FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
This addresses the problem of grid flexibility for renewable energy integration, though it appears incremental as it builds on existing hierarchical and federated learning approaches.
The paper tackles the challenge of implementing energy markets in renewable energy grids by proposing FRESCO, a hierarchical reinforcement learning framework with federated training, which demonstrated cooperative optimization among greedy agents under changing conditions.
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.