PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [SWR-22-07]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
NREL's PowerGridworld provides a modular simulation environment for training heterogenous, grid-aware, multi-agent reinforcement learning (RL) policies at scale. The package enables the user to create component gym environments that can be composed into more complex agents. For example, a grid interactive building environment can be created by composing together component environments each encapsulating the building, PV, and battery physics. These multi-component environments can then be combined into multi-agent simulation where each agent's power consumption/injection becomes an input for solving the optimal power flow on a distribution feeder modeled in OpenDSS. Information from OpenDSS, such as bus voltages and line flows, can be included in the agents' observation spaces to enable grid-aware rewards. The default API for the PowerGridworld simulator conforms to RLLib's MultiAgent API and thus enables distributed training using HPC and cloud resources.
- Site Accession Number:
- SWR-22-07
- Software Type:
- Scientific
- License(s):
- BSD 3-clause "New" or "Revised" License
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308
- DOE Contract Number:
- AC36-08GO28308
- Code ID:
- 66827
- OSTI ID:
- code-66827
- Country of Origin:
- United States
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