A multi-agent based scheduling algorithm for adaptive electric vehicles charging. (1st September 2016) - Xydas, Erotokritos; Marmaras, Charalampos; Cipcigan, Liana M.
Highlights: A decentralised EV charging control model was developed. EV were separated in "Responsive" and "Unresponsive" EV to control signals. Generation and demand forecasts were considered in the charging control model. The adaptive behaviour of Responsive EV agents was experimentally demonstrated. 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.