Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets
Poria Astero, Corentin Evens
Abstract
This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the stochastic methodology behind this application. To achieve this, first, deterministic models are developed to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for disturbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed application estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed application is evaluated by using empirical charging data of public EVCS in Helsinki area.