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.
Preface. Contributors. 1. Introduction; Y.H. Song. 2. Simulated annealing applications; K. Nara. 3. Tabu search application in fault section estimation and state identification of unobserved protective relays in power system; F. Wen, C.S. Chang. 4. Genetic algorithms for scheduling generation and maintenance in power systems; C.J. Aldridge, et al. 5. Transmission network planning using genetic algorithms; M.R. Irving, et al. 6. Artificial neural networks for generation scheduling; M.P. Walsh, M.J. O'Malley. 7. Decision making in a deregulated power environment based on fuzzy sets; S.M. Shahidehpour, M.I. Alomoush. 8. Lagrangian relaxation applications to electric power operations and planning problems; A.J. Conejo, et al. 9. Inter point methods and applications in power systems; K. Xie, Y.H. Song. 10. Ant colony search, advanced engineered-conditioning genetic algorithms and fuzzy logic controlled genetic algorithms: economic dispatch problems; Y.H. Song, et al. 11. Industrial applications of artificial intelligence techniques; A.O. Ekwue.