Collaborative Research: AMPS: Robust Failure Probability Minimization for Grid Operational Planning with Non-Gaussian Uncertainties

The proposed research will develop a novel methodology for the \textbf{probabilistic quantification and minimization of power system failures due to non-Gaussian sources of uncertainty} for day-to-day operational planning. The US goverment's goal of decarbonizing the electric power grid is spurring record levels of renewable energy penetration, particularly wind and solar energy. Integrating this growing share of renewable energy sources in operational planning tools %, such as economic dispatch, AC optimal power flow, and unit commitment, requires addressing several key mathematical and computational challenges brought about by their inherent intermittency, variability, and forecast uncertainty. As the total supply of electricity must always match its demand at all times, failure to account for uncertainty can create power imbalances that can de-stabilize the grid or result in cascade failures. Using more accurate descriptions of renewable power forecast uncertainty is also crucial for economic and environmental efficiency, as fewer thermal generation reserves need to be kept or brought online in order to compensate for changes in renewable power output. Consequently, failure to accurately characterize and mitigate risks due to uncertainty in renewable generation can jeopardize both system reliability and the achievement of decarbonization targets. Historically, load fluctuations have been the only major source of uncertainty in transmission planning due to the low amount of grid-connected renewable power. Characterizing and minimizing the associated risk has not posed a major hurdle because of two factors. First, load forecasts are fairly accurate over operational planning horizons with Gaussian-like error distributions. Second, the grid has had a large amount of rotational synchronous inertia that has been able to effectively absorb power imbalances. Both these factors are no longer true. Renewable power outputs are strongly affected by exogenous conditions such as temperatures, wind speeds and solar irradiance that exhibit strong non-Gaussian characteristics. Therefore, the larger share of renewables in total generation in conjunction with the reduction in inertia are necessitating new mathematical developments for grid operational planning under uncertainty that is primarily non-Gaussian.