A hierarchical blockchain architecture based V2G market trading system

Qingsong Luo, Yimin Zhou, Weicheng Hou and Lei Peng

Applied Energy, 2022, vol. 307, issue C, No S0306261921014392

Abstract: Along with the increasing prosperity of electric vehicles (EVs), the large-scale penetration of EVs in V2G (Vehicle to Grid) trading market brings difficulties and security issues in the operation of the energy market. The blockchain has the characteristics of security, transparency, decentralization and traceability, which provides an alternative for complex energy market operation in a secure environment. In this paper, a novel hierarchical blockchain architecture is constructed for the V2G trading system including Data Blockchain layer, Operation Blockchain layer and Transaction Blockchain layer. The intelligent contracts of the V2G trading, two-level auction and optimization strategies are designed for scheduling EVs orderly charge and discharge to participate in the grid load regulation so as to minimize the total load variance of the grid and realize reasonable benefit distribution among the involved market entities, i.e., EVs, aggregators and grid. Based on the V2G trading characteristics, a transaction manager with four optimization strategies are designed to further optimize the transaction generation process. The CA (cellular automata) based simulation experiments are performed to verify the effectiveness of the proposed V2G trading system.

Keywords: Intelligent contract; Blockchain; Cellular automata; Trading system; Vehicle to Grid (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921014392
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:307:y:2022:i:c:s0306261921014392

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.2021.118167

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 ().