Improving the Computational Efficiency of the Unit Commitment Problem in Hydrothermal Systems by Using Multi-Agent Deep Reinforcement Learning

Improving the Computational Efficiency of the Unit Commitment Problem in Hydrothermal Systems by Using Multi-Agent Deep Reinforcement Learning

In power systems with a significant hydroelectric component, instances of the Unit Commitment (UC) problem may be much more computationally intensive due to the longer decision horizons and the additional hydro constraints. Therefore, this paper presents a methodology to reduce the solution space to...

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Bibliographic Details
Main Authors: Philip Guerra, Esteban Gil, Victor H. Hinojosa
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Artificial neural networks
multi-agent deep reinforcement learning
unit commitment
variable reduction
Online Access:https://ieeexplore.ieee.org/document/10486892/

Internet

https://ieeexplore.ieee.org/document/10486892/

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