4.7 Article

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

Journal

IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 38, Issue 2, Pages 1619-1631

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2022.3175932

Keywords

COVID-19; Power systems; Data models; Pandemics; Estimation; Resilience; Software development management; Extreme event; data-driven assessment; power system operation; resilience; electricity market; open-source

Ask authors/readers for more resources

Intervention policies against COVID-19 have disrupted the power system operation globally, leading to pattern changes. To understand the risks and impacts, an open-access data hub, an open-source toolbox, and evaluation methods were developed for analyzing the U.S. power systems during COVID-19. These resources are valuable for research, public policy, and education. The data hub harmonizes various data, while the toolbox includes reformulated methods and proposes new indices. Empirical studies provide insights and solutions, expanding the understanding of COVID-19's effects.
Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. For this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, public policy, and educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Typical methods are reformulated and standardized in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Here the fluctuation index and probabilistic baseline are proposed for the first time to consider data fluctuation and estimation uncertainty. Furthermore, we conduct three empirical studies on the U.S. power systems, and share new solutions and findings to address several issues of public concerns. This conveys a more complete picture of the COVID-19 impact and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available