Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach-Reference-Cited by-同舟云学术

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Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach

Published:2024-03 Issue:2 Volume:30 Page:63-76
ISSN:1077-2618
Container-title:IEEE Industry Applications Magazine
language:
Short-container-title:IEEE Ind. Appl. Mag.

Author:

Gautam Mukesh¹^ORCID,Bhusal Narayan²^ORCID,Ben-Idris Mohammed³^ORCID

Affiliation:

1. Idaho National Laboratory, Idaho Falls, ID, USA

2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

3. Michigan State University, East Lansing, MI, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/2943/10417749/10304423-aam.pdf

Reference33 articles.

1. A Deep Reinforcement Learning-based Approach to Post-Disaster Routing of Movable Energy Resources

2. Cascading risks: Understanding the 2021 winter blackout in Texas

3. Pre-positioning of Movable Energy Resources for Distribution System Resilience Enhancement

4. Application of Mobile Energy Storage for Enhancing Power Grid Resilience: A Review

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