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See the table below for our open-source software offerings.

For the most current listing of NREL-developed open-source software, see U.S. Department of Energy Office of Scientific and Technical Information's DOE CODE database.

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Explore more than 250 copyrighted software tools available for licensing via the Department of Energy's Lab Partnering Service.

RLC4CLR: Reinforcement Learning Controller for Critical Load Restoration Problems

Mar 17, 2022, 18:00 PM

RLC4CLR uses a reinforcement learning controller to solve a critical load restoration problem, which improves grid resilience after a substation outage. RLC4CLR consists of two parts: (1) a reinforcement learning environment that encapsulates the problem to be solved and provides interfacing functions to follow the standard OpenAI Gym format and (2) a reinforcement learning training script that enables the agent to learn its control policy by interacting with the environment.

Title : RLC4CLR: Reinforcement Learning Controller for Critical Load Restoration Problems

Url : https://github.com/NREL/rlc4clr

Software Id : 72324

Categories :

Grid Modernization
Python
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