Robust vehicle-to-grid power dispatching operations amid sociotechnical complexities

The technical and social complexities that characterize electric vehicle owners and the power market degrade the positive impacts of the emerging vehicle-to-grid technique. Motivated by sociotechnical challenges in practical V2G operations, we aim to design an efficient EV charging and discharging scheduling strategy to improve the reliability and profitability of V2G operations. Specifically, we propose a robust model to optimize V2G charging and discharging scheduling. Without requiring full information regarding the distribution data, our methodology framework, which adopts a distributed robust optimization framework, facilitates V2G aggregators to address operational uncertainties such as users’ travel demands. We adopt a Benders Decomposition algorithm to handle the intractable nonlinear robust counterparts. Our linear approximation of the nonlinear BD subproblem is more effective at reducing the solution complexity than previous research. A case study in CAR2GO in Amsterdam, with 12 service region and three months of travel demand data, reveal that: (1) The adverse impacts on the power dispatching cost, caused by the Range Anxiety in the vehicle-to-grid operations, are mitigated by adopting our integrated policy compared with the traditional deterministic method. (2) By adopting the proposed policy and decomposition algorithm, vehicle-to-grid aggregator benefits through lower operational costs and near 76.74% decision efficiency improvement under the large-scale dispatching programming. (3) Vehicle-to-grid aggregator, the government should be prudent to design a power dispatching plan by considering the range anxiety and battery durability for their significant impacts on the reliable service, environment, and cost control.