Withdrawn - 04. Statistical Framework for Spatial and Economic Prioritization of Distributed Wind

This study presents a statistically rigorous framework for assessing where distributed wind (DW) energy can most effectively reduce household energy burdens in the U.S. We integrate high-resolution geospatial techno-economic data from NREL's Distributed Wind Energy Futures Study with probability distribution modeling and inferential statistics to examine links between wind potential and socioeconomic vulnerability across multiple scenarios.

First, we construct a household-level electric-only energy burden (EB) metric using public data on income and electricity spending. We validate this metric through empirical distribution comparisons against official estimates and find it exhibits right-skewed, non-normal behavior. Normality tests, Box-Cox transformations, and nonparametric methods (e.g., Kolmogorov–Smirnov) guide our statistical choices.

Second, we define a standardized wind feasibility metric−Annual Energy Production (AEP) normalized by sectoral energy demand (AEP-to-demand ratio) − to enable cross-county comparisons across time (2022, 2025, 2035) and policy (2025) scenarios. Using this and our EB metric, we conduct:

- Pairwise scenario comparisons (parametric and nonparametric).
-Correlation analysis (Pearson, Kendall).
-Linear mixed-effects modeling with state as a random effect and demographic/economic covariates as fixed effects.
-Rank-based geographic comparisons using Spearman's ρ.

To enhance interpretability, we apply a Net Energy Return (NER) indicator−an algebraic function of energy burden−and extend both EB and NER to county-level values normalized by local GDP.

Results show significant correlations between AEP-to-demand ratio and EB, especially in Georgia, Louisiana, and Colorado. Agricultural employment and poverty rate emerge as key burden predictors. This framework informs spatially targeted DW deployment strategies.

Distributed Wind

Energy Burden

Nonparametric Methods

Mixed Models

Annual Energy Production 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025