Electricity Markets - Part 2 | Foundations for Energy Data Analytics
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 Published On Apr 6, 2022

Enjoy Part 1 of Electricity Markets? (   • Electricity Markets | Foundations for...  ) In Part 2 of this talk, given June 9, 2021, Dr. Luana Lima (@dukeuniversity) introduces the organizations that balance supply and demand for the U.S. electric grid and describes the critical role that energy data analytics can play as the power sector undergoes massive shifts. For one thing, data analytics can help with developing ways to address #uncertainty and #variability of supply of renewable energy resources. Dr. Lima offers examples from her own research on developing resource optimization and inflow #forecasting techniques for the use of #hydropower in Brazil.

0:00 – Overview of balancing authorities in the U.S.
2:28 – How vertically integrated utilities vs. RTOs/ISOs handle the balancing function
3:56 – Unit commitment problem
7:38 – Major shifts in power sector and how data analytics can help
11:12 – Examples from research on hydropower resource optimization and forecasting

Part of the Foundations for Energy Data Analytics Series. Some videos in the series introduce data science concepts and technique that are applicable to energy challenges. Others (like this one!) are designed to provide background on energy systems and policy, elucidating contexts that are rich with opportunities for data scientists. (   • Foundations for Energy Data Analytics  )

This talk was originally presented during a workshop of the Energy Data Analytics Ph.D. Student Fellows Program (https://energy.duke.edu/energy-data-a..., organized by the Energy Data Analytics Lab (https://energy.duke.edu/research/ener...) at Duke University. The Fellows Program is funded by a grant from the Alfred P. Sloan Foundation, Grant-G2020-13922. (https://sloan.org)

(Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff).

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