Network Reconfiguration for Enhanced Operational Resilience Using Reinforcement Learning

Michael Abdelmalak, Mukesh Gautam, Sean Morash, Aaron Snyder, Eliza Hotchkiss, Mohammed Benidris

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations


This paper proposes a reinforcement learning-based approach for distribution network reconfiguration(DNR) to enhance the resilience of the electric power supply. Resilience enhancements usually require solving large-scale stochastic optimization problems that are computationally expensive and sometimes infeasible. The exceptional performance of reinforcement learning techniques has encouraged their adoption in various power system control studies, specifically resilience-based real-time applications. In this paper, a single agent framework is developed using an Actor-Critic algorithm (ACA) to determine statuses of tie-switches in a distribution feeder impacted by an extreme weather event. The proposed approach provides a fast-acting control algorithm that reconfigures the feeder topology to reduce or even avoid load shedding. The problem is formulated as a discrete Markov decision process in such a way that a system state captures the system topology and its operational characteristics. An action is made to open or close a specific set of tie-switches after which a reward is calculated to evaluate the practicality and advantage of that action. The iterative Markov process is used to train the proposed ACA under diverse failure scenarios and is demonstrated on the 33-node distribution feeder system. Results show the capability of the proposed ACA to determine proper switching action of tie-switches with accuracy exceeding 93%.

Original languageAmerican English
Number of pages6
StatePublished - 2022
Event5th International Conference on Smart Energy Systems and Technologies, SEST 2022 - Eindhoven, Netherlands
Duration: 5 Sep 20227 Sep 2022


Conference5th International Conference on Smart Energy Systems and Technologies, SEST 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/CP-5R00-84621


  • Actor critic
  • Markov decision process
  • network reconfiguration
  • reinforcement learning
  • resilience


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