Designing aggregation criteria for end-users integration in energy communities: Energy and economic optimisation based on hybrid neural networks models

G. Barone, A. Buonomano, G. Cipolla, C. Forzano, G.F. Giuzio and G. Russo

Applied Energy, 2024, vol. 371, issue C, No S0306261924009267

Abstract: This study presents innovative methodologies addressing critical challenges in energy community real-cases implementation. The investigation is conducted by exploring and enhancing the concept of Peer-to-Peer energy community, where prosumers interact with consumers by sharing surplus energy to meet their electricity demands. The end-users' connections are optimised by maximizing their energy interactions and the proposed pricing strategies are based on balancing the supply and demand curves for tailored unit costs.

Keywords: P2P energy community; Energy community aggregation criteria; Hybrid neural network model for electrical loads estimations; Bi-level optimisation problem to design energy community; Local energy market; P2P cluster archetype; Building archetype (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924009267
Full text for ScienceDirect subscribers only

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:eee:appene:v:371:y:2024:i:c:s0306261924009267

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123543

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().