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Solar Integration National Dataset Toolkit

NREL is working on a Solar Integration National Dataset (SIND) Toolkit to enable researchers to perform U.S. regional solar generation integration studies. It will provide modeled, coherent subhourly solar power data, information, and tools.

Subhourly solar power research leverages NREL's extensive experience in solar power analysis, deployment, and integration. The goals of the SIND Toolkit project are to:

  • Create a new national solar database with higher temporal and spatial resolution
  • Provide public access to this data to reduce the costs and risks of integrating solar power systems into the electric power grid.

Subhourly solar power data are used in Phase 2 of the Western Wind and Solar Integration Study and the Eastern Renewable Generation Integration Study.

Variable generation integration studies have evolved from determining if high penetrations of variable generation are possible to evaluating the strategies of operating an electric power system with them. As system topology, operation practices, and electrics power markets evolve, system operators will need to rapidly simulate new conditions. The foundations of effective studies are coherent data sets (for solar, wind, and load, among others) that accurately represent system conditions.

For integration studies, solar power data must:

  • Be time-synchronized to weather conditions during each time step and at each geographic location
  • Have sufficient temporal resolution to capture site-specific solar power output ramps
  • Have appropriate spatial-temporal correlations to capture intra-plant and plant-to-plant ramping correlations
  • Have sufficient geographic resolution to represent the relative solar power injection into the power system at each location.

Related Publications

NREL Develops Sub-Hour Solar Power Data Set
This fact sheet explains how NREL data will help utilities incorporate solar energy into their electric power systems.

Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis
High-penetration renewable integration studies need high-quality solar power data with spatial-temporal correlations that are representative of a real system. This paper summarizes research that led to the development of an algorithm to generate coherent subhourly data sets that span distances ranging from 10 to 4,000 km.


Bri-Mathias Hodge