Cascade Hydropower Plant Operational Dispatch Control Using Deep Reinforcement Learning on a Digital Twin Environment

Erik Rot Weiss (), Robert Gselman, Rudi Polner and Riko Šafarič
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Erik Rot Weiss: HSE Invest, d.o.o., Obrežna Ulica 170, SI-2000 Maribor, Slovenia
Robert Gselman: HSE Invest, d.o.o., Obrežna Ulica 170, SI-2000 Maribor, Slovenia
Rudi Polner: HSE Invest, d.o.o., Obrežna Ulica 170, SI-2000 Maribor, Slovenia
Riko Šafarič: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, Slovenia

Energies, 2025, vol. 18, issue 17, 1-30

Abstract: In this work, we propose the use of a reinforcement learning (RL) agent for the control of a cascade hydropower plant system. Generally, this job is handled by power plant dispatchers who manually adjust power plant electricity production to meet the changing demand set by energy traders. This work explores the more fundamental problem with the cascade hydropower plant operation of flow control for power production in a highly nonlinear setting on a data-based digital twin. Using deep deterministic policy gradient (DDPG), twin delayed DDPG (TD3), soft actor-critic (SAC), and proximal policy optimization (PPO) algorithms, we can generalize the characteristics of the system and determine the human dispatcher level of control of the entire system of eight hydropower plants on the river Drava in Slovenia. The creation of an RL agent that makes decisions similar to a human dispatcher is not only interesting in terms of control but also in terms of long-term decision-making analysis in an ever-changing energy portfolio. The specific novelty of this work is in training an RL agent on an accurate testing environment of eight real-world cascade hydropower plants on the river Drava in Slovenia and comparing the agent’s performance to human dispatchers. The results show that the RL agent’s absolute mean error of 7.64 MW is comparable to the general human dispatcher’s absolute mean error of 5.8 MW at a peak installed power of 591.95 MW.

Keywords: cascade hydropower; reinforcement learning; digital twin (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: 2025
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