Optimal bidding strategy for V2G regulation services under uncertainty

Charging and discharging flexibility of plug-in electric vehicles (PEVs) implies a substantial utilization as a grid service provider. Among different grid services, regulation service is regarded as one of the most lucrative and requires an immediate action which PEVs can make by promptly charging or discharging; denoted by the Vehicle-to-Grid (V2G) technology. Reliability, however, remains challenging, mainly because of uncertainties in i) PEV driving and charging behaviors and ii) real-time operations of the system operator. Besides, evaluating grid service capability of PEVs at the individual vehicle level is computationally expensive. In this paper, we first integrate a PEV aggregate model to address the uncertainties in PEV plugin schedules and to improve computational efficiency. Then, we propose a stochastic model of an optimal bidding strategy to effectively address uncertainties in real-time operations for V2G regulation services. Simulations are conducted to validate the proposed strategy in the PJM Energy and Ancillary market. The results show that the bidding strategy is more lucrative with more elaborate evaluations in real-time operations. It is also shown that the proposed strategy is still lucrative despite a high penalty of regulation non-compliance and battery degradations.