Spatio-Temporal Data Analytics for Wind Energy Integration

2014
ISBN: 978-3-319-12318-9
Verlag: Springer International Publishing

Buch, Englisch, 80 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1474 g

Reihe: SpringerBriefs in Electrical and Computer Engineering

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.

Zielgruppe

Research

Autoren/Hrsg.

Yang, Lei

Vittal, Vijay

He, Miao

Zhang, Junshan

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Energietechnik | Elektrotechnik Windkraftanlagen, Wasserkraftanlagen

Inhaltsverzeichnis

Introduction.- A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation.- Support Vector Machine Enhanced Markov Model for Short-Term Wind Power Forecast.- Stochastic Optimization based Economic Dispatch and Interruptible Load Management.- Conclusions and Future Works.