Event-based reinforcement learning algorithm for dynamic resource allocation in smart grid

Jiayi Wang and Wenfeng Hu

International Journal of Systems Science, 2025, vol. 56, issue 10, 2447-2465

Abstract: In this paper, we propose a dynamic event-based distributed reinforcement learning algorithm to address the resource allocation problem in a smart grid. The main challenge lies in the presence of unknown resource transfer functions and cost functions. Our objective is to discover a set of resource allocation solutions that minimise the total cost functions over a specified period. Our approach involves re-constructing a new convex optimisation problem to facilitate the approximation of Q-values in the reinforcement learning training process. Additionally, to avoid continuous communication, we integrate an event-based distributed optimisation approach to approximate the action-value function. We support our methodology with theoretical proofs that establish the validity of the event-triggered conditions and demonstrate the algorithm's convergence. We further validate the algorithm's feasibility through an illustrative example involving output power allocation in a smart grid.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2024.2448770 (text/html)
Access to full text is restricted to subscribers.

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:taf:tsysxx:v:56:y:2025:i:10:p:2447-2465

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2024.2448770

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().