Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50× resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

NREL prints on paper that contains recycled content.

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NREL prints on paper that contains recycled content.

This report is one in a series of Electrification Futures Study (EFS) publications. The EFS is a multi-year research project to explore widespread electrification in the future energy system of the United States. This report documents a new model, the demand-side grid (dsgrid) model, which was developed for the EFS and in recognition of a general need for a more detailed understanding of electricity load. dsgrid utilizes a suite of bottom-up engineering models across all major economic sectors-transportation, residential and commercial buildings, and industry-to develop hourly electricity consumption profiles for every county in the contiguous United States (CONUS). The consumption profiles are available by subsector and end use as well as in aggregate. This report documents a bottom-up modeling assessment of historical ( 2012) consumption and explains the key inputs, methodology, assumptions, and limitations of dsgrid.The EFS is specifically designed to examine electric technology cost advancement and adoption for end uses across all major economic sectors as well as electricity consumption growth and load profiles, future power system infrastructure development and operations, and the economic and environmental implications of electrification. Because of the expansive scope and the multiyear duration of the study, research findings and supporting data will be published as a series of reports, with each report released on its own timeframe. Future research to be presented in future planned EFS publications will rely on dsgrid to analyze the hourly electricity consumption under scenarios with various levels of electrification. In addition to providing electricity consumption data for the planned EFS analysis, dsgrid can be used for other analysis outside the EFS research umbrella.More information and the supporting data associated with this report, links to other reports in the EFS study, and information about the broader study are available at www.nrel.gov/efs.

With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Peak Reliability to evaluate the impact of the August 21, 2017, total solar eclipse on reliability and electric grid operations in the Western Electricity Coordinating Council (WECC) territory. As a North American Electric Reliability Corporation Reliability Coordinator, Peak Reliability manages the grid reliability across all or parts of the14 western United States, British Columbia, and the northern region of Baja California, Mexico. Peak Reliability used inputs from the study to augment its own analysis for preparing transmission operating plans for the day of the eclipse. Peak Reliability also provided NREL with data from across its footprint to uncover important insights into the impact of the eclipse from the penetration of photovoltaics (PV) along its path. The 2017 total solar eclipse came and went without causing any issues to the operation of the North American electric power system. This report presents the results of NREL's studies performed before and after the August 21 event. In a preevent analysis, NREL researchers focused on the spatial and temporal profile of the eclipse and how the output of PV sites across WECC would be affected. Although the size and location of utility-scale PV (UPV) sites are known and their outputs are monitored, distributed PV (DPV) (e.g., rooftop solar) poses a greater challenge for grid operations because of its lack of visibility. The estimated installed capacity of UPV and DPV across the WECC region is 15,800 MW (without concentrating solar power plants) and 9,200 MW, respectively. NREL used databases developed through previous efforts funded by the U.S. Department of Energy to determine how much DPV was present at the substation level and then estimated the impact of the eclipse on its output. This geospatial analysis assumes a typical meteorological day from NREL's typical meteorological year database. NREL researchers estimated that during the peak of the event, the loss of PV would be around 5.2 GW, with UPV loss at 4 GW and the rest from DPV. The second part of the analysis was a simulation of production costs. Using the results from the geospatial study, the economic dispatch and unit commitment strategies were evaluated to see how the other generators would be redispatched. The analysis showed that the loss of PV generation can be easily compensated by the existing generation fleet, with the natural gas fleet picking up the majority of the slack because of its flexibility. The change in system production costs were minimal. The third part of the analysis consisted of transient simulations to assess the stability and reliability of grid operations. No reliability concerns were identified, even for large system disturbance events. The system also exhibited high damping capabilities to mitigate inter-area oscillations.

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