Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks

The ability to determine optimal charging profiles of electric vehicles (EVs) is paramount in developing an efficient and reliable smart-grid. However, so far the level of analysis proposed to address this issue lacks combined spatial and temporal elements, thus making mobility a key challenge to address for a proper representation of this problem. This paper details the principles applied to represent optimal charging of EVs by employing an agent-based model that simulates the travelling patterns of vehicles on a road network. The output data is used as a reliable forecast so an optimal power flow model can devise optimal charging scenarios of EVs in a local electrical network. The effectiveness of the model is illustrated by presenting a multi-day case study in an urban area. Results show a high level of detail and variability in EV charging when a present-day carbon fuel mix is compared to one with lower carbon intensity.

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