Model order reduction applied to heat conduction in photovoltaic modules

TL;DR

This paper applies a modified Discrete Empirical Interpolation Method to efficiently reduce the computational complexity of modeling heat conduction in photovoltaic modules, maintaining accuracy while significantly decreasing system size.

Contribution

It introduces a tailored DEIM approach for nonlinear thermal equations in PV modules, enhancing model reduction effectiveness.

Findings

Significant reduction in system size achieved

Maintains high accuracy in temperature predictions

Applicable to large-scale PV system simulations

Abstract

Modelling of physical systems may be a challenging task when it requires solving large sets of numerical equations. This is the case of photovoltaic (PV) systems which contain many PV modules, each module containing several silicon cells. The determination of the temperature field in the modules leads to large scale systems, which may be computationally expensive to solve. In this context, Model Order Reduction (MOR) techniques can be used to approximate the full system dynamics with a compact model, that is much faster to solve. Among the several available MOR approaches, in this work we consider the Discrete Empirical Interpolation Method (DEIM), which we apply with a suitably modified formulation that is specifically designed for handling the nonlinear terms that are present in the equations governing the thermal behaviour of PV modules. The results show that the proposed DEIM technique is able to reduce significantly the system size, by retaining a full control on the accuracy of the solution.