The objective of this research is to develop computationally efficient methods for evaluating the global impact of parametric uncertainties on power flow solutions and the dynamic performance of electrical power systems. The approach is to integrate two promising tools, the probabilistic collocation method and trajectory sensitivity-based methods. These tools permit the selection of a sparse set of simulations from which a mapping between uncertain parameters and the output(s) of interest can be found. The mappings can be used to study the effects of critical uncertainties, and to tune parameters to mitigate those effects.
Intellectual Merit:
The intellectual merit of the project lies in the study of methodologies that permit uncertainty evaluation for large dynamic models of electric power systems. The project will develop techniques for selecting a sparse set of simulations that enable performance evaluation over the domain of possible parameter values. Investigations of trajectory sensitivities will seek to establish a fundamental understanding of the influence of dynamic models on uncertainty propagation. Insights into the role played by the network structure will be provided, and tools that exploit that structure developed.
Broader Impact:
Utilities use simulation-based evaluation of system behavior to make decisions that have tremendous economic and reliability implications. The parameters of many models are, however, quite uncertain. The techniques developed in this project will allow efficient assessment of such uncertainty. The risk of large disturbances will be reduced accordingly. Given the wide applicability of the developed methods to general large-scale systems, the project will stimulate diverse educational opportunities including courses on the graduate level and undergraduate capstone design. Efforts for recruiting students from underrepresented groups including students from the University of Puerto Rico-Mayaguez will increase diversity at the home institutions end ultimately contribute to a more diverse engineering workforce.
