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