Evaluating the network stability using probabilistic modelling of the future e-mobility ecosystem in urban areas

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Environmental concerns about greenhouse gas emission from transportation sector along with advancements in battery technologies has led to massive private and public investment into electrification of the sector (buses, vehicles, scooters, bikes). In this paradigm shift, managing e-mobility ecosystem and overcoming range anxiety are known as the key factors to unlock widespread adoption of the technologies. In this project, we intend to develop a stochastic optimal strategy for charging infrastructure design within distribution networks in urban areas to support future e-mobility ecosystem in both vehicle-to-grid and grid-to-vehicle operation. Several sources of uncertainties, including local traffic pattern, drivers' behaviour and renewable energy resources, will be considered in the proposed planning tool through stochastic modelling. A physical model of the transportation system (including roads, popular stopping points, parking lots, on-street parking) will be mapped over local low- and medium-voltage power network to account for network capacity availability and constraints. The outcome of the tool will be the optimal location of the future e-parking lots and on-street charging points, the size and types of the chargers at each location as well as the future network expansion to fulfil the e-mobility ecosystem requirements.