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Electrical Power Unit Commitment

Deterministic and Two-Stage Stochastic Programming Models and Algorithms

  • Book
  • © 2017

Overview

Authors:
  1. Yuping Huang

    1. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, USA

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  2. Panos M. Pardalos

    1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

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  3. Qipeng P. Zheng

    1. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, USA

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  • Is the first book since the early 2000s to focus on emerging trends in UC problems
  • Focuses on stochastic programming models and advanced techniques to handle large numbers of integer decision variables due to scenario propagation
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Energy (BRIEFSENERGY)

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Table of contents (3 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction

    • Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
    Pages 1-9

  3. Deterministic Unit Commitment Models and Algorithms

    • Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
    Pages 11-47

  4. Two-Stage Stochastic Programming Models and Algorithms

    • Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
    Pages 49-86

  5. Back Matter

    Pages 87-93
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Keywords

About this book

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

Reviews

“A short, carefully written and accessible text, despite the mathematical complexity, the reader is provided with a comprehensive view of the problem allowing to quickly reach know-how in the handling of various models and algorithms aiming to formulate and reach solutions for it. Obviously interesting for both academics and practitioners in energy production and planning domains.” ( Manuel Alberto M. Ferreira, Acta Scientiae et Intellectus, Vol. 4 (04), 2018)

Authors and Affiliations

  • Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, USA

    Yuping Huang, Qipeng P. Zheng

  • Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

    Panos M. Pardalos

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