A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach in identifying a suitable schedule for the charging requests of a large EV fleet (up to 3200 units), with acceptable computational effort. The benefits on the economic sustainability of the E-carsharing fleet given by the participation in the electricity market are also confirmed by an extensive sensitivity analysis.

Automated summary

This paper proposes a tool to optimally schedule the charging requests of carsharing Electric Vehicles (EVs) in an urban area, allowing them to participate in the Ancillary Service Market. The tool uses a nature-inspired optimization algorithm to determine the initial charging time of each vehicle, and numerical simulations confirm its effectiveness. The participation of the EV fleet in the electricity market leads to economic sustainability for the carsharing service.