Hierarchical RL-MPC for demand response scheduling
File(s)
Published version
Author(s)
Bloor, Maximilian
Del Rio Chanona, Ehecatl Antonio
Tsay, Calvin
Type
Conference Paper
Abstract
This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive control (LMPC). We investigate two control architectures: a direct RL approach and a control-informed methodology where an RL agent provides setpoints to a lower-level LMPC. The proposed RL-LMPC framework demonstrates improved sample efficiency during training and better constraint satisfaction compared to direct RL control. Using an industrial ASU case study, we show that our approach successfully manages operational constraints while optimizing electricity costs under time-varying pricing. Results indicate that the RL-LMPC architecture achieves comparable economic performance to direct RL while providing better robustness and requiring fewer training samples to converge. The framework offers a practical solution for implementing flexible operation strategies in process industries, bridging the gap between data-driven methods and traditional control approaches.
Date Issued
2025-08-13
Date Acceptance
2025-06-01
Citation
IFAC-PapersOnLine, 2025, 59 (6), pp.229-234
ISSN
2405-8963
Publisher
Elsevier BV
Start Page
229
End Page
234
Journal / Book Title
IFAC-PapersOnLine
Volume
59
Issue
6
Copyright Statement
Copyright © 2025 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control.
Identifier
10.1016/j.ifacol.2025.07.150
Source
14th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2025
Publication Status
Published
Start Date
2025-06-16
Finish Date
2025-06-19
Coverage Spatial
Bratislava, Slovakia
Date Publish Online
2025-08-13