FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
arxiv(2024)
摘要
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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