FRESCO: Federated Reinforcement Energy System for Cooperative Optimization

Nicolas Mauricio Cuadrado; Roberto Alejandro Gutierrez; Martin Takáč

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arXiv · 2403.18444

FRESCO: Federated Reinforcement Energy System for Cooperative Optimization

Nicolas Mauricio Cuadrado·Roberto Alejandro Gutierrez·Martin Takáč

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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 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.

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Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin Takáč. 2024-03-27. FRESCO: Federated Reinforcement Energy System for Cooperative Optimization. https://arxiv.org/abs/2403.18444

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