Distributed Solar Technoeconomic Agent Characteristics for U.S. Counties

Comprising key techno-economic metrics for modeling distributed solar adoption, derived from the NREL dGen model. It represents each county and sector in the continental United States as a single weighted-average agent based on 2018 simulation assumptions.

Use Cases

• Analyze county-level Capacity Factor variations to identify high-potential regions for distributed solar investment.
• Compare techno-economic metrics across residential, commercial, and industrial sectors for policy and market analysis.
• Use Agent-based modeling outputs to calibrate or validate other distributed energy adoption models.
• Assess the impact of Roof suitability proxies and other agent characteristics on modeled solar adoption rates.

Strengths

• Data is derived from the NREL dGen model, a recognized tool for distributed generation analysis.
• Provides a summarized agent representation for every county in the continental United States.
• Simulation uses the NREL 2018 Standard Scenario Mid Case assumptions, providing a consistent baseline.

Limitations

• The underlying simulation was conducted in 2018, so techno-economic assumptions may be temporally stale.
• Data is aggregated to a single weighted-average agent per county and sector, limiting granular individual agent analysis.
• The BIN file format may require specific tools or libraries for access and processing.

Provenance

Source

Department of Energy, National Renewable Energy Laboratory (NREL).

Collection Method

Derived from outputs of the NREL dGen agent-based modeling simulation.

Time Range

Simulation based on 2018 assumptions.

Freshness

The dataset metadata was last updated in March 2026, but the simulation data is from 2018.

Geography

Continental United States, at the county level.