A computational approach for real-time stochastic recovery of electric power networks during a disaster

Alireza Inanlouganji, Giulia Pedrielli, T. Agami Reddy and Fernando Tormos Aponte

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 163, issue C

Abstract: Disasters are occurring with increasing frequency worldwide, causing significant social hardship and economic losses. Critical infrastructures such as electric power networks are prone to failure under such events, and this significantly impacts the daily lives of people in affected areas. It is hence critical that the restoration planning of these power networks be done proactively. Disaster response in power networks is a well-studied problem, especially for pre-and post-event restoration Similar to pre-event, we consider uncertainty associated with the failure paths, and we look into real-time response while failures are happening. In this regard, at each time step, we move repair teams towards distribution loads based on their current state, their likelihood to fail, and the impact of the damage in case of node failure. We consider large-scale networks (>50 nodes and >20 repair teams) and propose an efficient algorithm to support real-time recovery.

Keywords: Disaster response; Power restoration; Reinforcement learning; Real-time decision making (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.tre.2022.102752

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