System and method for a hierarchical multi-agent framework for transactive microgrids
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 multi-agent reinforcement learning framework for managing energy transactions in microgrids that includes three layers of agents, each pursuing different objectives. The first layer, including prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon emission impact while balancing the consumption and production of both renewable and conventional energy. The framework takes into account fluctuations in energy demand and supply due to household supplied energy from renewable energy sources and energy storage levels in household energy storage devices.
Citation
M. Takáč, N. M. Cuadrado Ávila, R. A. Gutiérrez Guillén, and S. Horváth, “System and method for a hierarchical multi‑agent framework for transactive microgrids,” U.S. Patent Application US 2025/0272621 A1
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US Patent
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Google Patent