Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A multi-agent based scheduling algorithm for adaptive electric vehicles charging

Xydas, Erotokritos, Marmaras, Charalampos and Cipcigan, Liana Mirela ORCID: 2016. A multi-agent based scheduling algorithm for adaptive electric vehicles charging. Applied Energy 177 , pp. 354-365. 10.1016/j.apenergy.2016.05.034

[thumbnail of 1-s2.0-S0306261916306286-main.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview
License URL:
License Start date: 1 January 2015


This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
Uncontrolled Keywords: Adaptive charging; Decentralized charging control algorithm; Electric vehicles and multi-agent
Publisher: Elsevier
ISSN: 0306-2619
Date of First Compliant Deposit: 31 May 2016
Date of Acceptance: 3 May 2016
Last Modified: 01 Nov 2022 10:24

Citation Data

Cited 62 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics