Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events [article]

Rozhin Eskandarpour, Amin Khodaei, A. Paaso, N. M. Abdullah
<span title="2018-10-05">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1810.02866v1">arXiv:1810.02866v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b52x3bzgc5atzbk5a2od262m2y">fatcat:b52x3bzgc5atzbk5a2od262m2y</a> </span>
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