Analyze energy procurement of EV charging stations

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Supervisor:
Dr. Divényi Dániel Péter
Department of Electric Power Engineering

The installation and operation of charging infrastructure, which is essential for the propagation of the electric vehicles, will be one of the biggest challenges of the following decade. From the power market’s point of view, the vehicles will appear as new, presumably household-related consumers in the sector. In addition to the home charging option, there will be noteworthy significance of public charging points. The energy, procured from public charging points for charging service, has to be purchased from the current power market.

The purpose of the work is to investigate financial models for charging electric vehicles. Accordingly, in this work it is presented, how a charging point operator can position itself in today's electricity market and what legislative framework provides suitable environment for its actions. Furthermore, the challenges that occur in the charging market, operating in the current structure are reviewed - both on a national and a wider, European scale. In addition, an alternative market model is being developed that investigates hypothetical roles, other than those which exist in the current market structure, with the aim of resolving some of the challenges associated with this market structure.

Due to the low number of electric cars and the limited number of available data on the charging infrastructure, the simulation of the charging models is inevitable. Therefore, a scalable stochastic model was developed to simulate the power consumption of electric vehicles arriving at public charging points. The model enables the setting of waiting time, the number of serving units as well as the time characteristic of charging power as parameters, in order to provide 15-minute load profiles. The load curves, obtained as the output of the simulation, describe the charging point’s daily energy consumption. Based on the consumption, data specific costs of the simulated charging point (monthly and annual energy procurement costs according to different portfolios, the amount and cost of balancing energy) can be estimated. This work aims to evaluate the energy procurement of charging points based on the simulated results of the self-developed stochastic simulation model.

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