activity
20132022
most citedAn Incentive-Based Online Optimization Framework for Distribution Grids

93 citations · 357 across the 26 of their papers we have counts for

collaborators

26 papers

math.OC2022

An Online Joint Optimization-Estimation Architecture for Distribution Networks

Yi Guo, Xinyang Zhou, Changhong Zhao +3

In this paper, we propose an optimal control-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimatio…

eess.SY2021★ 1 cited

Smoothed Least-Laxity-First Algorithm for EV Charging

Niangjun Chen, Christian Kurniawan, Yorie Nakahira +2

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem t…

cs.DC2021★ 8 cited

Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices

Khaled Alanezi, Xinyang Zhou, Lijun Chen +1

A key challenge in wide adoption of sophisticated context-aware applications is the requirement of continuous sensing and context computing. This paper presents Panorama, a middlew…

math.OC2020★ 16 cited

Economic Dispatch With Distributed Energy Resources: Co-Optimization of Transmission and Distribution Systems

Xinyang Zhou, Chin-Yao Chang, Andrey Bernstein +2

The increasing penetration of distributed energy resources (DERs) in the distribution networks has turned the conventionally passive load buses into active buses that can provide g…

cs.RO2020★ 9 cited

Cooperative Control of Mobile Robots with Stackelberg Learning

Joewie J. Koh, Guohui Ding, Christoffer Heckman +2

Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry…

cs.LG2020

A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem

Zhiyuan Liu, Huazheng Wang, Bo Waggoner +3

We investigate the sparse linear contextual bandit problem where the parameter is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the c…