Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events
This addresses grid hardening for utility operators, but it is incremental as it builds on existing literature with a co-optimization approach.
The paper tackles improving power grid resilience to extreme weather by co-optimizing economic and resilience objectives, resulting in a more robust solution that significantly protects against multiple component outages on the IEEE 118-bus test system.
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.