Automated Decision-Making for Electric Power Network Recovery

📝 Original Paper Info

- Title: Decision Automation for Electric Power Network Recovery
- ArXiv ID: 1910.00699
- Date: 2019-10-22
- Authors: Yugandhar Sarkale, Saeed Nozhati, Edwin K. P. Chong, Bruce R. Ellingwood

📝 Abstract

Critical infrastructure systems such as electric power networks, water networks, and transportation systems play a major role in the welfare of any community. In the aftermath of disasters, their recovery is of paramount importance; orderly and efficient recovery involves the assignment of limited resources (a combination of human repair workers and machines) to repair damaged infrastructure components. The decision maker must also deal with uncertainty in the outcome of the resource-allocation actions during recovery. The manual assignment of resources seldom is optimal despite the expertise of the decision maker because of the large number of choices and uncertainties in consequences of sequential decisions. This combinatorial assignment problem under uncertainty is known to be \mbox{NP-hard}. We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices. Our framework is closed-loop, and naturally incorporates all the attractive features of such a decision-making system. In contrast to myopic approaches, which do not account for the future effects of the current choices, our methodology has an anticipatory learning component that effectively incorporates \emph{lookahead} into the solutions. To this end, we leverage the theory of regression analysis, Markov decision processes (MDPs), multi-armed bandits, and stochastic models of community damage from natural disasters to develop a method for near-optimal recovery of communities. Our method contributes to the general problem of MDPs with massive action spaces with application to recovery of communities affected by hazards.

💡 Summary & Analysis

This research paper presents a new decision-making technique to automate the recovery process of critical infrastructure systems, such as electric power networks, following disasters. The main issue addressed is that efficient resource allocation for repairing damaged components in these systems becomes complex due to numerous choices and uncertainties in sequential decisions, making it an NP-hard problem. To tackle this challenge, the authors propose a closed-loop decision framework incorporating predictive learning elements. This method leverages regression analysis, Markov Decision Processes (MDPs), multi-armed bandits, and stochastic models of community damage from natural disasters to develop near-optimal solutions for recovery. The paper highlights that their approach significantly contributes to solving MDPs with large action spaces, which is crucial for recovering communities affected by hazards.

📄 Full Paper Content (ArXiv Source)

📊 논문 시각자료 (Figures)

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