Cost (Capital Cost) 27\u003c\/p\u003e \u003cp\u003e3.2.2.2 Operating Cost 27\u003c\/p\u003e \u003cp\u003e3.2.2.3 LP Formula 28\u003c\/p\u003e \u003cp\u003e3.2.3 Integer Programming 28\u003c\/p\u003e \u003cp\u003e3.2.4 Multi-objective Linear Programming 28\u003c\/p\u003e \u003cp\u003e3.2.5 Genetic Algorithm 29\u003c\/p\u003e \u003cp\u003e3.2.6 Game Theory 30\u003c\/p\u003e \u003cp\u003e3.2.7 Reliability Worth 32\u003c\/p\u003e \u003cp\u003e3.2.8 Maximum Principle 32\u003c\/p\u003e \u003cp\u003e3.3 Conventional Approach for Load Modeling 34\u003c\/p\u003e \u003cp\u003e3.3.1 Load Duration Curve 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Load Model and Generation Expansion Planning 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 39\u003c\/p\u003e \u003cp\u003e4.2 Analytical Approach for Long-Term Generation Expansion Planning 40\u003c\/p\u003e \u003cp\u003e4.2.1 Representation of Random Load Fluctuations 41\u003c\/p\u003e \u003cp\u003e4.2.2 Available Generation Capacities 43\u003c\/p\u003e \u003cp\u003e4.2.3 Expected Plant Outputs 44\u003c\/p\u003e \u003cp\u003e4.2.4 Expected Annual Energy 47\u003c\/p\u003e \u003cp\u003e4.2.5 Reliability Measures 47\u003c\/p\u003e \u003cp\u003e4.2.5.1 Expected Annual Unserved Energy 47\u003c\/p\u003e \u003cp\u003e4.2.5.2 Annual Loss-of-Load Probability 47\u003c\/p\u003e \u003cp\u003e4.2.6 Expected Annual Cost 48\u003c\/p\u003e \u003cp\u003e4.2.7 Expected Marginal Values 49\u003c\/p\u003e \u003cp\u003e4.3 Optimal Utilization of Hydro Resources 50\u003c\/p\u003e \u003cp\u003e4.3.1 Introduction 50\u003c\/p\u003e \u003cp\u003e4.3.2 Conventional Peak-Shaving Operation and its Problems 51\u003c\/p\u003e \u003cp\u003e4.3.3 Peak-Shaving Operation Based on Analytical Production Costing Model 52\u003c\/p\u003e \u003cp\u003e4.3.3.1 Basic Concept 52\u003c\/p\u003e \u003cp\u003e4.3.3.2 Peak-Shaving Operation Problem 53\u003c\/p\u003e \u003cp\u003e4.3.4 Optimization Procedure for Peak-Shaving Operation 53\u003c\/p\u003e \u003cp\u003e4.4 Long-Range Generation Expansion Planning 56\u003c\/p\u003e \u003cp\u003e4.4.1 Statement of Long-Range Generation Expansion Planning Problem 56\u003c\/p\u003e \u003cp\u003e4.4.1.1 Master Problem and Basic Subproblems 57\u003c\/p\u003e \u003cp\u003e4.4.1.2 Hydro Subproblem 58\u003c\/p\u003e \u003cp\u003e4.4.2 Optimization Procedures 59\u003c\/p\u003e \u003cp\u003e4.5 Case Studies 60\u003c\/p\u003e \u003cp\u003e4.5.1 Test for Accuracy of Formulas 60\u003c\/p\u003e \u003cp\u003e4.5.2 Test for Solution Convergence and Computing Efficiency 62\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Probabilistic Production Simulation Model 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 67\u003c\/p\u003e \u003cp\u003e5.2 Effective Load Distribution Curve 67\u003c\/p\u003e \u003cp\u003e5.3 Case Studies 71\u003c\/p\u003e \u003cp\u003e5.3.1 Case Study I: Sample System I With One 30MW Generator Only 71\u003c\/p\u003e \u003cp\u003e5.3.2 Case Study II: Sample System II With One 10MW Generator Only 75\u003c\/p\u003e \u003cp\u003e5.3.3 Case Study III: Sample System III With Two Generators – 30 and 10MW 78\u003c\/p\u003e \u003cp\u003e5.4 Probabilistic Production Simulation Algorithm 82\u003c\/p\u003e \u003cp\u003e5.4.1 Hartley Transform 82\u003c\/p\u003e \u003cp\u003e5.5 Supply Reserve Rate 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Decision Maker’s Satisfaction Using Fuzzy Set Theory 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 95\u003c\/p\u003e \u003cp\u003e6.2 Fuzzy Dynamic Programming 96\u003c\/p\u003e \u003cp\u003e6.3 Best Generation Mix 97\u003c\/p\u003e \u003cp\u003e6.3.1 Problem Statement 97\u003c\/p\u003e \u003cp\u003e6.3.2 Objective Functions 97\u003c\/p\u003e \u003cp\u003e6.3.3 Constraints 99\u003c\/p\u003e \u003cp\u003e6.3.4 Membership Functions 100\u003c\/p\u003e \u003cp\u003e6.3.5 The Proposed Fuzzy Dynamic Programming-Based Solution Procedure 101\u003c\/p\u003e \u003cp\u003e6.4 Case Study 102\u003c\/p\u003e \u003cp\u003e6.4.1 Results and Discussion 104\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Best Generation Mix Considering Air Pollution Constraints 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 111\u003c\/p\u003e \u003cp\u003e7.2 Concept of Flexible Planning 111\u003c\/p\u003e \u003cp\u003e7.3 LP Formulation of the Best Generation Mix 112\u003c\/p\u003e \u003cp\u003e7.3.1 Problem Statement 112\u003c\/p\u003e \u003cp\u003e7.3.2 Objective Functions 113\u003c\/p\u003e \u003cp\u003e7.4 Fuzzy LP Formulation of Flexible Generation Mix 116\u003c\/p\u003e \u003cp\u003e7.4.1 The Optimal Decision Theory by Fuzzy Set Theory 116\u003c\/p\u003e \u003cp\u003e7.4.2 The Function of Fuzzy Linear Programming 117\u003c\/p\u003e \u003cp\u003e7.5 Case Studies 118\u003c\/p\u003e \u003cp\u003e7.5.1 Results by Non-Fuzzy Model 120\u003c\/p\u003e \u003cp\u003e7.5.2 Results by Fuzzy Model 122\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Generation System Expansion Planning with Renewable Energy 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 127\u003c\/p\u003e \u003cp\u003e8.2 LP Formulation of the Best Generation Mix 128\u003c\/p\u003e \u003cp\u003e8.2.1 Problem Statement 128\u003c\/p\u003e \u003cp\u003e8.2.2 Objective Function and Constraints 129\u003c\/p\u003e \u003cp\u003e8.3 Fuzzy LP Formulation of Flexible Generation Mix 132\u003c\/p\u003e \u003cp\u003e8.3.1 The Optimal Decision Theory by Fuzzy Set Theory 132\u003c\/p\u003e \u003cp\u003e8.3.2 The Function of Fuzzy Linear Programming 133\u003c\/p\u003e \u003cp\u003e8.4 Case Studies 134\u003c\/p\u003e \u003cp\u003e8.4.1 Test Results 134\u003c\/p\u003e \u003cp\u003e8.4.2 Sensitivity Analysis 134\u003c\/p\u003e \u003cp\u003e8.4.2.1 Capacity Factor of WTG and SCG 134\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Reliability Evaluation for Power System Planning with Wind Generators and Multi-Energy Storage Systems 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 141\u003c\/p\u003e \u003cp\u003e9.2 Probabilistic Reliability Evaluation by Monte Carlo Simulation 143\u003c\/p\u003e \u003cp\u003e9.2.1 Probabilistic Operation Model of Generator 1 143\u003c\/p\u003e \u003cp\u003e9.2.2 Probabilistic Operation Model of Generator 2 144\u003c\/p\u003e \u003cp\u003e9.3 Probabilistic Output Prediction Model of WTG 145\u003c\/p\u003e \u003cp\u003e9.4 Multi-Energy Storage System Operational Model 147\u003c\/p\u003e \u003cp\u003e9.4.1 Constraints of ESS control (\u003ci\u003eEUi\u003c\/i\u003e,\u003ci\u003ek\u003c\/i\u003e) 149\u003c\/p\u003e \u003cp\u003e9.5 Multi-ESS Operation Rule 150\u003c\/p\u003e \u003cp\u003e9.5.1 Discharging Mode 150\u003c\/p\u003e \u003cp\u003e9.5.2 Charging Mode 151\u003c\/p\u003e \u003cp\u003e9.6 Reliability Evaluation with Energy Storage System 151\u003c\/p\u003e \u003cp\u003e9.7 Case Studies 152\u003c\/p\u003e \u003cp\u003e9.7.1 Power System of Jeju Island 152\u003c\/p\u003e \u003cp\u003e9.7.2 Reliability Evaluation of Single-ESS 156\u003c\/p\u003e \u003cp\u003e9.7.3 Reliability Evaluation of Multi-ESS 159\u003c\/p\u003e \u003cp\u003e9.7.4 Comparison of System A and System B 162\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 163\u003c\/p\u003e \u003cp\u003e9.A Appendices 164\u003c\/p\u003e \u003cp\u003e9.A.1 Single-ESS Model 164\u003c\/p\u003e \u003cp\u003e9.A.2 Multi-ESS Model 167\u003c\/p\u003e \u003cp\u003e9.A.3 Operation of Multi-ESS Models 168\u003c\/p\u003e \u003cp\u003eMethod 1: Energy Rate Dispatch Method (ERDM) 173\u003c\/p\u003e \u003cp\u003eMethod 2: Maximum First Priority Method (MFPM) 173\u003c\/p\u003e \u003cp\u003e9.A.4 A Comparative Analysis of Single-ESS and Multi-ESS Models 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Genetic Algorithm for Generation Expansion Planning and Reactive Power Planning 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 177\u003c\/p\u003e \u003cp\u003e10.2 Generation Expansion Planning 178\u003c\/p\u003e \u003cp\u003e10.3 The Least-Cost GEP Problem 179\u003c\/p\u003e \u003cp\u003e10.4 Simple Genetic Algorithm 180\u003c\/p\u003e \u003cp\u003e10.4.1 String Representation 181\u003c\/p\u003e \u003cp\u003e10.4.2 Genetic Operations 181\u003c\/p\u003e \u003cp\u003e10.5 Improved GA for the Least-Cost GEP 182\u003c\/p\u003e \u003cp\u003e10.5.1 String Structure 182\u003c\/p\u003e \u003cp\u003e10.5.2 Fitness Function 182\u003c\/p\u003e \u003cp\u003e10.5.3 Creation of an Artificial Initial Population 183\u003c\/p\u003e \u003cp\u003e10.5.4 Stochastic Crossover, Elitism, and Mutation 185\u003c\/p\u003e \u003cp\u003e10.6 Case Studies 186\u003c\/p\u003e \u003cp\u003e10.6.1 Test Systems’ Description 186\u003c\/p\u003e \u003cp\u003e10.6.2 Parameters for GEP and IGA 187\u003c\/p\u003e \u003cp\u003e10.6.3 Numerical Results 189\u003c\/p\u003e \u003cp\u003e10.6.4 Summary 192\u003c\/p\u003e \u003cp\u003e10.7 Reactive Power Planning 192\u003c\/p\u003e \u003cp\u003e10.8 Decomposition of Reactive Power Planning Problem 194\u003c\/p\u003e \u003cp\u003e10.8.1 Investment-Operation Problem 194\u003c\/p\u003e \u003cp\u003e10.8.2 Benders Decomposition Formulation 195\u003c\/p\u003e \u003cp\u003e10.9 Solution Algorithm for VAR Planning 196\u003c\/p\u003e \u003cp\u003e10.10 Simulation Results 198\u003c\/p\u003e \u003cp\u003e10.10.1 The 6-bus System 198\u003c\/p\u003e \u003cp\u003e10.10.2 IEEE 30-bus System 199\u003c\/p\u003e \u003cp\u003e10.10.3 Summary 200\u003c\/p\u003e \u003cp\u003e10.11 Conclusion 201\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Transmission System Expansion Planning 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Transmission Expansion Planning Problem 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 215\u003c\/p\u003e \u003cp\u003e11.2 Long-Term Transmission Expansion Planning 216\u003c\/p\u003e \u003cp\u003e11.3 Yearly Transmission Expansion Planning 218\u003c\/p\u003e \u003cp\u003e11.3.1 Power Flow Model 218\u003c\/p\u003e \u003cp\u003e11.3.2 Optimal Operation Cost Model 220\u003c\/p\u003e \u003cp\u003e11.3.3 Probability of Line Failures 222\u003c\/p\u003e \u003cp\u003e11.3.4 Expected Operation Cost 223\u003c\/p\u003e \u003cp\u003e11.3.5 Annual Expected Operation Cost 224\u003c\/p\u003e \u003cp\u003e11.4 Long-Term Transmission Planning Problem 224\u003c\/p\u003e \u003cp\u003e11.4.1 Long-Term Transmission Planning Model 225\u003c\/p\u003e \u003cp\u003e11.4.2 Solution Technique for the Planning Problem 226\u003c\/p\u003e \u003cp\u003e11.5 Case Study 227\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Models and Methodologies 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 235\u003c\/p\u003e \u003cp\u003e12.2 Transmission System Expansion Planning Problem 235\u003c\/p\u003e \u003cp\u003e12.3 Cost Evaluation for TEP Considering Electricity Market 236\u003c\/p\u003e \u003cp\u003e12.4 Model Development History for TEP Problem 237\u003c\/p\u003e \u003cp\u003e12.5 General DC Power Flow-Based Formulation of TEP Problem 238\u003c\/p\u003e \u003cp\u003e12.5.1 Linear Programming 239\u003c\/p\u003e \u003cp\u003e12.5.2 Dynamic Programming 240\u003c\/p\u003e \u003cp\u003e12.5.3 Integer Programming (IP) 242\u003c\/p\u003e \u003cp\u003e12.5.4 Genetic Algorithm by Mixed Integer Programming (MIP) 245\u003c\/p\u003e \u003cp\u003e12.6 Branch and Bound Algorithm 246\u003c\/p\u003e \u003cp\u003e12.6.1 Branch and Bound Algorithm and Flow Chart 246\u003c\/p\u003e \u003cp\u003e12.6.2 Sample System Study by Branch and Bound 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Probabilistic Production Cost Simulation for TEP 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 257\u003c\/p\u003e \u003cp\u003e13.2 Modeling of Extended Effective Load for Composite Power System 259\u003c\/p\u003e \u003cp\u003e13.3 Probability Distribution Function of the Synthesized Fictitious Equivalent Generator 263\u003c\/p\u003e \u003cp\u003e13.4 Reliability Evaluation and Probabilistic Production Cost Simulation at Load Points 265\u003c\/p\u003e \u003cp\u003e13.5 Case Studies 266\u003c\/p\u003e \u003cp\u003e13.5.1 Numerical Calculation of a Simple Example 266\u003c\/p\u003e \u003cp\u003e13.5.2 Case Study: Modified Roy Billinton Test System 274\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Reliability Constraints 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Deterministic Reliability Constraint Using Contingency Constraints 291\u003c\/p\u003e \u003cp\u003e14.1.1 Introduction 291\u003c\/p\u003e \u003cp\u003e14.1.2 Transmission Expansion Planning Problem 292\u003c\/p\u003e \u003cp\u003e14.1.3 Maximum Flow Under Contingency Analysis for Security Constraint 297\u003c\/p\u003e \u003cp\u003e14.1.4 Alternative Types of Contingency Criteria 298\u003c\/p\u003e \u003cp\u003e14.1.5 Solution Algorithm 299\u003c\/p\u003e \u003cp\u003e14.1.6 Case Studies 300\u003c\/p\u003e \u003cp\u003e14.1.7 Conclusion 316\u003c\/p\u003e \u003cp\u003eAppendix 319\u003c\/p\u003e \u003cp\u003e14.2 Deterministic Reliability Constraints 322\u003c\/p\u003e \u003cp\u003e14.2.1 Introduction 322\u003c\/p\u003e \u003cp\u003e14.2.2 Transmission System Expansion Planning Problem 323\u003c\/p\u003e \u003cp\u003e14.2.3 Maximum Flow Under Contingency Analysis for Security Constraint 325\u003c\/p\u003e \u003cp\u003e14.2.4 Solution Algorithm 325\u003c\/p\u003e \u003cp\u003e14.2.5 Case Studies 326\u003c\/p\u003e \u003cp\u003e14.2.6 Conclusion 331\u003c\/p\u003e \u003cp\u003e14.3 Probabilistic Reliability Constraints 333\u003c\/p\u003e \u003cp\u003e14.3.1 Introduction 333\u003c\/p\u003e \u003cp\u003e14.3.2 Transmission System Expansion Planning Problem 338\u003c\/p\u003e \u003cp\u003e14.3.3 Composite Power System Reliability Evaluation 340\u003c\/p\u003e \u003cp\u003e14.3.4 Solution Algorithm 343\u003c\/p\u003e \u003cp\u003e14.3.5 Case Study 344\u003c\/p\u003e \u003cp\u003e14.3.6 Conclusion 357\u003c\/p\u003e \u003cp\u003e14.4 Outage Cost Constraints 357\u003c\/p\u003e \u003cp\u003e14.4.1 Introduction 357\u003c\/p\u003e \u003cp\u003e14.4.2 The Objective Function 358\u003c\/p\u003e \u003cp\u003e14.4.3 Constraints 359\u003c\/p\u003e \u003cp\u003e14.4.4 Outage Cost Assessment of Transmission System 360\u003c\/p\u003e \u003cp\u003e14.4.5 Reliability Evaluation of Transmission System 363\u003c\/p\u003e \u003cp\u003e14.4.6 Outage Cost Assessment 363\u003c\/p\u003e \u003cp\u003e14.4.7 Solution Algorithm 364\u003c\/p\u003e \u003cp\u003e14.4.8 Case Study 365\u003c\/p\u003e \u003cp\u003e14.4.9 Conclusion 369\u003c\/p\u003e \u003cp\u003e14.5 Deterministic–Probabilistic (D–P) Criteria 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Fuzzy Decision Making for TEP 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 375\u003c\/p\u003e \u003cp\u003e15.2 Fuzzy Transmission Expansion Planning Problem 377\u003c\/p\u003e \u003cp\u003e15.3 Equivalent Crisp Integer Programming and Branch and Bound Method 379\u003c\/p\u003e \u003cp\u003e15.4 Membership Functions 380\u003c\/p\u003e \u003cp\u003e15.5 Solution Algorithm 381\u003c\/p\u003e \u003cp\u003e15.6 Testing 382\u003c\/p\u003e \u003cp\u003e15.6.1 Discussion of Results 384\u003c\/p\u003e \u003cp\u003e15.6.2 Solution Sensitivity to Reliability Criterion 387\u003c\/p\u003e \u003cp\u003e15.6.3 Sensitivity to Budget for Construction Cost 389\u003c\/p\u003e \u003cp\u003e15.7 Case Study 390\u003c\/p\u003e \u003cp\u003e15.8 Conclusion 396\u003c\/p\u003e \u003cp\u003e15.A Appendix 396\u003c\/p\u003e \u003cp\u003e15.A.1 Network Modeling of Power System 396\u003c\/p\u003e \u003cp\u003e15.A.2 Definition 397\u003c\/p\u003e \u003cp\u003e15.A.3 Fuzzy Integer Programming (FIP) 398\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Optimal Reliability Criteria for TEP 401\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 401\u003c\/p\u003e \u003cp\u003e16.2 Probabilistic Optimal Reliability Criterion 401\u003c\/p\u003e \u003cp\u003e16.2.1 Introduction 401\u003c\/p\u003e \u003cp\u003e16.2.2 Optimal Reliability Criterion Determination 403\u003c\/p\u003e \u003cp\u003e16.2.3 Optimal Composite Power System Expansion Planning 403\u003c\/p\u003e \u003cp\u003e16.2.3.1 The Objective Function 403\u003c\/p\u003e \u003cp\u003e16.2.3.2 Constraints 405\u003c\/p\u003e \u003cp\u003e16.2.4 Composite Power System Reliability Evaluation and Outage Cost Assessment 406\u003c\/p\u003e \u003cp\u003e16.2.4.1 Reliability Evaluation at HLI 406\u003c\/p\u003e \u003cp\u003e16.2.4.2 Reliability Evaluation at HLII (Composite Power System) 407\u003c\/p\u003e \u003cp\u003e16.2.4.3 Flow Chart of the Proposed Methodology for Optimal Reliability Criterion Determination in Transmission System Expansion Planning 409\u003c\/p\u003e \u003cp\u003e16.2.5 Case Study 410\u003c\/p\u003e \u003cp\u003e16.2.6 Conclusion 416\u003c\/p\u003e \u003cp\u003e16.3 Deterministic Reliability Criterion for Composite Power System Expansion Planning 416\u003c\/p\u003e \u003cp\u003e16.3.1 Introduction 416\u003c\/p\u003e \u003cp\u003e16.3.2 Optimal Reliability Criterion Determination 419\u003c\/p\u003e \u003cp\u003e16.3.3 Optimal Composite Power System Expansion Planning 419\u003c\/p\u003e \u003cp\u003e16.3.3.1 Composite Power System Expansion Planning Formulation in CmExpP.For 419\u003c\/p\u003e \u003cp\u003e16.3.3.2 Flow Chart 421\u003c\/p\u003e \u003cp\u003e16.3.4 Composite Power System Reliability Evaluation 421\u003c\/p\u003e \u003cp\u003e16.3.4.1 Reliability Indices at Load Points 422\u003c\/p\u003e \u003cp\u003e16.3.4.2 Reliability Indices of the Bulk System 423\u003c\/p\u003e \u003cp\u003e16.3.5 DMR Evaluation using Maximum Flow Method 424\u003c\/p\u003e \u003cp\u003e16.3.6 Flow Chart of Optimal Reliability Criterion Determination 424\u003c\/p\u003e \u003cp\u003e16.3.7 Case Study 425\u003c\/p\u003e \u003cp\u003e16.3.7.1 Basic Input Data 425\u003c\/p\u003e \u003cp\u003e16.3.7.2 Results of Construction Costs of Cases 428\u003c\/p\u003e \u003cp\u003e16.3.7.3 Reliability Evaluation 428\u003c\/p\u003e \u003cp\u003e16.3.8 Conclusion 431\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Probabilistic Reliability-Based Expansion Planning with Wind Turbine Generators 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 433\u003c\/p\u003e \u003cp\u003e17.2 The Multistate Operation Model of WTG 434\u003c\/p\u003e \u003cp\u003e17.2.1 WTG Power Output Model 434\u003c\/p\u003e \u003cp\u003e17.2.2 Wind Speed Model 435\u003c\/p\u003e \u003cp\u003e17.2.3 The Multistate Model of WTG using Normal ProbabilityDistribution Function 435\u003c\/p\u003e \u003cp\u003e17.3 Reliability Evaluation of a Composite Power System with WTG 438\u003c\/p\u003e \u003cp\u003e17.3.1 Reliability Indices at Load Buses 440\u003c\/p\u003e \u003cp\u003e17.3.2 System Reliability Indices 440\u003c\/p\u003e \u003cp\u003e17.4 Case Study 441\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 448\u003c\/p\u003e \u003cp\u003e17.A Appendix 448\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Probabilistic Reliability-Based HVDC Expansion Planning with Wind Turbine Generators 449\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 The Status of HVDC 449\u003c\/p\u003e \u003cp\u003e18.2 HVDC Technology for Energy Efficiency and Grid Reliability 451\u003c\/p\u003e \u003cp\u003e18.3 HVDC Impacts on Transmission System Reliabili ty 455\u003c\/p\u003e \u003cp\u003e18.4 Case Study 455\u003c\/p\u003e \u003cp\u003eReferences 465\u003c\/p\u003e \u003cp\u003eIndex 469\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJAESEOK CHOI, PHD,\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKWANG Y. LEE, PHD,\u003c\/b\u003e 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDiscover how modern techniques have shaped complex power system expansion planning with this one-stop resource from two experts in the field\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eProbabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems delivers\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eDivided 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eProbabilistic Power System Expansion Planning\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough discussion of generation expansion planning, including cost assessment, methodology and optimization, and probabilistic production cost\u003c\/li\u003e \u003cli\u003eAn exploration of transmission system expansion planning, including the branch and bound algorithm, probabilistic production cost simulation for TEP, and TEP with reliability constraints\u003c\/li\u003e \u003cli\u003eAn examination of fuzzy decision making applied to transmission system expansion planning\u003c\/li\u003e \u003cli\u003eA treatment of probabilistic reliability-based grid expansion planning of power systems including wind turbine generators\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for power and energy systems designers, planners, operators, consultants, practicing engineers, software developers, and researchers, \u003ci\u003eProbabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems\u003c\/i\u003e will also earn a place in the libraries of practicing engineers who regularly deal with optimization problems.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989857157349,"sku":"NP9781119684138","price":156.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119684138.jpg?v=1761785695","url":"https:\/\/k12savings.com\/es\/products\/probabilistic-power-system-expansion-planning-with-renewable-energy-resources-and-energy-storage-systems-isbn-9781119684138","provider":"K12savings","version":"1.0","type":"link"}