  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. With 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. Based on these,
we conduct three empirical studies on the U.S. power systems, and share new
solutions and unexpected findings to address the issues of public concerns.
This conveys a more complete picture of the pandemic's impacts, 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.
