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20162021
most citedNetwork-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

46 citations · 58 across the 12 of their papers we have counts for

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Showing 2019Show all

10 papers · 1 filter

math.OC2019

Decentralized Low-Rank State Estimation for Power Distribution Systems

April Sagan, Yajing Liu, Andrey Bernstein

This paper considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix com…

eess.SY2019

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

Jonatan Ostrometzky, Konstantin Berestizshevsky, Andrey Bernstein +1

The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution…

math.OC20192 cited

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

Yue Chen, Andrey Bernstein, Adithya Devraj +1

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit…

math.OC2019

Matrix Completion Using Alternating Minimization for Distribution System State Estimation

Yajing Liu, April Sagan, Andrey Bernstein +3

This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method whi…

eess.SY2019

Aggregating Privacy-Conscious Distributed Energy Resources for Grid Service Provision

Jun-Xing Chin, Andrey Bernstein, Gabriela Hug

The increasing adoption of advanced metering infrastructure has led to growing concerns regarding privacy risks stemming from the high resolution measurements. This has given rise…

math.OC2019

Towards robustness guarantees for feedback-based optimization

Marcello Colombino, John W. Simpson-Porco, Andrey Bernstein

Feedback-based online optimization algorithms have gained traction in recent years because of their simple implementation, their ability to reject disturbances in real time, and th…