Integrating Renewable Energy Forecast Uncertainty in Smart-Charging Approaches for Plug-in Electric Vehicles

Both an increasing share of intermittent renewable energies and an introduction of plug-in electric vehicles (PEVs) are challenging for the electric power system. Nevertheless, PEVs could be used as distributed storage resources to help integrate fluctuating energy sources into the power system. In this paper we analyze the case where PEV batteries are used to compensate the forecast error of a wind power plant. We introduce a day-ahead charging scheduling strategy that minimizes system generation costs, enforces network and PEV end-use constraints, and at the same time enables the fleet to compensate deviations of wind power output from its day-ahead forecast. For this purpose, a probabilistic wind power forecast model is integrated into an Optimal Power Flow based smart-charging scheme. The fleet is modeled as a set of virtual storages whose characteristics depend on individual driving patterns. Results show that with the proposed scheme enough charging flexibility is made available to compensate the forecast error of a wind power plant. However, there is a trade-off between charging flexibility and cost-minimization.