FRESCO: Federated Reinforcement Energy System for Cooperative Optimization

Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez Guillen, Martin Takáč

Keywords: microgrids, reinforcement learning, federated learning, energy markets

TL;DR: Hierarchical reinforcement learning architecture that enable energy markets using federated learning to protect privacy.

Abstract: 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 creating 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.