Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

Short summary

Researchers propose a Supervised Reinforcement Learning framework that pre-trains policies on demonstration data before fine-tuning with RL for managing distributed energy resources. The two-step approach (offline + online adaptation) outperforms traditional methods with cost efficiency even under low-quality training data. Addresses the challenge of power system decarbonization by handling DER uncertainty and complexity.

• •Combines supervised pre-training with RL fine-tuning for distributed energy resource coordination
• •Two-step fine-tuning: offline performance enhancement plus online real-world adaptation
• •Demonstrates superior cost efficiency versus benchmarks, even with poor-quality demonstration data

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