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Spatio-Temporal Data Analytics for Wind Energy Integration

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  • © 2014

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Authors:
  1. Lei Yang

    1. Electrical Computer & Energy Engineering Ira A. Fulton School of Engineering, Arizona State University, Tempe, USA

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  2. Miao He

    1. Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, USA

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  3. Junshan Zhang

    1. Electrical Computer & Energy Engineering Ira A. Fulton School of Engineering, Arizona State University, Tempe, USA

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  4. Vijay Vittal

    1. Electrical Computer & Energy Engineering Ira A. Fulton School of Engineering, Arizona State University, Tempe, USA

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Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction

    • Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 1-6

  3. A Spatio-Temporal Analysis Approach for Short-Term Forecast ofWind Farm Generation

    • Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 7-34

  4. Support Vector Machine Enhanced Markov Model for Short TermWind Power Forecast

    • Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 35-57

  5. Stochastic Optimization Based Economic Dispatch and Interruptible Load Management

    • Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 59-75

  6. Conclusions and Future Works

    • Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 77-80
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Keywords

About this book

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful.

Authors and Affiliations

  • Electrical Computer & Energy Engineering Ira A. Fulton School of Engineering, Arizona State University, Tempe, USA

    Lei Yang, Junshan Zhang, Vijay Vittal

  • Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, USA

    Miao He

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