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

TL;DR

This paper introduces a data-driven sparse polynomial chaos expansion method that accurately and efficiently estimates the probabilistic total transfer capability in power systems with renewable energy sources, without assuming input distributions.

Contribution

The novel DDSPCE method directly uses data sets for probabilistic TTC estimation, improving efficiency and accuracy without pre-assumed input distributions.

Findings

Accurately estimates probabilistic TTC characteristics.

Demonstrates high computational efficiency.

Highlights importance of discrete random inputs in assessments.

Abstract

The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC). In this 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 probabilistic distributions of random inputs, exploits data sets directly in estimating the PTTC. Besides, a sparse scheme is integrated to improve the computational efficiency. Numerical studies on the modified IEEE 118-bus system demonstrate that the proposed DDSPCE method can achieve accurate estimation for the probabilistic characteristics of PTTC with a high efficiency. Moreover, numerical results reveal the great significance of incorporating discrete random inputs in PTTC and ATC assessment, which nevertheless was not given sufficient attention.