A flexible numerical tool for large dynamic DC networks

TL;DR

This paper introduces a flexible numerical tool that efficiently simulates large dynamic DC networks in the time domain, leveraging sparsity to handle stiff, oscillatory equations with linear scalability in computational cost.

Contribution

It demonstrates that conventional adaptive time stepping schemes can be effectively applied to large DC networks by exploiting sparsity, enabling scalable and efficient simulations.

Findings

Adaptive time stepping schemes are effective for large DC networks.

Simulation cost scales linearly with network size.

Sparsity exploitation improves computational efficiency.

Abstract

DC networks play an important role within the ongoing energy transition. In this context, simulations of designed and existing networks and their corresponding assets are a core tool to get insights and form a support to decision-making. Hereby, these simulations of DC networks are executed in the time domain. Due to the involved high frequencies and the used controllers, the equations that model these DC networks are stiff and highly oscillatory differential equations. By exploiting sparsity, we show that conventional adaptive time stepping schemes can be used efficiently for the time domain simulation of very large DC networks and that this scales linearly in the computational cost as the size of the networks increase.