Distributed model-free optimisation in community-based energy market

Houman Asgari and Maryam Babazadeh

International Journal of Systems Science, 2025, vol. 56, issue 12, 3098-3113

Abstract: A distributed data-driven algorithm is proposed for market clearing within a community-based energy market. The objective is to maximise social welfare in energy trading. Each participant in the market, known as a prosumer, employs an extremum-seeking control (ESC) algorithm within a consensus-based architecture based on simple third-order dynamics. The proposed method circumvents the need for a central coordinator in the market clearing process. Furthermore, prosumers do not require analytical and exact formulations of cost or utility functions. They are required to transmit only a single local decision variable to the neighbours through an undirected connected graph. Under the assumption that prosumers have strongly convex local objective functions, the proposed algorithm demonstrates semi-global practical asymptotic (SPA) convergence to the optimal solution. This convergence is established using averaging theory. Simulation results validate the effectiveness, scalability, and robustness of the proposed distributed strategy.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2025.2469151 (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:12:p:3098-3113

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

DOI: 10.1080/00207721.2025.2469151

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 ().