The next generation of electric power unit commitment models
- 其他作者: Hobbs, B. F.
- 出版: New York : Kluwer Academic ©2002.
- 稽核項: 1 online resource (viii, 319 pages) :illustrations.
- 叢書名: International series in operations research & management science ;36
- 標題: TECHNOLOGY & ENGINEERING , TECHNOLOGY & ENGINEERING Power Resources -- Electrical. , Electric power consumption Forecasting -- Mathematical models -- Congresses. , PurchasingDecision makingMathematical models , Decision makingMathematical models , Power ResourcesElectrical. , Conference papers and proceedings. , Electric power production , ForecastingMathematical models , Electric power consumption , ForecastingMathematical models. , Electronic books. , Electric power Purchasing -- Decision making -- Mathematical models -- Congresses. , Electric power consumption Forecasting -- Mathematical models. , Electric power , Electric power production Decision making -- Mathematical models -- Congresses.
- ISBN: 0306476630 , 9780306476631
- ISBN: 0792373340 , 9780792373346
- 試查全文@TNUA:
- 附註: Contains papers presented at a workshop entitled, The next generation of unit commitment models, held September 27-28, 1999 at the Center for Discrete Mathematics and Theoretical Computer Science, DIMACS, Rutgers University. Includes bibliographical references and index.
- 摘要: 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.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=78571
- 系統號: 005320588
- 資料類型: 電子書
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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.
來源: Google Book
來源: Google Book
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