On the Statistical Settings of Generation and Load in a Synthetic Grid Modeling

TL;DR

This paper develops a statistically grounded method for assigning generation capacities and load settings in synthetic power grid models, considering electrical parameters, topology, and correlations to improve realism.

Contribution

It introduces an entropy-based optimization approach that accounts for correlations between bus locations, capacities, and network topology in synthetic grid modeling.

Findings

The method accurately reproduces the distribution of generation and load settings.

It captures the correlation between bus degree and capacity/load.

The approach enhances the realism of synthetic power grid models.

Abstract

This paper investigates the problem of generation and load settings in a synthetic power grid modeling of high-voltage transmission network, considering both electrical parameters and topology measures. Our previous study indicated that the relative location of generation and load buses in a realistic grid are not random but correlated. And an entropy based optimization approach has been proposed to determine a set of correlated siting for generation and load buses in a synthetic grid modeling. Using the exponential distribution of individual generation capacity or load settings in a grid, and the non-trivial correlation between the generation capacity or load setting and the nodal degree of a generation or load bus we develop an approach to generate a statistically correct random set of generation capacities and load settings, and then assign them to each generation or load bus in a grid.