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.
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