Electric Power System Applications of OptimizationA study of electric power system applications of optimization. It highlights essential trends in optimizational and genetic algorithms; linear programming; interior point methods of linear, quadratic, and non-linear systems; decomposition and Lagrange relaxation methods; unit commitment; optimal power flow; Var planning; and hands-on applications. |
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... Techniques Applicable to Power Systems References 2. Electric Power System Models I. Introduction II . Complex Power Concepts III . Three - Phase Systems 1 13592 14 17 41 IV . Per Unit Representation V. Synchronous Machine Modeling VI ...
... Techniques Applicable to Power Systems References 2. Electric Power System Models I. Introduction II . Complex Power Concepts III . Three - Phase Systems 1 13592 14 17 41 IV . Per Unit Representation V. Synchronous Machine Modeling VI ...
Contents
Introduction | 1 |
Electric Power System Models | 19 |
Reactive Capability Limits | 32 |
20 | 57 |
PowerFlow Computations | 65 |
30 | 77 |
Constrained Optimization and Applications | 80 |
65 | 89 |
Decomposition Method | 325 |
Formulation of the Decomposition Problem | 326 |
Algorithm of Decomposition Technique | 328 |
Illustrative Example of the Decomposition Technique | 329 |
Conclusions | 336 |
References | 338 |
Optimal Power Flow | 339 |
OPFFuel Cost Minimization | 340 |
66 | 95 |
Power Systems Application Examples | 123 |
VII | 133 |
VIII | 139 |
Interior Point Methods | 197 |
Nonlinear Programming | 229 |
Dynamic Programming | 257 |
Characteristics of Dynamic Programming | 260 |
Concept of Suboptimization and the Principle of Optimality | 261 |
Formulation of Dynamic Programming | 263 |
Backward and Forward Recursion | 268 |
Computational Procedure in Dynamic Programming | 278 |
Computational Economy in DP | 279 |
Conversion of a Final Value Problem into an Initial Value Problem | 282 |
Conclusions | 287 |
Problem Set | 288 |
References | 291 |
Lagrangian Relaxation | 293 |
Concepts | 294 |
The Subgradient Method for Setting the Dual Variables | 295 |
Setting T | 302 |
Comparison with Linear ProgrammingBased Bounds | 307 |
An Improved Relaxation | 309 |
Summary of Concepts | 310 |
Past Applications | 311 |
Summary | 313 |
Illustrative Examples | 320 |
Conclusions | 321 |
Problem Set | 322 |
References | 323 |
OPFActive Power Loss Minimization | 344 |
OPFVAr Planning | 349 |
OPFAdding Environmental Constraints | 358 |
Commonly Used Optimization Technique LP | 360 |
Commonly Used Optimization Technique NLP | 373 |
Illustrative Examples | 387 |
Conclusions | 394 |
Problem Set | 395 |
References | 397 |
Unit Commitment | 401 |
Formulation of Unit Commitment | 403 |
Optimization Methods | 406 |
Illustrative Example | 410 |
Updating At in the Unit Commitment Problem | 422 |
Unit Commitment of Thermal Units Using Dynamic Programming | 425 |
Illustrative Problems | 434 |
Problem Set | 436 |
441 | |
Genetic Algorithms | 443 |
Definition and Concepts Used in Genetic Computation | 444 |
Genetic Algorithm Approach | 446 |
Theory of Genetic Algorithms | 449 |
The Schemata Theorem | 452 |
General Algorithm of Genetic Algorithms | 454 |
Application of Genetic Algorithms | 455 |
Application to Power Systems | 457 |
Illustrative Examples | 469 |
Epilog | 473 |
Common terms and phrases
active additional algorithm angle application approach assume basic bound bus voltages buses calculated called Consider constant constraints continuous cost decision defined depends determine dual dynamic programming electric elements equal equation example existing expressed feasible solution Figure formulation frequency genetic given hour incremental initial integer involves iteration Lagrangian limits linear programming load loss magnitude matrix Maximize maximum Minimize multipliers node nonlinear objective function obtained operating optimal optimal solution optimum phase planning population positive power flow power system presented problem procedure quadratic reactive power referred relaxation represent reserve respectively result satisfy schedule selected shown simplex method slack solution solve specified stage Step studies Subject subproblems TABLE techniques tion transformer transmission unit unit commitment usually variables vector voltage x₁