Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform
The increasing demand for flexibility in hydropower systems requires pumped storage power plants to change operating modes and compensate reactive power more frequently. In this work, we demonstrate the potential of applying reinforcement learning (RL) to control the blow-out process of a hydraulic...
Main Authors: | Carlotta Tubeuf, Felix Birkelbach, Anton Maly, René Hofmann |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-02-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/16/4/1796 |
Similar Items
-
Hydropower and Pumped Storage Hydropower Resource Review and Assessment for Alaska’s Railbelt Transmission System
by: Leif Bredeson, et al.
Published: (2023-07-01) -
Hydrodynamical and Hydrochemical Assessment of Pumped-Storage Hydropower (PSH) Using an Open Pit: The Case of Obourg Chalk Quarry in Belgium
by: Angélique Poulain, et al.
Published: (2021-05-01) -
Deep Reinforcement Learning for Intraday Multireservoir Hydropower Management
by: Rodrigo Castro-Freibott, et al.
Published: (2025-01-01) -
Underground Pumped Storage Hydropower Case Studies in Belgium: Perspectives and Challenges
by: Alessandro Morabito, et al.
Published: (2020-08-01) -
Model and Analysis of Integrating Wind and PV Power in Remote and Core Areas with Small Hydropower and Pumped Hydropower Storage
by: Xianxun Wang, et al.
Published: (2018-12-01)