TF Motivation: Uncertainty quantification in modern power-system operation and planning is attracting the attention of power researchers due to an increasing penetration of renewable energy resources, the stochasticity of the loads, and the advent of demand response to price, among others. These uncertainties can lead to dramatic fluctuations in the system states that may jeopardize the system operation, protection, and planning.

Thanks to a large deployment of advanced sensors and communication technologies, power engineers are improving the quantification of the uncertainties in power systems. Subsequently, the methodologies to handle these uncertainty are also in a booming period. New methods based on machine learning and statistical and applied mathematics have been developed, which rely on modern-computing technologies, such as HPC and GPU. Although increasing efforts have been made, the scalability, computing efficiency and the economic cost of the proposed solution methods call for further improvement. Motivated by these challenges, this TF has been set up with the aim to provide a systematically way to simulate, quantify, manage, and mitigate these uncertainties in power systems and to facilitate the uncertainty management for the power industry.

TF Plan: This TF will first gather the practical concerns from the industry and utilities regarding vast uncertainties in power systems. It will further engage researchers from universities, national labs, industry, and utilities to propose novel solutions to the major challenges. Apart from the power engineers, researchers from statistics, applied mathematics, and computational data science will also be invited to share their expertise and experience in addressing the uncertainty in their research.