The U.S. electrical power system is at a crossroads between its mission to deliver cheap and safe electrical energy, a strategic aim to increase the penetration of renewable energy, an increased reliance on smart grid technology, and the critical need to maintain and increase the reliability of the grid. Additionally, the operation and planning of the grid with these requirements involves an unprecedented amount of uncertainty in supply and demand brought on by the high variability of wind, solar, and other renewable power sources.

This project will develop advanced optimization methods for the power grid under uncertainty, with the aim of ensuring efficient, reliable, cost-effective, and sustainable electrical energy operations. Specifically, researchers will investigate the modeling of stochastic power grid optimization on massively parallel supercomputers. In this particular case, stochastic programming is a leading formulation that uses optimization under uncertainty to balance the variability in the wind supply with its benefits in the context of operational reliability.

The project’s main technical challenges are that the tasks involved are highly computationally intensive, they must run under strict time constraints, and they must run at a high frequency to properly manage the large real-time fluctuations of supply and demand. This project will solve optimization problems of unprecedented size (tens of billions variables and constraints), which will also help to advance the field of mathematical optimization.