Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential

Harrison-Atlas, Dylan; Maclaurin, Galen; Lantz, Eric

doi:10.3390/en14123609

Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential

Journal Article · Thu Jun 17 00:00:00 EDT 2021 · Energies

DOI:https://doi.org/10.3390/en14123609· OSTI ID:1797534

; Maclaurin, Galen; Lantz, Eric

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1797534

Alternate ID(s):: OSTI ID: 1810283

Report Number(s):: NREL/JA--6A20-75390; PII: en14123609

Journal Information:: Energies, Journal Name: Energies Journal Issue: 12 Vol. 14; ISSN 1996-1073; ISSN ENERGA

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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