A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal Profiles

TL;DR

This paper presents a detailed synthetic Texas power system model with time-series weather-dependent profiles, enabling more accurate analysis of renewable integration and operational costs compared to static test cases.

Contribution

The creation of a comprehensive, weather-dependent, spatiotemporal synthetic Texas power system dataset with five years of hourly profiles for advanced grid analysis.

Findings

Dynamic line ratings reduce operating costs by 1.7%.

Renewable dispatch increases by 1.3%.

High-resolution weather profiles significantly impact system costs.

Abstract

We developed a synthetic Texas 123-bus backbone transmission system (TX-123BT) with spatio-temporally correlated grid profiles of solar power, wind power, dynamic line ratings and loads at one-hour resolution for five continuous years, which demonstrates unique advantages compared to conventional test cases that offer single static system profile snapshots. Three weather-dependent models are used to create the hourly wind power productions, solar power productions, and dynamic line ratings respectively. The actual historical weather information is also provided along with this dataset, which is suitable for machine learning models. Security-constrained unit commitment is conducted on TX-123BT daily grid profiles and numerical results are compared with the actual Texas system for validation. The created hourly DLR profiles can cut operating cost from USD 8.09 M to USD 7.95 M (-1.7 %), raises renewable dispatch by 1.3 %, and lowers average LMPs from USD 18.66 to USD 17.98 /MWh (-3.6 %). Two hydrogen options -- a 200 MW dual hub and a 500 MW hydrogen-energy transmission and conversion system -- reduce high-load Q3 daily costs by 13.9 % and 14.1 %, respectively. Sensitivity tests show that suppressing the high-resolution weather-driven profiles can push system cost up by as much as 15 %, demonstrating the economic weight of temporal detail.