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Electrical Power Unit Commitment

Deterministic and Two-Stage Stochastic Programming Models and Algorithms

  • Yuping Huang
  • Panos M. Pardalos
  • Qipeng P. Zheng

Part of the SpringerBriefs in Energy book series (BRIEFSENERGY)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
    Pages 1-9
  3. Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
    Pages 11-47
  4. Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
    Pages 49-86
  5. Back Matter
    Pages 87-93

About this book

Introduction

This volume in the SpringerBriefs in Energy series offers a systematic review of unit commitment (UC) problems in electrical power generation. It updates texts written in the late 1990s and early 2000s by including the fundamentals of both UC and state-of-the-art modeling as well as solution algorithms and highlighting stochastic models and mixed-integer programming techniques.

The UC problems are mostly formulated as mixed-integer linear programs, although there are many variants. A number of algorithms have been developed for, or applied to, UC problems, including dynamic programming, Lagrangian relaxation, general mixed-integer programming algorithms, and Benders decomposition. In addition the book discusses the recent trends in solving UC problems, especially stochastic programming models, and advanced techniques to handle large numbers of integer- decision variables due to scenario propagation

Keywords

Electrical Power Unit Commitment Mixed Integer Programming Network-based UC Models Stochastic UC Models UC Models Unit Commitment Models Electrical Power Generation

Authors and affiliations

  • Yuping Huang
    • 1
  • Panos M. Pardalos
    • 2
  • Qipeng P. Zheng
    • 3
  1. 1.Department of Industrial Engineering and Management SystemsUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Department of Industrial Engineering and Management SystemsUniversity of Central FloridaOrlandoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-6768-1
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Boston, MA
  • eBook Packages Energy
  • Print ISBN 978-1-4939-6766-7
  • Online ISBN 978-1-4939-6768-1
  • Series Print ISSN 2191-5520
  • Series Online ISSN 2191-5539
  • Buy this book on publisher's site