Grid Data and Tools

NREL develops data sets and tools to support the integration of distributed and renewable energy into the electric power system.

Grid Tools


ALSAT creates load profiles for individual customer nodes with realistic diversity and variability to support accurate time-series analysis for interconnection studies.


CYMEpy provides users with Hierarchical Engine for Large-scale Infrastructure Co-Simulation bindings for a popular distribution system simulator.


Flexible Energy Scheduling Tool for Integrating Variable Generation simulates the electric power system to help researchers understand the impacts of variability.


The Object-Oriented Controllable High-Resolution Residential Energy Model allows researchers to study emerging technologies in residential buildings.


OptGrid autonomously optimizes power flow between distributed energy resources to bring real-time management to the grid edge.


PyDSS supports a distribution system simulator by expanding on its organizational, analytical, and visualization capabilities.


PyPSSE supports the Power System Simulator for Engineering by allowing users to perform time series power flow and dynamic simulation for power systems.


PREconfiguring and Controlling Inverter SEt-points helps utilities quickly establish optimal inverter settings for distributed solar energy.


The Scalable Integrated Infrastructure Planning Model addresses the scheduling problems and dynamic simulations of quasi-static infrastructure systems.

Grid Data Sets


The Reliability Test System–Grid Modernization Lab Consortium is a modernized, medium-scale test data set with many features of modern electric power systems.


Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios offers high-quality distribution network models and advanced tools to generate model scenarios.

Test Case Repository for High Renewable Study

The repository contains open-source test cases, models, and datasets to help power system researchers evaluate ideas for a renewable-rich future.