Over the years, the electric power industry has been using  optimization methods to help them solve the unit commitment problem.  The result has been savings of tens and perhaps hundreds of millions  of dollars in fuel costs. Things are changing, however. Optimization  technology is improving, and the industry is undergoing radical  restructuring. Consequently, the role of commitment models is  changing, and the value of the improved solutions that better  algorithms might yield is increasing. The dual purpose of this book is  to explore the technology and needs of the next generation of computer  models for aiding unit commitment decisions.  Because of the unit commitment problem's size and complexity and  because of the large economic benefits that could result from its  improved solution, considerable attention has been devoted to  algorithm development in the book. More systematic procedures based on  a variety of widely researched algorithms have been proposed and  tested. These techniques have included dynamic programming,  branch-and-bound mixed integer programming (MIP), linear and network  programming approaches, and Benders decomposition methods, among  others. Recently, metaheuristic methods have been tested, such as  genetic programming and simulated annealing, along with expert systems  and neural networks. Because electric markets are changing rapidly,  how UC models are solved and what purposes they serve need  reconsideration. Hence, the book brings together people who understand  the problem and people who know what improvements in algorithms are  really possible. The two-fold result in The Next Generation of  Electric Power Unit Commitment Models is an assessment of  industry needs and new formulations and computational approaches that  promise to make unit commitment models more responsive to those needs.

The Next Generation of Electric Power Unit Commitment Models (International Series in Operations Research & Management Science) PDF

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