Electric vehicle aggregation model: A probabilistic approach in representing flexibility

• Published in: Electric power systems research 2022-12, Vol.213, p.108484, Article 108484
• Main Authors: Jenkins, Mark, Kockar, Ivana
• Format: Article
• Language: English
• Subjects: Aggregation; Behavioural modelling; Electric vehicles; Markov-chain; Probabilistic models
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2022.108484

•Stochastic Monte Carlo Markov chain model is used to synthetically produce verifiable electric charging and driving profiles.•Identification of prominent socioeconomic and behavioural factors affecting charging and driving behaviour.•Statistically significant analysis of electric vehicle driving and charging patterns that captures the inherit risk and uncertainty when leveraging their flexibility. 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.