Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform

Carlotta Tubeuf (), Felix Birkelbach, Anton Maly and René Hofmann
Additional contact information
Carlotta Tubeuf: Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria
Felix Birkelbach: Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria
Anton Maly: Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria
René Hofmann: Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/E302, 1060 Vienna, Austria

Energies, 2023, vol. 16, issue 4, 1-10

Abstract: 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 machine during pump start-up and when operating in synchronous condenser mode. Even though RL is a promising method that is currently getting much attention, safety concerns are stalling research on RL for the control of energy systems. Therefore, we present a concept that enables process control with RL through the use of a digital twin platform. This enables the safe and effective transfer of the algorithm’s learning strategy from a virtual test environment to the physical asset. The successful implementation of RL in a test environment is presented and an outlook on future research on the transfer to a model test rig is given.

Keywords: reinforcement learning; hydropower; digital twin; pumped storage; transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/4/1796/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/4/1796/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:4:p:1796-:d:1065335

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().