Spatiotemporal Energy Infrastructure Datasets for the United States: A Review

Understanding spatiotemporal patterns of energy infrastructure is foundational to characterizing environmental impacts and improving system resilience. We develop a systematic review of publicly available energy infrastructure datasets in the United States (US) to reveal the existing baseline data available for characterizing the energy system. Six fuel types that are used for electricity generation are examined: uranium, coal, natural gas, wind, hydropower, and solar. For each fuel, energy infrastructure data on fuel extraction, processing, storage, fuel transportation, power generation, and transmission and distribution of electricity to final energy product are reviewed. After screening, 146 unique datasets were evaluated for their spatiotemporal characteristics using a data quality assessment framework adapted for this study. The number of available datasets, their spatiotemporal resolution and coverage, the geographic extent and their completeness were found to be highly variable across the 19 different types of energy infrastructure examined. Connections between fuel supply, energy transportation infrastructure, and conversion through final energy product are not well characterized, making the construction of a complete, dynamic energy systems model challenging. Data suppliers may address this challenge by reporting supply-chain linking attributes; for example, unique identification numbers for each facility or segment of infrastructure could bridge datasets across the supply chain. Whereas government policies and reporting requirements largely dictate data format, inter-agency collaboration and harmonization of collection procedures and metadata requirements across regions could support more consistent datasets for each stage of the supply chain through power generation.