  Electrical energy is a vital part of modern life, and expectations for grid
resilience to allow a continuous and reliable energy supply has tremendously
increased even during adverse events (e.g., Ukraine cyber-attack, Hurricane
Maria). The global pandemic COVID-19 has raised the electric energy reliability
risk due to potential workforce disruptions, supply chain interruptions, and
increased possible cybersecurity threats. The pandemic introduces a significant
degree of uncertainly to the grid operation in the presence of other extreme
events like natural disasters, unprecedented outages, aging power grids, high
proliferation of distributed generation, and cyber-attacks. This situation
increases the need for measures for the resiliency of power grids to mitigate
the impacts of the pandemic as well as simultaneous extreme events. Solutions
to manage such an adverse scenario will be multi-fold: a) emergency planning
and organizational support, b) following safety protocol, c) utilizing enhanced
automation and sensing for situational awareness, and d) integration of
advanced technologies and data points for ML-driven enhanced decision support.
Enhanced digitalization and automation resulted in better network visibility at
various levels, including generation, transmission, and distribution. These
data or information can be utilized to take advantage of advanced machine
learning techniques for automation and increased power grid resilience. In this
paper, a) we review the impact of COVID-19 on power grid operations and actions
taken by operators/organizations to minimize the impact of COVID-19, and b) we
have presented the recently developed tool and concepts using natural language
processing (NLP) in the domain of machine learning and artificial intelligence
that can be used for increasing resiliency of power systems in normal and in
extreme scenarios such as COVID-19 pandemics.
