Abstract: Using data from two large US wind interconnection studies and two grid-scale wind power plants, this paper provides evidence that mesoscale meteorological models under-predict the variability in wind speeds, but for large wind farms the power production data have more similar statistics. Specifically, the mesoscale models under-predict the high-frequency variability in wind speeds, as measured by the power spectral density and the probability of large changes in wind speeds. However, these differences only appear to translate into an under-prediction of power production variability when modeling small wind plants (less than 10 square miles in area), where the effect of geographic diversity is minimal. When modeling larger wind plants, the filtering of the power output due to geographic diversity roughly offsets the filtering effect of the mesoscale model on predicted wind speeds. The exception to this is that the simulated data consistently under-predict the probability of very large wind ramping events, such as a 50% change in power output over an hour. The results show some evidence that methods aimed at correcting the reduced variability may result in too much high-frequency variability. We conclude that while meteorological models are important for large-scale wind integration studies, caution is needed for analyses that could be sensitive to the probability of large ramping events and high-frequency variability.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).