Abstract
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.
Highlights
The integration of electric vehicles is considered as a promising alternative to reduce transportation related emissions and improve energy consumption efficiency
The control model was experimentally demonstrated at the Electric Energy Systems Laboratory hosted at the National Technical University of Athens (NTUA)
The results demonstrated the adaptive behaviour of Responsive EV agents and their preference to charge from renewable energy sources
Summary
The integration of electric vehicles is considered as a promising alternative to reduce transportation related emissions and improve energy consumption efficiency. The infeasibility problems of [52] are solved in [54], where the price responses of non-strictly convex DERs are considered fixed This approach creates significant new demand peaks, as the price responses are concentrated at the lowest-priced periods of the coordination horizon. To overcome this problem, a non-linear pricing scheme is adopted in [53]. According to the best of the authors’ knowledge, there are no decentralised charging control models utilising forecasting procedures to deal with the uncertainties regarding EV participation in the control scheme. Three factors were investigated: (a) the location of the EV/DG aggregator, (b) the importance of forecasting the demand from Unresponsive EV agents and (c) the charging behaviour of Responsive EV agents when renewables generation is available.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
