Power Reserve Procurement Considering Dependent Random Variables with PCE

TL;DR

This paper introduces a method using generalized polynomial chaos and Gaussian copulas to model dependent uncertainties in power reserve procurement, improving the accuracy of chance-constrained optimization under dependency structures.

Contribution

It develops a novel approach to incorporate dependencies between stochastic inputs in power system optimization using PCE and copulas, enhancing modeling fidelity.

Findings

The method accurately captures dependencies between variables.

Compared to independent assumptions, the approach yields more reliable reserve procurement.

Demonstrated effectiveness in a probabilistic power reserve problem.

Abstract

This paper presents an approach for the modelling of dependent random variables using generalised polynomial chaos. This allows to write chance-constrained optimization problems with respect to a joint distribution modelling dependencies between different stochastic inputs. Arbitrary dependencies are modelled by using Gaussian copulas to construct the joint distribution. The paper exploits the problem structure and develops suitable transformations to ensure tractability. The proposed method is applied to a probabilistic power reserve procurement problem. The effectiveness of the method to capture dependencies is shown by comparing the approach with a standard approach considering independent random variables.