Estimating the technical wind energy potential of Kansas that incorporates the atmospheric response for policy applications

TL;DR

This study introduces the KEBA approach to better estimate Kansas's wind energy potential by accounting for atmospheric kinetic energy removal, revealing significant overestimations in standard methods and emphasizing the importance of stability effects.

Contribution

The paper presents the KEBA method, a novel approach that incorporates atmospheric kinetic energy removal effects into wind resource assessments, improving accuracy over traditional observed wind speed methods.

Findings

KEBA reproduces simulated estimates within 10-11%

Standard approach overestimates potential by 30-50%

Daytime and nighttime wind depletion differ due to stability effects

Abstract

Energy scenarios and transition pathways need estimates of technical wind energy potentials. However, the standard policy-side approach uses observed wind speeds, thereby neglecting the effects of kinetic energy (KE) removal by the wind turbines that depletes the regional wind resource, lowers wind speeds, and reduces capacity factors. The standard approach therefore significantly overestimates the wind resource potential relative to estimates using numerical models of the atmosphere with interactive wind farm parameterizations. Here, we test the extent to which these effects of KE removal can be accounted for by our KE Budget of the Atmosphere (KEBA) approach over Kansas in the central US, a region with a high wind energy resource. We find that KEBA reproduces the simulated estimates within 10 - 11%, which are 30 - 50% lower than estimates using the standard approach. We also evaluate important differences in the depletion of the wind resource between daytime and nighttime conditions, which are due to effects of stability. Our results indicate that the KEBA approach is a simple yet adequate approach to evaluating regional-scale wind resource potentials, and that resource depletion effects need to be accounted for at such scales in policy applications.