# Steady-State Simulation for Combined Transmission and Distribution Systems

## TL;DR

This paper presents a unified circuit-based modeling approach for steady-state simulation of combined transmission and distribution systems, improving convergence robustness and scalability for large, complex power grids with renewable integration.

## Contribution

It introduces an equivalent circuit framework for combined T&D networks and demonstrates robust convergence using Newton-Raphson and parallel solvers for large-scale systems.

## Key findings

- Robust convergence achieved for small systems with a single machine.
- Scalable solution demonstrated for large systems with over a million nodes.
- Unified modeling approach enables efficient simulation of future renewable-rich grids.

## Abstract

The future electric grid will consist of significant penetration of renewable and distributed generation that is likely to create a homogenous transmission and distribution (T&D) system, requiring tools that can model and robustly simulate the combined T&D networks. Existing tools use disparate models and formulations for simulation of transmission versus distribution grids and solving for the steady-state solution of the combined T&D networks often lacks convergence robustness and scalability to large systems. In this paper, we show that modeling both the T&D grid elements in terms of currents and voltages using an equivalent circuit framework enables simulation of combined positive sequence networks of the transmission grids with three-phase networks of the distribution grids without loss of generality. We further demonstrate that we can ensure robust convergence for these resulting large-scale complex T&D systems when the circuit simulation methods are applied to them. Our results illustrate robust convergence of combined T&D networks using a direct Newton-Raphson solver on a single machine for smaller sized systems and using a parallel Gauss-Seidel-Newton solver on multiple machines for larger sized systems with greater than million nodes.

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Source: https://tomesphere.com/paper/1907.12725