A three-layer joint distributionally robust chance-constrained framework for optimal day-ahead scheduling of e-mobility ecosystem

A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation as a whole order to overcome range anxiety and create viable business model charging stations (CSs). The must account stochastic nature all stakeholders' operations, including EV drivers, CSs, retailers their mutual interactions. In this paper, three-layer joint distributionally robust chance-constrained (DRCC) is proposed plan grid-to-vehicle (G2V) vehicle-to-grid (V2G) day-ahead ecosystems. does not rely on specific probability distribution parameters. To solve problem, iterative process using DRCC formulation. achieve computational traceability, exact reformulation implemented double-sided single-sided chance constraints (CCs). Furthermore, impact temporal correlation uncertain PV generation CSs considered. simulation study carried out three retailers, nine 600 EVs based real data from San Francisco, USA. results show necessity applicability such method environment, e.g., by reducing unique that failed reach destination 272 61.