(Online) Convex Optimization for Demand-Side Management: Application to Thermostatically Controlled Loads

Bianca M. Moreno (), Margaux Brégère (), Pierre Gaillard () and Nadia Oudjane ()
Additional contact information
Bianca M. Moreno: Univ. Grenoble Alpes
Margaux Brégère: EDF R&D
Pierre Gaillard: Univ. Grenoble Alpes
Nadia Oudjane: EDF R&D

Journal of Optimization Theory and Applications, 2025, vol. 205, issue 3, No 2, 32 pages

Abstract: Abstract To counter the challenge of integrating fluctuating renewables into the grid, devices like thermostatically controlled loads (water-heaters, air conditioners, etc.) offer flexible demand. However, efficiently controlling a large population of these devices to track desired consumption signals remains a complex challenge. Existing methods lack convergence guarantees and computational efficiency or resort to regularization techniques instead of tackling the target tracking problem directly. This work addresses these drawbacks. We propose to model the problem as a finite horizon episodic Markov decision process, enabling us to adapt convex optimization algorithms with convergence guarantees and computational efficiency. This framework also extends to online learning scenarios, where daily control decisions are made without prior knowledge of consumer behavior and with daily-changing target profiles due to fluctuations of energy production and inflexible consumption. We introduce a new algorithm, called Online Target Tracker (OTT), the first online learning load control method, for which we prove sub-linear regret. We demonstrate our claims with realistic experiments. This combination of optimization and learning lays the groundwork for more dynamic and efficient load control methods.

Keywords: Thermostatically controlled loads; Online learning; Convex optimization; Markov decision process; 68W27; 90C40 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-025-02658-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joptap:v:205:y:2025:i:3:d:10.1007_s10957-025-02658-9

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-025-02658-9

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().