Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

TL;DR

This paper introduces a hybrid uncertainty propagation method combining probability and possibility distributions, along with evidence theory, to improve the reliability assessment of distributed generation systems under both aleatory and epistemic uncertainties.

Contribution

It is the first to jointly model and propagate aleatory and epistemic uncertainties in DG system adequacy assessment using a hybrid approach with evidence theory.

Findings

Hybrid approach explicitly captures imprecision in DG parameters.

Method effectively models uncertainty growth with higher DG penetration.

Demonstration on IEEE test feeder validates the approach.

Abstract

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels.