Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence

Cover......Page 1
Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence......Page 4
Copyright - ISBN: 0387234705......Page 5
Contents......Page 7
List of Figures......Page 13
List of Tables......Page 19
Preface......Page 21
Contributing Authors......Page 24
1 Introduction......Page 32
2 Workshop Presentations......Page 34
3 Synopses of the Articles in this Compilation......Page 37
4 Conclusions......Page 39
1 Introduction......Page 41
2 Reactive Power......Page 42
3 Reactive Power in Operations......Page 43
4 A Fundamental Illustration......Page 47
5 Challenges in Voltage Control and Related Security......Page 52
6 Conclusions......Page 53
3 Identification of Weak Locations using Voltage Stability Margin Index......Page 55
1 Introduction......Page 56
2 Basic Mathematical Model......Page 57
3 Application of the New Method to Large Scale Power Systems......Page 60
4 Simulation Results......Page 61
5 Conclusions......Page 63
6 Future Work......Page 65
1 Introduction......Page 68
2 Identification of Saddle Node, Hopf Bifurcation, and Damping Margins......Page 72
2.1 Identification of critical eigenvalue......Page 74
2.2 Damping margin identification......Page 76
2.3 Example......Page 77
3.1 Boundary predictor......Page 80
3.2 Boundary corrector......Page 81
3.3 Computation result......Page 82
4.1 Optimal margin boundary: cost based security......Page 83
4.3 Impact on power system security......Page 84
5 Research Needs......Page 85
1 Introduction......Page 92
2 Transfer Capability......Page 97
3 Transaction-Dependent ATC......Page 98
4 System Modeling......Page 100
5 Identify Critical Contingencies for Static Security......Page 103
6 Estimating Load Margins to Nose Points......Page 109
7 Estimating Load Margins to Static Security Violations......Page 115
8 Identify Critical Contingencies for Dynamic Security......Page 116
9 Solution Algorithm......Page 118
10 Numerical Studies......Page 120
11 Conclusions......Page 126
6 Automating Operation of Large Electric Power Systems Over Broad Ranges of Supply/Demand and Equipment Status......Page 133
1 Introduction......Page 134
2 Electric Power Grids as Complex Network Systems......Page 136
2.1 Assumptions underlying today\'s operation of hierarchical systems......Page 137
2.3 The major challenge: monitoring and control outside monotone response system conditions......Page 139
3 Current Operating Practice: Problems and Open Questions......Page 140
3.1 Historic problems of operating under stress......Page 141
3.2 Some possible solutions and their shortcomings......Page 142
4 Multi-Layered Modeling, Estimation and Control Approach to Managing Electric Power Networks Over Broad Ranges of Operating Conditions......Page 143
4.1 Full non-linear dynamics of electric power systems......Page 144
4.2 Disturbance- and control-driven multi-layered models......Page 145
4.3 A large-scale quasi-stationary model......Page 146
4.4 Multi-layered system constraints......Page 147
5 Multi-Layered Estimation and Control......Page 148
5.1 Quasi-stationary state estimators......Page 151
5.2 Multi-layered control approach......Page 153
5.3 Automated short-term dispatch and unit commitment over broad ranges of conditions and equipment status......Page 156
5.4 Particular case: Today\'s hierarchical control......Page 157
6.1 Quasi-stationary state estimators......Page 158
7 Conclusions and Open Questions......Page 161
1 Introduction......Page 166
2 Exciter Control Design using Linear Matrix Inequalities......Page 168
3 Some Simulation Results......Page 172
4.1 Design of decentralized output control......Page 174
4.2 Coordinated design of power system stabilizers and robust feedback......Page 176
4.3 Control design with information exchange between subsystems......Page 180
5 Conclusions......Page 181
8 Instability Monitoring and Control of Power Systems......Page 186
2 Participation Factors......Page 187
2.1 Modal participation factors......Page 188
3 Precursor-Based Monitoring......Page 189
4 Case Studies......Page 193
4.1 Single-generator system with dynamic load......Page 194
4.2 Single generator connected to an infinite bus......Page 195
4.3 Three-generator nine-bus power system......Page 198
5 Conclusions and Suggested Future Research......Page 200
Appendix: Parameter Values for the Generators in Sections 4.1 and 4.2......Page 203
9 Dynamic Embedded Optimization and Shooting Methods for Power System Performance Assessment......Page 206
1 Introduction......Page 207
2.1 Hybrid systems......Page 208
2.2 Trajectory sensitivities......Page 210
3 Dynamic Embedded Optimization......Page 211
4 Shooting Methods......Page 213
4.1 Limit cycles......Page 215
4.2 Grazing phenomena......Page 219
6 Conclusions......Page 222
1 Introduction......Page 227
2 FACTS Devices and Conventional Control......Page 229
2.1 Static Compensators (STATCOM)......Page 230
2.2 Static Synchronous Series Compensator (SSSC)......Page 231
2.3 Unified Power Flow Controller (UPFC)......Page 234
3 Adaptive Neurocontrol of FACTS Devices......Page 238
3.2 Neurocontroller......Page 239
3.3 Desired response predictor......Page 240
3.4 Adaptive neurocontrol of a STATCOM based power system......Page 241
3.5 Adaptive neurocontrol of a UPFC based power system......Page 245
4 Optimal Neurocontrol with Adaptive Critic Designs......Page 252
4.1 Optimal DHP neurocontrol of a Static Synchronous Series Compensator (SSSC)......Page 253
5 Conclusions......Page 259
6 Future Research......Page 261
11 Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using Metaheuristics......Page 264
1 Introduction......Page 265
2.1 The placement problem......Page 266
2.3 The combined placement and tuning problem......Page 267
3 The Metaheuristcs Approach......Page 268
4 Optimal Protection Devices Placement in Distribution Networks......Page 269
4.1 Proposed approach......Page 270
4.2 Genetic algorithm formulation......Page 271
4.3 Computational results......Page 272
5 Coordinated Tuning of Power System Controls......Page 278
5.1 Problem formulation......Page 279
5.2 Robust tuning using GAs......Page 280
5.3 Test results......Page 282
5.4 Feedback signal selection......Page 283
5.5 Robust decentralized control......Page 284
5.6 Time simulation results......Page 286
6 Conclusions and Further Developments......Page 287
Appendix: Genetic Algorithms......Page 289
1 Introduction......Page 293
2 Important Factors for Forecasts......Page 295
3 Forecasting Methods......Page 296
3.1 Medium- and long-term load forecasting methods......Page 297
3.2 Short-term load forecasting methods......Page 301
4 Future Research Directions......Page 305
5 Conclusions......Page 306
13 Independent Component Analysis Techniques for Power System Load Estimation......Page 310
1 Introduction......Page 311
2 Independent Component Analysis......Page 312
2.2 ICA source assumptions......Page 313
2.3 Objective functions for the maximization of source independence......Page 314
2.4 FastICA source estimation algorithm......Page 315
3.1 Linear mixing models for load profile estimation......Page 316
3.2 Preprocessing of load profile data......Page 320
3.4 FastICA based load profile estimation algorithm......Page 322
4.1 Data generation......Page 323
4.2 Error measures......Page 324
4.3 Results for active load profile estimation......Page 325
4.4 Results for reactive load profile estimation......Page 328
4.5 Results for harmonic load profile estimation......Page 330
5 Conclusions......Page 334
6 Future Research......Page 336
D......Page 341
L......Page 342
P......Page 343
V......Page 344
Z......Page 345