Post-Disaster Microgrid Formation for Enhanced Distribution System Resilience

Mukesh Gautam, Michael Abdelmalak, Mohammed Ben-Idris, Eliza Hotchkiss

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations

Abstract

This paper proposes a deep reinforcement learning (DRL) based approach for post-disaster critical load restoration in active distribution systems to form microgrids through network reconfiguration to minimize critical load curtailments. Distribution networks are represented as graph networks, and optimal network configurations with microgrids are obtained by searching for the optimal spanning forest. The constraints to the research question being explored are the radial topology and power balance. Unlike existing analytical and population-based approaches, which necessitate the repetition of entire analyses and computation for each outage scenario to find the optimal spanning forest, the proposed approach, once properly trained, can quickly determine the optimal, or near-optimal, spanning forest even when outage scenarios change. When multiple lines fail in the system, the proposed approach forms microgrids with distributed energy resources in active distribution systems to reduce critical load curtailment. The proposed DRL-based model learns the action-value function using the REINFORCE algorithm, which is a model-free reinforcement learning technique based on stochastic policy gradients. A case study was conducted on a 33-node distribution test system, demonstrating the effectiveness of the proposed approach for post-disaster critical load restoration.

Original languageAmerican English
Number of pages6
DOIs
StatePublished - 2022
Event2022 Resilience Week, RWS 2022 - National Harbor, United States
Duration: 26 Sep 202229 Sep 2022

Conference

Conference2022 Resilience Week, RWS 2022
Country/TerritoryUnited States
CityNational Harbor
Period26/09/2229/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/CP-5R00-85275

Keywords

  • Active distribution systems
  • microgrid formation
  • network reconfiguration
  • reinforcement learning
  • resilience

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