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Computer Science XI - Modeling and Simulation

BaCaTeC - Analyzing the Potential of Bidirectional Charging Strategies Based on Real-Data

Project Description

The increased number of electric vehicles causes challenges to the electric power system, mainly due to high peak loads from electric vehicle charging. Unidirectional smart charging algorithms can prevent this issue and, on top of that, help to better integrate renewable energy sources and decrease CO2 emissions. Even more can be achieved with Vehicle-to-Grid applications, where electric vehicles can feed power back to the grid. This immensely increases the possibilities for smart charging. Instead of only being able to shift demand, electric vehicles can now act as energy storage, charging energy at times with large amounts of renewable generation and discharging it at times of high demand. Vehicle-to-Grid increases the financial viability of smart charging use cases, for example, arbitrage or the provision of ancillary services, and introduces entirely new use cases, like self-sufficient PV and EV combinations for households and companies.

In this project, in cooperation with UC Berkeley's Transportation Sustainability Research Center, we analyze real-world smart charging data provided by BMW of North America. We utilize mixed-integer linear programming and simulation techniques to determine the profitability and possible CO2 emission reductions in unidirectional smart charging and vehicle-to-grid applications.