Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

Researchers propose a Supervised Reinforcement Learning framework for coordinating distributed energy resources, pre-training policies on demonstration data before fine-tuning with reinforcement learning. The method outperforms benchmarks in cost efficiency even with low-quality data, addressing challenges in power system decarbonization.

arXiv:2606.24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources DERs is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity. As traditional optimization methods struggle with such uncertainty and complexity of DERs, reinforcement learning RL has emerged as a promising alternative for DER management. However, standard RL methods suffer from sample inefficiency and sub-optimality when trained from scratch. Inspired by the training paradigms in large language models, this paper proposes a Supervised Reinforcement Learning SRL framework for learning DER coordination policies. This framework first pre-trains a policy on demonstration data in a supervised-learning fashion, which is then further fine-tuned using RL. Furthermore, we propose a two-step fine-tuning process: offline fine-tuning for enhancing policy performance and online fine-tuning for adapting it to the real-world dynamics. Experiments demonstrate that RL implementations based on the proposed framework significantly outperform all benchmarks, achieving high cost efficiency even under low-quality demonstration data.