Power the Future Energy System with Computer Science

The legacy power system, though it has worked admirably over a century, is in a dramatic shift for a more sustainable future. The shift is stimulated by the new components in the system, including storage systems, renewable energies, smart meter devices, etc. These new components are important for the sustainable grid, but they also challenge the control paradigm as well as the business model for the legacy grid. The theme of my research is to contribute a computer science perspective to power the future energy system.

During my research, I am deeply attracted by the chemistry between computer science and energy systems. When customizing computer science algorithms or frameworks for the electricity sector, the refined concepts or models in the energy system often enrich the scope of computer science. Many of such research seem counterintuitive in light of commonly held beliefs. For example, is local clustering property enough to guarantee the global robustness for data-driven pricing schemes? Is one simple threshold policy able to handle the uncertainties in the dynamic prices? Can we design an efficient energy sharing mechanism facing the regulatory obstacles? Does centralized control always guarantee the best performance? Are renewable energies really free? Inherent in these questions is my pursuit of the most reliable and effective design for the future electricity sector.

The following stories illustrate my most recent and future endeavors.

1. Robust data-driven pricing scheme design

2. Optimal storage control facing dynamic prices

3. Business model for storage sharing

4. Differential privacy in non-intrusive load monitoring

The interesting stories between CS and energy abound!

How to conduct load prediction? One may select its favorite predictor and seek to minimize the mean square error between prediction and true value. However, is this objective function the best choice for the system operator? Not necessarily! We submit that the best choice needs to consider the impact of prediction accuracy across time on the system cost. Meanwhile, the data utilization needs to be rather high for the selected objective function.

Storage device provides the vital flexibilities to the power system. In this research statement, the storage is often used to conduct arbitrage against different pricing schemes. However, storage can contribute much more than that. Without the carbon tax or the cap-and-trade scheme, how can the system operator achieve the carbon emission reduction? Use storage!

If you find any of these research problems interesting, talk to me!