Restoring Critical Loads In Resilient Distribution Systems using A Curriculum Learned Controller

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations

Abstract

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2021
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: 26 Jul 202129 Jul 2021

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period26/07/2129/07/21

Bibliographical note

See NREL/CP-2C00-78351 for preprint

NREL Publication Number

  • NREL/CP-2C00-82309

Keywords

  • curriculum learning
  • grid resiliency
  • load restoration
  • microgrid
  • reinforcement learning

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