Tracking and Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector

1 code implementation • 11 May 2020 • Guangchun Ruan, Dongqi Wu, Xiangtian Zheng, S. Sivaranjani, Le Xie, Haiwang Zhong, Chongqing Kang

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U. S. becoming the epicenter of COVID-19 cases and deaths in late March.

Computers and Society

A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

1 code implementation • 12 Oct 2021 • Xiangtian Zheng, Nan Xu, Loc Trinh, Dongqi Wu, Tong Huang, S. Sivaranjani, Yan Liu, Le Xie

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.

Time Series Time Series Analysis

OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation

1 code implementation • 6 Mar 2022 • Rayan El Helou, Kiyeob Lee, Dongqi Wu, Le Xie, Srinivas Shakkottai, Vijay Subramanian

This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.

Decision Making

How Much Demand Flexibility Could Have Spared Texas from the 2021 Outage?

1 code implementation • 1 Jun 2022 • Dongqi Wu, Xiangtian Zheng, Ali Menati, Lane Smith, Bainan Xia, Yixing Xu, Chanan Singh, Le Xie

The February 2021 Texas winter power outage has led to hundreds of deaths and billions of dollars in economic losses, largely due to the generation failure and record-breaking electric demand.

Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids

no code implementations • 5 Mar 2020 • Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Le Xie

This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs).

reinforcement-learning Reinforcement Learning (RL)

PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems

no code implementations • 13 Sep 2021 • Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Le Xie

This paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids.

BIG-bench Machine Learning Decision Making