What question did this study set out to answer?

Establish a comprehensive tutorial and methodological foundation for formulating distribution system operational challenges as Markov decision processes and solving them with deep reinforcement learning.

June 1, 2025Proceedings of the IEEE

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

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Authors

DGDaniel Glover GKGayathri Krishnamoorthy HRHongda Ren

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Overview

Tutorial and survey outlines standardized deep reinforcement learning formulations for electric distribution systems, highlighting pathways to improve operational reproducibility and control.

Key Points

Establish a comprehensive tutorial and methodological foundation for formulating distribution system operational challenges as Markov decision processes and solving them with deep reinforcement learning.
Synthesized sequential decision-making formulations across diverse active distribution grid operations into standardized Markov decision process frameworks.
Developed a modular environment design methodology accompanied by an open-source code repository to construct and benchmark reinforcement learning algorithms.
Identified critical domain-specific bottlenecks preventing reinforcement learning adoption, specifically high modeling overhead and poor experimental reproducibility.
Established standard translation templates for mapping complex grid device dynamics into structured state, action, and reward representations for robust algorithm training.

Cite This Study

Glover et al. (2025) studied this question.

synapsesocial.com/papers/6a0e9ce3a14f152feaf99a9e https://doi.org/10.1109/jproc.2025.3599840

View Full Paper

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June 1, 2025Proceedings of the IEEE

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

View Full Paper

Ask AI

Bookmark

Authors

DGDaniel Glover GKGayathri Krishnamoorthy HRHongda Ren

Discussion

Member takes

Overview

Tutorial and survey outlines standardized deep reinforcement learning formulations for electric distribution systems, highlighting pathways to improve operational reproducibility and control.

Key Points

Establish a comprehensive tutorial and methodological foundation for formulating distribution system operational challenges as Markov decision processes and solving them with deep reinforcement learning.
Synthesized sequential decision-making formulations across diverse active distribution grid operations into standardized Markov decision process frameworks.
Developed a modular environment design methodology accompanied by an open-source code repository to construct and benchmark reinforcement learning algorithms.
Identified critical domain-specific bottlenecks preventing reinforcement learning adoption, specifically high modeling overhead and poor experimental reproducibility.
Established standard translation templates for mapping complex grid device dynamics into structured state, action, and reward representations for robust algorithm training.

Cite This Study

Glover et al. (2025) studied this question.

synapsesocial.com/papers/6a0e9ce3a14f152feaf99a9e https://doi.org/10.1109/jproc.2025.3599840

View Full Paper

Ask AI

Bookmark