Flood-aware Optimal Power Flow for Proactive Day-ahead Transmission Substation Hardening
Authors: Mohadese Movahednia, Amin Kargarian
Abstract: Power system components, particularly electrical substations, may be severely damaged due to flooding, resulting in prolonged power outages and resilience degradation. This problem is more severe in low-elevated regions such as Louisiana. Protective operational actions such as placing tiger dams around substations before flooding can reduce substation vulnerability, damage costs, and energy not supplied cost, thus enhancing power grid resilience. This paper proposes a stochastic mixed-integer programming model for protecting transmission substations one day before flood events using tiger dams. A flood-aware optimal power flow problem is formulated for transmission system operators with respect to the protected/unprotected status of substations. The formulated model aims to identify critical substations whose protection maximizes grid resilience. The output of the proposed model is a day-ahead crew team schedule for installing tiger dams on transmission substations. The effectiveness of the proposed model is evaluated on a 6-bus system, and promising results are obtained.
Tools for this paper
Ask questions about this paper to our AI assistant
You can also chat with multiple papers at once here.
⚠The license of the paper does not allow us to build upon its content and the AI assistant only knows about the paper metadata rather than the full article.
The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.
⚠The license of this specific paper does not allow us to build upon its content and the summarizing tools will be run using the paper metadata rather than the full article. However, it still does a good job, and you can also try our tools on papers with more open licenses.
Look for similar papers (in beta version)
We compare the title and abstract with papers indexed in our database. For arXiv papers, we search within the same category; for DOI and other sources, we search across sources and subjects. You can rerun the search as our database grows.
Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.