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Abstract |
Growing uncertainty from renewable energy integration and distributed
energy resources motivate the need for advanced tools to quantify the
effect of uncertainty and assess the risks it poses to secure system
operation. In general, Uncertainty Quantification (UQ) methods are used
in complex systems research to give probabilistic guarantees.
I will introduce the context of this work, as well as the Polynomial
chaos expansion (PCE) method that has been recently proposed as a tool
for UQ in Power Systems. The method produces results that are highly
accurate, but are computationally challenging to scale to large systems.
We propose a modified algorithm based on PCE and using the system
sparsity with significantly improved computational efficiency while
retaining the desired high level of accuracy. In an example, we show how
to solve the so called chance constrained power flow problem, e.g. we
need a solution such that the power transmitted trough the lines should
be lower than some safe value 99 percent of the time.
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