The industrial sector in the United States consumes over 20% of the total electricity generated and growth in the sector could further increase its electricity demand and operational expenses. A well-resolved dataset of representative load profiles for industrial subsectors could provide a better understanding of their consumption patterns to stakeholders, and uncover opportunities to reduce these expenses for industrial customers. Therefore, in this study, we present Simulation of Industrial Electrical Load Shapes (SIELS), an empirical model that was developed to characterize hourly load profiles of seven energy-intensive industrial subsectors across the contiguous U.S. for the year 2018. Specifically, we analyzed the meter data from industrial customers in California to identify the most commonly occurring temporal patterns. We then developed a methodology to sample daily load shapes from this dataset and pair it with outputs from spatial datasets including the IGATE-E tool to develop hourly load profiles for industrial electricity demand. This marks the beginning of efforts to understand the heterogeneity of industrial loads and their operations. The results from the simulation are characterized using two metrics to analyze their hourly variability. In order to demonstrate a use case for this dataset, we explored the value of industrial load flexibility using broad-based assumptions for load shed. Our results indicate that industrial load flexibility could provide a significant amount of savings in capacity and billions of dollars in system costs.
