Ebook: Modern Optimisation Techniques in Power Systems

The electric power industry is currently undergoing an unprecedented reform. The deregulation of electricity supply industry has introduced new opportunity for competition to reduce the cost and cut the price. It is a tremendous challenge for utilities to maintain an economical and reliable supply of electricity in such an environment. Faced by an increasingly complicated existence, power utilities need efficient tools and aids to ensure that electrical energy of the desired quality can be provided at the lowest cost. The overall objective, both for short-term and long-term operations, is then to find the best compromise between the requirements of security and economy. That is, effective tools are urgently required to solve highly constrained optimisation problems. In recent years, several major modem optimisation techniques have been applied to power systems. A large number of papers and reports have been published. In this respect, it is timely to edit a book on this topic with an aim to report the state of the art development internationally in this area.

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Under an ever-increasingly competitive/deregulated environment, power utilities need efficient and effective tools to ensure that electrical energy of the desired quality can be provided at the lowest cost. These usually form highly constrained optimisation problems. ModernOptimisation Techniques in Power Systems is the first book to offer a comprehensive cover of major modern optimisation methods applied to power systems, including: simulated annealing, tabu search, genetic algorithms, neural networks, fuzzy programming, Lagrangian relaxation, interior point methods, ant colony search and hybrid techniques. Various applications and case studies are presented to demonstrate the potential and procedures of applying such techniques in solving complex power system optimisation problems.
Written by top international experts in this field, this book will be a useful reference for professional engineers and managers involved in the optimisation of power system operation. It will also be of interest to postgraduates and researchers.

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Under an ever-increasingly competitive/deregulated environment, power utilities need efficient and effective tools to ensure that electrical energy of the desired quality can be provided at the lowest cost. These usually form highly constrained optimisation problems. ModernOptimisation Techniques in Power Systems is the first book to offer a comprehensive cover of major modern optimisation methods applied to power systems, including: simulated annealing, tabu search, genetic algorithms, neural networks, fuzzy programming, Lagrangian relaxation, interior point methods, ant colony search and hybrid techniques. Various applications and case studies are presented to demonstrate the potential and procedures of applying such techniques in solving complex power system optimisation problems.
Written by top international experts in this field, this book will be a useful reference for professional engineers and managers involved in the optimisation of power system operation. It will also be of interest to postgraduates and researchers.
Content:
Front Matter....Pages i-xi
Introduction....Pages 1-13
Simulated Annealing Applications....Pages 15-38
Tabu Search Application in Fault Section Estimation and State Identification of Unobserved Protective Relays in Power System....Pages 39-61
Genetic Algorithms for Scheduling Generation and Maintenance in Power Systems....Pages 63-89
Transmission Network Planing Using Genetic Algorithms....Pages 91-111
Artificial Neural Networks for Generator Scheduling....Pages 113-139
Decision Making in a Deregulated Power Environment Based on Fuzzy Sets....Pages 141-171
Lagrangian Relaxation Applications to Electric Power Operations and Planning Problems....Pages 173-203
Interior Point Methods and Applications in Power Systems....Pages 205-245
Ant Colony Search, Advanced Engineered-Conditioning Genetic Algorithms and Fuzzy Logic Controlled Genetic Algorithms: Economic Dispatch Problems....Pages 247-260
Industrial Applications of Artificial Intelligence Techniques....Pages 261-275

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Under an ever-increasingly competitive/deregulated environment, power utilities need efficient and effective tools to ensure that electrical energy of the desired quality can be provided at the lowest cost. These usually form highly constrained optimisation problems. ModernOptimisation Techniques in Power Systems is the first book to offer a comprehensive cover of major modern optimisation methods applied to power systems, including: simulated annealing, tabu search, genetic algorithms, neural networks, fuzzy programming, Lagrangian relaxation, interior point methods, ant colony search and hybrid techniques. Various applications and case studies are presented to demonstrate the potential and procedures of applying such techniques in solving complex power system optimisation problems.
Written by top international experts in this field, this book will be a useful reference for professional engineers and managers involved in the optimisation of power system operation. It will also be of interest to postgraduates and researchers.
Content:
Front Matter....Pages i-xi
Introduction....Pages 1-13
Simulated Annealing Applications....Pages 15-38
Tabu Search Application in Fault Section Estimation and State Identification of Unobserved Protective Relays in Power System....Pages 39-61
Genetic Algorithms for Scheduling Generation and Maintenance in Power Systems....Pages 63-89
Transmission Network Planing Using Genetic Algorithms....Pages 91-111
Artificial Neural Networks for Generator Scheduling....Pages 113-139
Decision Making in a Deregulated Power Environment Based on Fuzzy Sets....Pages 141-171
Lagrangian Relaxation Applications to Electric Power Operations and Planning Problems....Pages 173-203
Interior Point Methods and Applications in Power Systems....Pages 205-245
Ant Colony Search, Advanced Engineered-Conditioning Genetic Algorithms and Fuzzy Logic Controlled Genetic Algorithms: Economic Dispatch Problems....Pages 247-260
Industrial Applications of Artificial Intelligence Techniques....Pages 261-275
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