A data-driven characterization of commercial electricity tariffs for building energy optimization

Electricity tariffs define the economic objective of demand-side building optimization, yet no standardized characterization of the commercial tariff population exists to guide their selection. Individual studies therefore adopt a single, locally available rate without situating it within the tariff population, which limits the interpretability and comparibility of teh reported outcomes. This gap is most consequential for commercial customers, whose bills are dominated by demand charges and contractual rules. In this study, commercial electricity tariffs are characterized at the population scale using 13,370 active plans from the U.S. Utility Rate Database, and their heterogeneity is summarized through complementary analyses of temporal segmentation and pricing structure that yield representative schedule templates and a compact set of pricing archetypes while retaining the filed rate structures for downstream analysis. The archetypes then serve as the tariff input to an open-source techno-economic optimization of battery storage for a standardized commercial building in four U.S. climate zones, and the cost-optimal decisions are compared under an identical building load. In the case study, the hourly rate structure alone moves the optimal battery power and the year-one bill reduction across factors of 3.3 and eight, respectively, and the average price level fails to order the outcomes that the structural families organize. An arbitrary choice of a single tariff therefore leaves an uncontrolled degree of freedom in the reported economics, and demand-side studies should state the structural position of the adopted tariff and test the sensitivity of their conclusions across contrasting structures.