Self Scheduling of Plug-In Electric Vehicle Aggregator to Provide Balancing Services for Wind Power

This paper focuses on the self-scheduling problem of an aggregator of plug-in electric vehicles (PEVs) purchasing energy in the day-ahead market, and offering balancing services for a wind power producer, i.e., committing to compensate the forecast errors of wind power plants. The aggregated charging and discharging flexibility of the PEV fleet is represented by a probabilistic virtual battery model, accounting for the uncertainty in the driving patterns of PEVs. Another source of uncertainty is related to the balancing requests, which are a function of the forecasted wind power output. A scenario-based robust approach is used to tackle both sources of uncertainty in a tractable way. The interdependency between the day-ahead market prices and the aggregator's bidding decisions is addressed using complementarity models. A case study analyzes the capability of the PEV aggregation to provide balancing services, for different settings of the balancing contract, and both with and without the use of vehicle-to-grid.