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The increasing uncertainty level caused by growing renewable energy sources(RES) and aging transmission networks poses a great challenge in the assessmentof total transfer capability (TTC) and available transfer capability (ATC). Inthis paper, a novel data-driven sparse polynomial chaos expansion (DDSPCE)method is proposed for estimating the probabilistic characteristics (e.g.,mean, variance, probability distribution) of probabilistic TTC (PTTC).Specifically, the proposed method, requiring no pre-assumed probabilisticdistributions of random inputs, exploits data sets directly in estimating thePTTC. Besides, a sparse scheme is integrated to improve the computationalefficiency. Numerical studies on the modified IEEE 118-bus system demonstratethat the proposed DDSPCE method can achieve accurate estimation for theprobabilistic characteristics of PTTC with a high efficiency. Moreover,numerical results reveal the great significance of incorporating discreterandom inputs in PTTC and ATC assessment, which nevertheless was not givensufficient attention.

A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables