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May 2019

Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model

May 8, 2019 @ 3:00 pm - 4:00 pm

Espen Flo Boedal (Norwegian University of Science and Technology)

32 – LIDS Lounge

Flexible resources such as batteries and demand-side management technologies are needed to handle future large shares of variable renewable power. Wind and solar power introduce more short-term uncertainty that have to be considered when making investment decisions as it significantly impacts the value of flexible resources. In this work we present a method for using duals from a rolling horizon operational model, with wind power uncertainty and market representations, to represent power system operation in an investment problem. The method…

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Generalization and Learning under Dobrushin’s Condition

May 1, 2019 @ 3:00 pm - 4:00 pm

Yuval Dagan (EECS)

32 – LIDS Lounge

Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data, there has been a growing literature on learning and generalization in settings where data is sampled from an ergodic process. This work has also developed complexity measures, which appropriately extend Rademacher complexity to bound the generalization error and learning rates of hypothesis classes in this setting. Rather than time-series data, our work is motivated by settings where…

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April 2019

Hierarchical Bayesian Network Model for Probabilistic Estimation of EV Battery Life

April 24, 2019 @ 3:00 pm - 4:00 pm

Mehdi Jafari (LIDS)

32 – LIDS Lounge

Bayesian models are applied to probabilistic analysis of phenomena which deal with multiple external stochastic factors and unmeasurable variables. Considering the large amount of available data for the EV driving, recharging and grid services such as solar charging which contains uncertainties and measurement errors, and their hierarchical effect on the battery life, this application of Bayesian models can be useful for the aging probabilistic analysis. Causality is of utmost importance for batteries as their aging is affected by a high…

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