Title: Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events,
Abstract: 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.