With the increased roll-out of electric vehicles (EV), the need, but also opportunity, for leveraging their flexibility in the context of grid support, ancillary services and local market energy trading. However, the uncertainty and variability in driving patterns and resultant charging profiles pose substantial risk for aggregators. Given this context, the paper demonstrates a method for producing a stochastic, socio-economically-differentiated aggregation model that determines the flexibility space of a realistic and diverse EV fleet. A probabilistic Monte Carlo Markov Chain model is developed that allows for the overlay and comparison of different technical, spatial and social-economic behavioural factors through clustering and correlation analyses. In turn, the model enables a statistically significant analysis of the 'Energy Space' available that captures the inherit risk and uncertainty when leveraging EV flexibility.
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