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Quantifying the Solar Energy Resource for Puerto Rico Nick Grue, Grant Buster, Andrew Kumler, Yu Xie, Manajit Sengupta, and Murali Baggu

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Quantifying the Solar Energy Resource for Puerto Rico Nick Grue, Grant Buster, Andrew Kumler, Yu Xie, Manajit Sengupta, and Murali Baggu Quantifying the Solar Energy Resource for Puerto Rico Nick Grue, Grant Buster, Andrew Kumler, Yu Xie, Manajit Sengupta, and Murali Baggu National Renewable Energy Laboratory NREL is a national laboratory of the U.S. Department of Energy Technical Report Office of Energy Efficiency & Renewable Energy NREL/TP-5D00-75524 Operated by the Alliance for Sustainable Energy, LLC May 2021 This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Contract No. DE-AC36-08GO28308 Quantifying the Solar Energy Resource for Puerto Rico Nick Grue, Grant Buster, Andrew Kumler, Yu Xie, Manajit Sengupta, and Murali Baggu National Renewable Energy Laboratory Suggested Citation Grue, Nick, Grant Buster, Andrew Kumler, Yu Xie, Manajit Sengupta, and Murali Baggu. 2021. Quantifying the Solar Energy Resource for Puerto Rico. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5D00-75524. https://www.nrel.gov/docs/fy21osti/75524.pdf. NREL is a national laboratory of the U.S. Department of Energy Technical Report Office of Energy Efficiency & Renewable Energy NREL/TP-5D00-75524 Operated by the Alliance for Sustainable Energy, LLC May 2021 This report is available at no cost from the National Renewable Energy National Renewable Energy Laboratory Laboratory (NREL) at www.nrel.gov/publications. 15013 Denver West Parkway Golden, CO 80401 Contract No. DE-AC36-08GO28308 303-275-3000 • www.nrel.gov NOTICE This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office and Office of Electricity. The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government. This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov. Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, NREL 46526. NREL prints on paper that contains recycled content. Acknowledgments This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE- AC36-08GO28308. Funding and support provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office and Office of Electricity. The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government. iii This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. List of Acronyms COV coefficient of variation DISC Direct Insolation Simulation Code DNI direct normal irradiance FARMS Fast All-Sky Radiation Model for Solar Applications GHI global horizontal irradiance IDW inverse distance weighted LCOE levelized cost of energy MBE mean bias error MERRA-2 Modern-Era Retrospective Analysis for Research and Applications, Version 2 NREL National Renewable Energy Laboratory NSRDB National Solar Radiation Database PSM Physical Solar Model RMSE root mean square error SAM System Advisor Model SURFRAD Surface Radiation Budget Network iv This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. Executive Summary After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Database (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties that were then used to compute solar radiation. The high-resolution data sets were validated against ground measurements, and the error metrics were found to be similar to the original lower resolution data set. Using 20 years of downscaled data from the NSRDB (1998-2017), multiyear capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multiyear data provides the ability to understand variability in capacity factors as a result of variability in weather during a long period of time. For Puerto Rico, the coastal regions were found to have significantly higher capacity factors than inland. Using land-use and terrain information, a technical potential analysis for utility-scale PV plants was conducted for Puerto Rico. This analysis restricted single modeled PV plant development to a maximum nameplate capacity of 100 MW. The nameplate capacities for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a single modeled PV plant allowed was 10 MW. The maximum total PV capacity within a county was 2,000 MW. Further, a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to far exceed the maximum peak load for the island; however, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first, and it ignores the complex decision paths for new power plant development; therefore, this analysis can be treated only as illustrative. Finally, this study investigates the impact of interannual variability of the resource using a variety of metrics, including the probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by more than 10% from one year to another. This clearly indicates the risks involved in using any particular year of v This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. data and clearly points to the use of multiyear data to reduce some of the risks related to variability in weather. vi This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. Table of Contents 1 Technical Potential and Supply Curve Analysis ............................................................................... 1 1.1 Technical Potential ........................................................................................................................ 1 1.1.1 Introduction ...................................................................................................................... 1 1.1.2 Calculating Generation Output ......................................................................................... 2 1.1.3 Assumptions and Data Inputs ........................................................................................... 3 1.1.4 Technical Potential Methodology .................................................................................... 4 1.1.5 Technical Potential Results .............................................................................................. 6 1.2 Supply Curve Analysis .................................................................................................................. 7 1.2.1 Transmission Line Routing .............................................................................................. 8 1.2.2 Cost Assumptions ............................................................................................................. 8 1.2.3 Supply Curve Results ....................................................................................................... 9 2 Interannual Variability ........................................................................................................................ 11 2.1 General Variability of the Solar Resource .................................................................................. 11 2.2 General Variability of the Solar Generation ................................................................................ 15 2.3 General Variability of the Levelized Cost of Energy .................................................................. 20 3 Conclusion .........................................................................................................................................
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