Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events
  In this paper, an artificial intelligence based grid hardening model is
proposed with the objective of improving power grid resilience in response to
extreme weather events. At first, a machine learning model is proposed to
predict the component states (either operational or outage) in response to the
extreme event. Then, these predictions are fed into a hardening model, which
determines strategic locations for placement of distributed generation (DG)
units. In contrast to existing literature in hardening and resilience
enhancement, this paper co-optimizes grid economic and resilience objectives by
considering the intricate dependencies of the two. The numerical simulations on
the standard IEEE 118-bus test system illustrate the merits and applicability
of the proposed hardening model. The results indicate that the proposed
hardening model through decentralized and distributed local energy resources
can produce a more robust solution that can protect the system significantly
against multiple component outages due to an extreme event.
