Federated reinforcement learning-based system and method for cooperative energy optimization
Takac, Martin Cuadrado Avila, Nicolas Mauricio Gutierrez Guillen, Roberto Alejandro Horvath, Samuel
Takac, Martin
Cuadrado Avila, Nicolas Mauricio
Gutierrez Guillen, Roberto Alejandro
Horvath, Samuel
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Department
Machine Learning
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Patent
Date
2025
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English
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Abstract
A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.
Citation
M. Takáč, N. M. Cuadrado Ávila, R. A. Gutiérrez Guillén, and S. Horváth, “Federated reinforcement learning‑based system and method for cooperative energy optimization,” U.S. Patent Application US 2025/0272723 A1, filed Feb. 26, 2024, published Aug. 28, 2025
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US Patent
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Google Patent