Research projects

Electric vehicles (EVs) are becoming popular in smart cities due to their ability to significantly reduce carbon intensity. However, the large-scale introduction of EVs into existing electricity grids will pose significant stability challenges that economics researchers can address. Specifically, through pricing schemes, researchers and policymakers can incentivize EV owners to shift their EV charging to periods when it is more beneficial for the grid in exchange for savings. In addition, the use of machine learning methods can further inform such pricing schemes by leveraging environmental information signals, thereby leading to improved outcomes for both cities and EV owners.

This project designs pricing algorithms that leverage available data to shape the EV charging demand profile and satisfy the objectives of stakeholders, such as grid operators and electricity providers. Specifically, the project has proposed a novel pricing scheme which, in combination with machine learning, can achieve better results for the grid while ensuring savings for EV owners. Current results show that the proposed pricing scheme can shape the demand generated by EV charging so that it follows the production patterns of renewable energy sources. Importantly, the proposed algorithms outperform benchmarks, yielding results very close to the theoretical optimum. This part of the project is under review at a major information systems journal.

Furthermore, the proposed pricing scheme can be used to enhance the profitability of stakeholders, such as EV charging hubs that offer large charging facilities, while supporting them in meeting sustainability goals. In this line of work, again the use of machine learning is crucial, as it facilitates better price-setting decisions for EV charging hubs. The results show that the proposed pricing scheme enhanced with machine learning yields better results compared to the benchmarks, and close to the theoretical optimum. This part of the project is currently under review at a major management journal.