The Next Generation of Electric Power Unit Commitment Models

  • Benjamin F. Hobbs
  • Michael H. Rothkopf
  • Richard P. O’Neill
  • Hung-po Chao

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 36)

Table of contents

  1. Front Matter
    Pages i-vii
  2. The Evolving Context for Unit Commitment Decisions

    1. Benjamin F. Hobbs, William R. Stewart Jr., Robert E. Bixby, Michael H. Rothkopf, Richard P. O’Neill, Hung-po Chao
      Pages 1-14
    2. Richard P. O’Neill, Udi Helman, Paul M. Sotkiewicz, Michael H. Rothkopf, William R. Stewart Jr.
      Pages 15-37
    3. Atif Debs, Charles Hansen, Yu-Chi Wu
      Pages 39-52
  3. New Features in Unit Commitment Models

  4. AlgorithmicAdvances

  5. Decentralized Decision Making

    1. A. Baíllo, M. Ventosa, A. Ramos, M. Rivier, A. Canseco
      Pages 227-248
    2. Xiaohong Guan, Ernan Ni, Peter B. Luh, Yu-Chi Ho
      Pages 249-270
    3. Francisco D. Galiana, Alexis L. Motto, Antonio J. Conejo, Maurice Huneault
      Pages 271-292
    4. Jinghai Xu, Richard D. Christie
      Pages 293-313
  6. Back Matter
    Pages 317-319

About this book


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.


Electric Power Electricity Markets Optimization Methods Stochastic model development linear optimization optimization

Editors and affiliations

  • Benjamin F. Hobbs
    • 1
  • Michael H. Rothkopf
    • 2
  • Richard P. O’Neill
    • 3
  • Hung-po Chao
    • 4
  1. 1.The Johns Hopkins UniversityUSA
  2. 2.Rutgers UniversityUSA
  3. 3.Federal Energy Regulatory CommissionUSA
  4. 4.Electric Power Research InstituteUSA

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 2001
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-7923-7334-6
  • Online ISBN 978-0-306-47663-1
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site
Industry Sectors
Finance, Business & Banking