Abstract
Solving the Power-Flow in realistic large-scale ill-conditioned systems supposes a challenging task for most of available solution methodologies. This paper tackles this issue by developing a novel efficient and robust Power-Flow method. It is mainly based on a Semi-Implicit approach but incorporates other numerical arrangements for enhancing its features. The resulting three-stage algorithm is validated using several realistic ill-conditioned systems ranging from 3012 to 70000-buses. Results show that the developed methodology constitutes an efficient and robust Power-Flow solution technique, outperforming the results obtained with other available approaches.
Highlights
If we assume the factorization of the Jacobian matrix as the heaviest computational part of a PF calculation [2], we can affirm that the developed PF solution procedure is quite efficient and its computational burden is comparable to Newton-Raphson technique (NR)
WORKS A novel three-stage algorithm (3S-SIA) for efficiently solving realistic large-scale ill-conditioned systems has been developed. It is mainly based on a Semi-Implicit approach (SIA) but incorporates other numerical arrangements for enhancing either the robustness or efficiency features
The developed 3S-SIA is quite efficient in the sense that it just requires a matrix factorization each iteration, resulting in similar computational burden in comparison NR
Summary
A huge variety of robust methodologies have been developed in order to properly solve ill-conditioned power systems These techniques are typically less efficient than NR, which limit its application in realistic large-scale systems. CONTRIBUTIONS From the Literature Review is deduced that most of available robust PF solvers are, totally inefficient to be applied in realistic cases This paper tackles this topic by developing a robust PF solution method suitable for large-scale ill-conditioned systems. This is achieved by proposing a robust but efficient yet algorithm based on the Semi-Implicit approach (SIA) developed in [19].
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