FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
<|reference_start|>FRESCO: Federated Reinforcement Energy System for Cooperative Optimization: The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part in making the required flexibility to achieve the vision of the next-generation grid. This work presents FRESCO, a framework that aims to ease the implementation of energy markets using a hierarchical control architecture of reinforcement learning agents trained using federated learning. The core concept we are proving is that having greedy agents subject to changing conditions from a higher level agent creates a cooperative setup that will allow for fulfilling all the individual objectives. This paper presents a general overview of the framework, the current progress, and some insights we obtained from the recent results.<|reference_end|>
@article{cuadrado2024fresco:,
    title={FRESCO: Federated Reinforcement Energy System for Cooperative
  Optimization},
    author={Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin
  Tak'av{c}},
    journal={arXiv preprint arXiv:2403.18444},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18444},
    primaryClass={cs.LG}
}