Large-Scale Grid Optimization: the Workhorse of Future Grid Computations
Abstract Purpose of Review The computation methods for modeling, controlling, and
optimizing the transforming grid are evolving rapidly. We review and systemize knowledge …
optimizing the transforming grid are evolving rapidly. We review and systemize knowledge …
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning
Successful machine learning involves a complete pipeline of data, model, and downstream
applications. Instead of treating them separately, there has been a prominent increase of …
applications. Instead of treating them separately, there has been a prominent increase of …
[PDF][PDF] Bridging Deep Learning and Electric Power Systems
P Donti - 2022 - kilthub.cmu.edu
Climate change is one of the most pressing issues of our time, requiring the rapid
mobilization of many tools and approaches from across society. Machine learning has been …
mobilization of many tools and approaches from across society. Machine learning has been …
Robust Optimal Control of Electric Vehicles Charging for Stochastic and Differentially Private Demand
This paper presents a comprehensive stochastic optimization model that seamlessly
integrates aggregate electric vehicle (EV) charging demand response with power grid …
integrates aggregate electric vehicle (EV) charging demand response with power grid …