At the MIT Energy Hackathon, in just 36 hours I created a working Matlab nonlinear constrained optimization solver to compute optimal power profiles for electric vehicle charging.
The solver finds the charge power profile that minimizes power variation at the grid, site, and vehicle levels, resulting in lower electricity costs, stabler grids, and longer battery lifetimes. Furthermore, to mitigate CO2 emissions, the solution prioritizes charging at times when more grid power is derived from clean energy such as solar. Historical hourly data about EV charge demand, grid usage, and energy production are used to inform the optimization.
The hackathon emphasized diverse skillsets and thinking about the economic, social, and political aspects of energy innovation in addition to the technical side. My teammates included two MIT MBA students and a British general engineering student. I was the sole developer on the Matlab optimization model, and I also contributed to my team's overall problem formulation, ideation, and presentation.
Link to optimization code (matlab problem-based nonlinear optimization solver)
Link to team presentation (5 minute pitch to a panel of industry judges)
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