The effect of mobility forecasts for stochastic charge scheduling of aggregated PEV

Abstract

Plug-in Electric Vehicles (PEV) integration into innovative smart grids has been studied widely. Recently, special attention is paid to the role of the aggregation agent, which might be responsible of controlling the charge schedules to its own and the system’s benefit. Furthermore, an increasing awareness of the stochasticity involved in PEV charging, reflected in a number of recent publications, can be observed. Among others, the PEV energy retail problem with interactions in day-ahead and balancing markets has been formulated from the aggregator’s perspective, taking into account location dependent network tariffs in the form of capacity prices for active power. However, a numerical quantification of the benefit from accounting for stochasticity has yet to be carried out. Therefore, this paper uses standard methodology to calculate stochastic programming quality metrics such as the value of the stochastic solution (VSS) and the expected value of private information (EVPI) for an established PEV energy retail aggregator model. Applied to a real medium voltage system with urban characteristics and realistic spatial PEV mobility, different levels of mobility forecasts are included as information at the first-stage here and now decisions.