Handbook of Power Systems I (eBook)

Handbook of Power Systems I 3
Preface of Volume I 7
Contents of Volume I 11
Contents of Volume II 15
Contributors 17
Part I Operation Planning 21
Constructive Dual DP for Reservoir Optimization 22
1 Introduction 22
2 Background 23
3 A Deterministic Single-Reservoir CDDP Algorithm 26
4 Alternative Precomputations for Intra-period Optimization 31
5 Guideline Augmentation vs. Demand Curve Addition 32
6 Dealing with Uncertainty 36
7 Efficient Simulation Using CDDP Precomputations 40
8 Adding Reservoirs 41
9 Current Models: RAGE/DUBLIN and ECON BID 44
10 Conclusion 49
References 49
Long- and Medium-term Operations Planning and Stochastic Modelling in Hydro-dominated Power Systems Based on Stochastic Dual Dynamic Programming 52
1 Introduction 52
2 Basic Power System Model 54
2.1 Introduction 54
2.2 Power Station Model 54
2.3 Reservoir and Inflow 55
2.4 Power Balance and Objective Function 56
2.5 Price Modelling 58
2.6 Overall Local Model 60
3 Solution Method for the Local Model 60
3.1 Overview 60
3.2 Solution by a Dynamic Programming Approach 61
4 Extensions to the Local Model 64
4.1 Head Variations in Medium-term Scheduling 64
4.2 Risk Control 65
4.3 Use of the Results from the Local Model 66
5 A Numerical Example 66
6 A Global Scheduling Model 67
7 More on Stochastic Inflow Modelling 68
8 Computational Issues 71
9 Discussion 72
10 Conclusion 73
References 73
Dynamic Management of Hydropower-Irrigation Systems 75
1 Introduction 75
2 Stochastic Dual Dynamic Programming 77
3 SDDP Model with Irrigation Benefits 81
4 SDDP Model of the Euphrates River in Turkey and Syria 86
5 Analysis of Allocation Policies 88
6 Conclusion 91
References 92
Latest Improvements of EDF Mid-term Power Generation Management 94
1 Introduction 94
2 EDF Mid-term Power Generation Management 95
2.1 EDF Generation Units 95
2.2 Mid-term Power Generation Management Purposes 95
2.3 Mid-term Power Generation Management Tools 96
2.4 Mid-term Power Generation Management Toolsas an Approximated Dynamic Programming Method 97
3 The Simulator 97
3.1 The Former Simulator and Its Limitations 97
3.2 Horizon of Simulation 98
3.3 The Simulator Modeling 98
3.4 MIP Resolution 99
3.4.1 Modeling 99
3.4.2 Comparison Between Solvers 103
3.4.3 Some Results 103
3.5 Heuristics 103
3.5.1 First Heuristic H1 104
3.5.2 Second Heuristic H2 107
3.6 Calculation Duration Comparison 108
4 Conclusion 109
Further Reading 110
EDF Modelling System 110
Other References 110
Large Scale Integration of Wind Power Generation 112
1 Wind Intermittence 112
2 Impact in the Power System 114
3 Options for Managing Intermittency 116
3.1 Wind Power Forecasting 118
3.2 Aggregation and Distribution 120
3.3 Interconnection with Other Grids 124
3.4 Power Plants Providing Reserve 124
3.5 Curtailment of Wind Farms 125
3.6 Distributed Generation 125
3.7 Complementarity Between Renewable Sources 125
3.8 Demand-Side Management 128
3.9 Demand Response 129
3.10 Energy Storage 131
4 Solutions Adopted in the Existent Markets 133
5 Conclusion 135
References 135
Optimization Models in the Natural Gas Industry 137
1 Introduction 137
2 Optimization in Gas Production (Recovery) 139
2.1 Production Scheduling Considering Well Placement 139
2.1.1 Mixed Integer Linear Programming Formulation 139
2.1.2 Nonlinear Programming Formulation 141
2.2 Total Gas Recovery Maximization: An Optimal Control Formulation 142
3 Natural Gas Pipeline Network Optimization 144
3.1 Compressor Station Allocation Problem Considering Pipeline Configurations 145
3.2 Least Gas Purchase Problem and Optimal Dimensioning of Gas Pipelines 147
3.3 Minimum Fuel Consumption Problem 150
4 Natural Gas Market Models 152
4.1 Reallocation Problem in a Regulated Natural Gas Market 152
4.2 Deregulated Natural Gas Market Models 154
4.3 Optimization in the Energy System Combining Natural Gas System and Electricity System 157
4.3.1 Electricity System Reliability Study using Natural Gas Transmission Network Modeling 157
4.3.2 Optimization in Natural Gas Contracts 159
5 Conclusion 161
References 162
Integrated Electricity–Gas Operations Planning in Long-term Hydroscheduling Based on Stochastic Models 165
1 Introduction 166
2 Overview of Electricity and Gas Sectors 167
3 Electricity–Natural Gas Integration Issues 170
4 Probabilistic Evaluation of Gas-Fired Plant Schedules 170
4.1 Stochastic Hydrothermal Scheduling 171
4.1.1 Objective Function 172
4.1.2 Water Balance Equations 172
4.1.3 Bounds on Storage, Turbined Volumes, and Thermal Generation Variables 173
4.1.4 Load Balance Equation 173
4.2 Probabilistic Gas Scheduling Model 173
4.2.1 Gas Production and Flow Limits 174
4.2.2 Gas Balance Equations 174
4.2.3 Objective Function 175
4.3 Case Study 175
5 Integrated Electricity–Gas Modeling in HydroScheduling Models 178
5.1 Gas Pipeline Equations 178
5.2 Case Study 179
6 Conclusions 181
References 181
Recent Progress in Two-stage Mixed-integer Stochastic Programming with Applications to Power Production Planning 192
1 Introduction 192
2 Models and Structural Properties 193
3 Stability 197
4 Scenario Reduction 199
5 Decomposition Algorithms 203
5.1 Convexification of the Expected Recourse Function 207
5.2 Convexification of the Value Function 208
5.2.1 Solving the Master Problem 209
5.2.2 Convexification of Disjunctive Cuts 210
5.2.3 Approximation of (u, t) by Linear Optimality Cuts 210
5.2.4 Approximation of (u, t) by Lift-and-Project 211
5.2.5 Approximation of (u, t) by Branch-and-Bound 212
5.2.6 Full Algorithm 213
5.2.7 Extension to Multistage Problems 214
5.3 Scenario Decomposition 215
6 Application to Stochastic Thermal Unit Commitment 216
7 Conclusions 220
References 221
Dealing With Load and Generation Cost Uncertainties in Power System Operation Studies: A Fuzzy Approach 224
1 Introduction 224
2 Uncertainty Modeling in Power System Studies 226
3 Fuzzy Set Basics 228
4 Fuzzy Optimal Power Flow 229
5 New Fuzzy Optimal Power Flow Model 231
5.1 General Aspects 231
5.2 Integration of Load Uncertainties 233
5.3 Integration of Generation Cost Uncertainties 236
5.4 Simultaneous Integration of Cost and Load Uncertainties 236
5.5 Integration of Active Losses 237
5.6 Computation of Nodal Marginal Prices 238
5.7 Final Remarks 239
6 Case Study 240
6.1 Data 240
6.2 Results Considering Only Load Uncertainties 241
6.3 Results Considering Only Generation Cost Uncertainties 243
6.4 Results Considering Load and Generation Cost Uncertainties 244
7 Conclusions 246
References 247
OBDD-Based Load Shedding Algorithm for Power Systems 249
1 Introduction 249
2 Literature Review 250
3 Preliminary 251
3.1 Boolean Expressions and Their OBDDs 251
3.2 Signed Integers and Their OBDD Vectors 252
3.3 Key Constraints 255
4 The Load Shedding Problem(LSP) 255
5 Solution of LSP Based on OBDD 257
5.1 Boolean Expression for the Power Balance Constraint (PBC) 258
5.2 Boolean Expression for the Priority Constraint (PRC) 260
6 NP-Hardness of the Problem 260
7 Case Study 262
8 Conclusions 265
References 266
Solution to Short-term Unit Commitment Problem 268
1 Introduction 270
2 Generating Units 272
2.1 Thermal Units 272
2.1.1 Steam Turbine Unit 272
2.1.2 Gas Turbine Unit 273
2.2 Hydro Units 274
3 Operating Constraints 274
3.1 System Constraints 274
3.2 Unit Constraints 274
4 Objective Function and Constraints of Unit Commitment Problem 275
5 Lagrangian Relaxation Approach 277
5.1 Lagrangian Dual Problem 277
5.2 Solution of the Dual Problem 279
5.3 Solving Thermal Subproblems 280
5.3.1 Steam Turbine Unit Without Ramp Rate Constraints 280
5.3.2 Steam Turbine Unit with Ramp Rate Constraints 283
5.3.3 Gas Turbine Unit 286
5.4 Solving Hydro Subproblems 287
6 Solution Methodology 287
6.1 Variable Metric Method for Dual Optimization 287
6.2 Linear Interpolation Method for Suboptimal Feasible Solution 289
6.3 Development of the Refinement Algorithm 290
6.4 Unit Commitment Expert System 292
7 Hydrothermal Scheduling 293
8 Numerical Results 298
9 Conclusions 304
References 305
A Systems Approach for the Optimal Retrofitting of Utility Networks Under Demand and Market Uncertainties 306
1 Introduction 306
2 Problem Description 308
3 The Stochastic Programming Based Approach 309
3.1 The Stochastic Programming Framework 309
3.2 Realisation of the Proposed Approach 310
4 Case Study 312
4.1 The Targeted Utility System 312
4.2 The Retrofit Problem Specification and Solution Choices 313
5 Results and Discussion 314
5.1 Optimal Retrofit Design Resulting from the Proposed Approach 315
5.2 Comparison with Other Designs 315
6 Conclusions and Future Work 317
References 318
Co-Optimization of Energy and Ancillary Service Markets 320
1 Introduction 320
2 Basic Concepts 322
3 Formulation 325
3.1 Energy Market Formulation 325
3.2 Defining Requirements 326
3.3 Defining Participant Offers 327
3.4 Defining Performance Limits18 328
4 Economic Interpretation 332
5 Multi-zone Formulations 335
6 Multi-period Formulations 337
7 Conclusions 339
References 339
Part II Expansion Planning 341
Investment Decisions Under Uncertainty Using Stochastic Dynamic Programming: A Case Study of Wind Power 342
1 Introduction 342
2 Real Options On Wind Power 344
3 Uncertainty 347
3.0.1 Market Prices 347
3.0.2 Investment Costs and Subsidies 348
4 Value of Wind Power Investments 349
5 Conclusion 350
References 351
The Integration of Social Concerns into Electricity Power Planning: A Combined Delphi and AHP Approach 353
1 Introduction 353
2 Energy and Sustainable Development 354
2.1 Sustainable Energy Planning 356
2.2 Importance of the Social Dimension 357
3 Methodology 358
3.1 Suitability of the AHP Approach 359
3.2 Suitability of the Delphi Approach 360
4 Implementation of the Proposed Methodology 361
4.1 Selection of Options (Electricity Generation Technologies) 361
4.2 Selection of Criteria 362
4.3 Hierarchical Structure Formulation 364
4.4 Delphi Implementation 366
4.5 Determination of Weights for the Electricity Generation Options 366
4.6 Social Impact of Future Electricity Generation Scenarios 369
5 Conclusions 371
References 372
Transmission Network Expansion Planning Under Deliberate Outages 375
1 Introduction 375
2 Traditional Transmission Network Expansion Planning 376
3 Vulnerability-Constrained Transmission Expansion Planning 378
4 Decision Framework, Uncertainty Characterization, and Risk Modeling 379
4.1 Decision-Making Process 379
4.2 Scenario Generation Procedure 381
4.3 Risk Modeling 383
5 Formulation 384
5.1 Risk-Neutral Approach 384
5.2 Risk-Averse Approach 386
5.3 Computational Issues 388
6 Numerical Results 389
6.1 Risk-Neutral Analysis 390
6.2 Risk-based Analysis 391
7 Conclusions 393
References 397
Long-term and Expansion Planning for Electrical Networks Considering Uncertainties 400
1 Introduction 400
2 Planning of Transmission and Distribution Networks 402
2.1 Uncertainties of Network Planning 402
2.1.1 Technical Uncertainties 403
2.1.2 Economical Uncertainties 403
2.1.3 Regulatory Uncertainties 404
2.2 Technical Boundary Conditions of Network Planning 404
2.3 Long-term Planning 406
2.4 Expansion Planning 407
3 Algorithms for Long-term and Expansion Planning 409
3.1 Genetic Algorithms 410
3.2 Ant Colony Optimization 412
3.3 Comparison of Algorithms 414
4 Practical Application of Network Optimization Algorithms 415
5 Conclusion 416
References 416
Differential Evolution Solution to Transmission Expansion Planning Problem 418
1 Introduction 418
2 Problem Formulation 420
2.1 Overall TEP Problem 420
2.2 Reference Network Subproblem 422
3 Solution of Reference Network Subproblem 423
4 Simple Differential Evolution 424
4.1 Initialization 424
4.2 Mutation 424
4.3 Crossover 425
4.4 Selection 425
5 Improved Differential Evolution 425
5.1 Scaling Factor F 425
5.2 Selection Scheme 426
5.3 Auxiliary Set 426
5.4 Treatment of Constraints 427
5.5 Handling of Integer Variables 427
6 Overview of the IDE Solution to TEP Problem 427
7 Results and Discussion 428
7.1 Parameter Values for IDE 428
7.2 Comparison of TEP Methods 428
7.2.1 Case 30 428
7.2.2 Case 57 and Case 118 432
8 Conclusions 432
References 434
Agent-based Global Energy Management Systems for the Process Industry 437
1 Introduction 437
2 Conceptual Representation of a Dynamic Management System 439
2.1 Utility Services Negotiation 439
2.2 Utility System Optimisation 440
3 Mathematical Formulations, Optimisation Models, and Integration 441
3.1 Level I: Tactical Level 442
3.1.1 Problem Statement 442
3.1.2 Problem Representation 442
3.1.3 Mathematical Formulation 445
3.2 Level II: Strategic Level 447
3.2.1 Superstructure Development 447
3.2.2 Optimisation Model 449
4 An Agent-enabled Realisation 450
5 Illustrative Examples 453
5.1 Case Study One 454
5.2 Case Study Two 455
6 Conclusions 457
References 457
Optimal Planning of Distributed Generation via Nonlinear Optimization and Genetic Algorithms 459
1 Introduction 459
2 Distributed Generation 461
3 DG Location and Sizing Issues 462
4 Problem Formulation 464
4.1 The Objective Function 465
4.2 Operational Constraints 466
5 Nonlinear Optimization Approach 467
6 Genetic Algorithms Approach 470
7 Case Study 474
7.1 Nonlinear Optimization Algorithm 474
7.2 Genetic Algorithm 475
7.2.1 Selection Mechanism 476
7.2.2 Population Size 477
7.2.3 Crossover Fraction 477
7.3 Solution Analysis 481
8 Conclusions 486
References 488
Index 491