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.
| Autorius: | Lei Yang, Vijay Vittal, Junshan Zhang, Miao He, |
| Serija: | SpringerBriefs in Electrical and Computer Engineering |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 88 |
| ISBN-10: | 3319123181 |
| ISBN-13: | 9783319123189 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Expert systems / knowledge-based systems |
Parašykite atsiliepimą apie „Spatio-Temporal Data Analytics for Wind Energy Integration“
