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Growing uncertainty from renewable energy integration and distributed energyresources motivate the need for advanced tools to quantify the effect ofuncertainty and assess the risks it poses to secure system operation.Polynomial chaos expansion (PCE) has been recently proposed as a tool foruncertainty quantification in power systems. The method produces results thatare highly accurate, but has proved to be computationally challenging to scaleto large systems. We propose a modified algorithm based on PCE withsignificantly improved computational efficiency that retains the desired highlevel of accuracy of the standard PCE. Our method uses computationalenhancements by exploiting the sparsity structure and algebraic properties ofthe power flow equations. We show the scalability of the method on the 1354pegase test system, assess the quality of the uncertainty quantification interms of accuracy and robustness, and demonstrate an example application tosolving the chance constrained optimal power flow problem.

Efficient Polynomial Chaos Expansion for Uncertainty Quantification in Power Systems