Electric Power System Applications of Optimization

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CRC Press, Dec 19, 2000 - Technology & Engineering - 494 pages
2 Reviews
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A 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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Very unhelpful book. I wonder how piracy is being monitored in America. This book has lots of plagiarise ideas. The author does not even know the what is in the book well. His graduate studnets plagiarise online resources to make up this book. Avoid this book at all cost, it is of no use!!!. I am saying so because I have taken the author's course in optimization in Howard U, DC and know very well that he does not write books but uses graduate students to plagiarise his books. 

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good book..

Contents

Introduction
1
Electric Power System Models
19
PowerFlow Computations
65
Constrained Optimization and Applications
119
Linear Programming and Applications
143
Interior Point Methods
197
Nonlinear Programming
229
Dynamic Programming
257
Illustrative Example of the Decomposition Technique
329
Conclusions
336
References
338
Optimal Power Flow
339
OPFFuel Cost Minimization
340
OPFActive Power Loss Minimization
344
OPFVAr Planning
349
OPFAdding Environmental Constraints
358

Characteristics of Dynamic Programming
260
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 Tk
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
Decomposition Method
325
Formulation of the Decomposition Problem
326
Algorithm of Decomposition Technique
328
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 ant in the Unit Commitment Problem
422
Unit Commitment of Thermal Units Using Dynamic Programming
425
Illustrative Problems
434
Problem Set
436
References
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
Copyright

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