Pathway for Multivariate Dependence Modeling in Long-Term Horizon of Electrical Power System

TL;DR

This paper proposes a multivariate dependence modeling approach using vine copulas to better understand the complex relationships between electrical load and renewable energy sources, aiding power system planning amidst increasing renewable penetration.

Contribution

It introduces a novel pathway for multivariate dependence modeling in power systems using vine copulas, integrating load and renewable generation variables.

Findings

Vine copula-based models effectively capture dependencies between load and renewable sources.

The approach improves the accuracy of uncertainty quantification in power system planning.

Enhanced modeling supports better integration of renewable energy into the grid.

Abstract

Future electricity consumption is fundamentally uncertain and dependent on many variables such as economic activity, weather, electricity rates and demand side management. The stochasticity of system load as well as power generation from renewable energy sources (RES) (i.e., wind and solar) poses special challenges to power system planners. Increasing penetration levels of wind and solar exacerbate the uncertainty and variability that must be addressed in coming years, and can be extremely relevant to power system planners. With this paper, pathways for multivariate dependence modeling using vine copula is proposed which includes both electrical load and power generation from RES.