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Chapter 9 Supplementary material provides examples of social and economic data and information for baseline data collection and development/operational data collection.
The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.
Commercial-scale marine renewable energy (MRE) developments continue to progress slowly, in part because of complicated consenting/permitting (hereafter consenting) processes that invoke the precautionary principle within environmental legislative frameworks. This can lead to broad, poorly scoped environmental assessments, lengthy and expensive environmental data collection requirements, and extended consenting timelines. Much of this delay is associated with uncertainty about the potential effects of MRE on marine animals and habitats. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-13-risk-retirement
The social and economic effects of marine renewable energy (MRE) are a necessary consideration for the consenting/permitting (hereafter consenting) of projects (including planning, siting, and project design) and for strategic planning processes. Social and economic effects can include impacts on people, communities, jobs, wages, and revenues. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-9-social-economic
This fact sheet describes the development and release of a high-fidelity solar time series data set for the entire Southeast Asia region, which can be used to drive energy sector investment, support enabling policies to support solar energy market expansion, and inform prospecting, renewable energy auctions, target-setting, grid integration and other high impact decisions, ranging from the micro to macro scale, to accelerate renewable energy deployment throughout SE Asia. This data is available on the enhanced and expanded RE Data Explorer (RE Explorer) platform.
This presentation provides an overview of renewable energy levelized cost of energy using geospatial calculations and maps.
Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.
High quality, robust, and reliable renewable energy resource data is foundational to climate-smart decision making, evidence-based policy planning, and clean energy investment mobilization. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to this critical resource data by providing free, high-fidelity solar resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights several ways the Southeast Asia solar resource data has been used for power system planning and project development in the region.
This dataset includes modeled velocity and discharge at five communities in the middle Kuskokwim River region: Aniak, Chuathbaluk, Crooked Creek, Red Devil and Stony River. Modeled velocities and discharge represent daily averages calculated for the openwater season (OWS) from June 1 - October 18 over the 20 year period 2000-2019 using the raw data described below and included in this archive; full details of methodology are described in (Brown et al. submitted to Renewable Energy). Raw data inputs to inform the modeling process include in-situ measurements of 1) discharge with an acoustic Doppler current meter (ADCP, 600kHz Workhorse Rio Grande by Teledyne RD Instruments) and a global positioning receiver (GPS, Trimble 5700, 5800 and R8) utilizing Real Time Kinematic (RTK) GPS mode over 1-2 days at each site in 2009 or 2010 (Ravens 2014), and 2) river stage with a water level logger (HOBO U20-001-01 by Onset) over 2-9 weeks at each site (Ravens 2014), 3) in addition to a 20 year long-term discharge record collected at the USGS stream gage site in Crooked Creek (USGS 2016). Raw data (discharge and stage) are included in this archive for two additional communities: Lower Kalskag and Sleetmute, where modeled velocities were not calculated due to equipment failure or loss. The USGS stream gage data at Crooked Creek (USGS 2016) and stream gage methodology (Turnipseed and Sauer 2010) are publicly available online, so the data are not duplicated here.
The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) comprehensively analyzes possible pathways for Puerto Rico to achieve its renewable energy goals while incorporating stakeholder perspectives and advancing energy resilience for all Puerto Ricans. PR100 is a wide-ranging and in-depth examination of Puerto Rico's energy system investment options. The findings are the culmination of two years of stakeholder engagement, scenario modeling, and impact analysis. The PR100 report and Implementation Roadmap contain a range of results and actions that reflect Puerto Rico's priorities around energy justice, resilience, and reliability. Led by the U.S. Department of Energy's Grid Deployment Office with funding from the Federal Emergency Management Agency, the PR100 study leveraged and integrated dozens of best-in-class models and in-depth analyses from researchers across six national laboratories: National Renewable Energy Laboratory (which led the study), along with Argonne National Laboratory, Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Sandia National Laboratories (which conducted the study). For more information, please see the "PR100 Project Website" resource below.
ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Additionally, gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.
Realistic data-driven renewable energy scenarios for stochastic grid operation problems
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NREL's advanced distribution management system (ADMS) research helps utilities meet customer expectations of reliability, power quality, renewable energy use, data security, and resilience to natural disasters and other threats.
Southeast Asia (SE Asia) is a region with growing energy demand and increasing development of floating solar photovoltaic (FPV) systems, which can help meet countries' renewable energy and energy security goals. This study uses a high-level geospatial assessment methodology to estimate the technical potential for monofacial and bifacial FPV on reservoirs and natural waterbodies in the ten countries within the Association of Southeast Asian Nations (ASEAN). Technical potential consists of the suitable waterbody area for FPV development (km2), the capacity of FPV that could be installed on this suitable area (MW), and the annual energy that could be generated from these installations (GWh/year). This first-of-its-kind FPV technical potential assessment for SE Asia can help policymakers and planners better understand the role that FPV could play in meeting regional energy demand and can ultimately guide investment decisions. Although this work focuses on SE Asia, the methodology may also be applicable for countries in other regions, with adaptations. The FPV technical potential results are also integrated into the Renewable Energy (RE) Data Explorer online tool (https://www.re-explorer.org/).
NREL's advanced distribution management system (ADMS) research helps utilities meet customer expectations of reliability, power quality, renewable energy use, data security, and resilience to natural disasters and other threats.
As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.
This fact sheet outlines the basic overview features, purpose, and additional information for the State and Local Planning for Energy (SLOPE) Platform. SLOPE is a collaboration between nine U.S. Department of Energy (DOE) technology offices and the National Renewable Energy Laboratory (NREL), integrates and delivers jurisdictionally resolved potential and projection data on energy efficiency, renewable energy, and sustainable transportation in an easy-to-access online platform to enable data-driven state and local energy planning and decision-making.