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Maclaurin, Galen

Publications and source records attributed to Maclaurin, Galen.

14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Renewable Energy Potential Model: Hawaii Geothermal Supply Curves

This dataset extends the development of the Renewable Energy Potential (reV) model to include geothermal energy, with a specific focus on Hawaii. Provided here are the results of two scenarios that were modeled for geothermal energy in Hawaii: binary enhanced geothermal systems (EGS) at a depth of 2.5 km and hydrothermal binary systems at a depth of 1.5 km. The resource data for both scenarios were derived from Lautze and Haskins (2024) using an exponential method. The PFA probability of heat map was used as a look up table for which temperature gradient to use (Lautze and Haskins, 2024). The dataset provides geospatial and techno-economic details for evaluating geothermal energy potential. It includes spatial coordinates, estimated capacity factors, developable area, resource potential, and annual energy production metrics. Economic details such as levelized cost of electricity (LCOE), site development costs, transmission costs, and fixed-charge rates are also included. The reV model, originally developed for wind and solar energy, incorporates these variables to evaluate deployment constraints related to land use, environmental and cultural factors, and grid integration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [Slides]

In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2024

Geothermal Uncertainty Representation in reV: the Renewable Energy Potential Model

We present a preliminary methodology for including geothermal resource uncertainty into the Renewable Energy Potential model, which estimates potential capacity and costs on a gridded surface at the national scale. The uncertainty outputs characterize the 10th, 50th and 90th percentile for geothermal resources using two energy capacity estimation equations. We then present a method and results that demonstrate how other geologic data layers, which may be indicative of permeability, can be used to inform the mean and standard deviation of the geothermal capacity. We demonstrate how the mean and standard deviation can be defined or partially informed by using collocated regression estimates and estimate errors, respectively . These regression results are from 36 observed geothermal power plants in the Great Basin region and are also used to benchmark the P10-P90 calculations.

exclusions↗
DOE OSTI2023

NSRDB (National Solar Radiation Database Software) [SWR-23-77]

This repository contains all of the methods for the NSRDB data processing pipeline. The NSRDB is a serially complete collection of hourly and half-hourly values of meteorological data and the three most common measurements of solar radiation: global horizontal, direct normal and diffuse horizontal irradiance.

Benton, Brandon↗
DOE OSTI2023

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗
DOE OSTI2023

Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach

In this work we develop and present a machine learning based downscaling approach using generative adversarial networks (GANs). GANs learn to distinguish the relationships between low-resolution and high-resolution simulations and generate accurate high-resolution output from low-resolution input (Stengel, Glaws, Hettinger, & King, 2020). Low-resolution numerical weather prediction (NWP) simulations at 9-km spatial and 60-minute temporal resolution were executed over Southeast Asia to provide input to the GANs model. GANs for wind, temperature, and pressure were trained on coarsened WIND Toolkit data with a diverse sampling of terrain and meteorological conditions. After training, the NWP simulations over Southeast Asia were enhanced by 3x along each horizontal spatial dimension and 4x along the temporal dimension. This novel downscaling approach generated 15-year high-resolution wind, temperature, and pressure data from January 2007 through December 2021 at multiple hub heights over Southeast Asia at 3-km spatial and 15-minute temporal resolution with a 16x reduction in compute time over standard dynamical downscaling.

17 WIND ENERGY↗
DOE OSTI2023

Exploring the Impact of Near-Term Innovations on the Technical Potential of Land-Based Wind Energy

Land based wind may play a critical role in reaching emissions reductions goals and high renewable contribution scenarios with capacity expansion modeling results estimating over 1 terawatt of land-based wind by 2035 to reach 100% clean electricity. Deployment of land-based wind at this magnitude may require significant investments in transmission infrastructure and will require significant land area for new wind and transmission. Cost reductions via technological advancements in wind turbine design, construction, and maintenance will have a major role in enabling the scale of deployment required. Since 1998, the levelized cost of wind energy has fallen by over 60% due to improvements in capacity factors, advancements in turbine controls, and cost reductions in installation, operation, and maintenance. These cost reduction pathways, generally referred to as wind technology "innovations", have enabled significant increases in the capacity and electric generation share of wind power in the United States. Nevertheless, achievements of past wind innovations have not led to widespread wind deployment outside of high wind speed geographies. This study evaluates the potential of near-term innovations to expand the geographic range of economically viable land-based wind power production in the United States. Many challenges to future deployment of wind power can be associated with increasing concentration in high-wind areas. As more wind power is deployed in these same areas, it is likely that residential and regulatory resistance to further deployment will increase, access to transmission will diminish, and options for distant companies and governments with renewable energy goals will remain limited. Therefore, this analysis aims to emphasize the potential for innovations to enable land-based wind in regions with limited wind deployment and with lower wind resource and better access to transmission.

17 WIND ENERGY↗
DOE OSTI2022

Recent Improvements in the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) has significantly evolved since the first release of the point source database in 1993. The NSRDB has been widely used by the solar energy industry to provide long-term time-series solar resource data for various project phases. The NSRDB represents the state of the art in the satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that allows improvements in accuracy with the deployment of the next-generation geostationary satellites. This poster provides an overview of (1) the improved spatiotemporal resolution; (2) on-demand services and their applications; (3) future Improvements, such as a new direct normal irradiance model and new methods to gap-fill missing data using physics-guided machine learning; (4) data quality; and (5) data dissemination.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗
DOE OSTI2022

The National Solar Radiation Database (NSRDB) Fiscal Years 2019-2021(Final Report)

The National Solar Radiation Database (NSRDB) is the leading public source of high-resolution solar resource data in the United States, with more than 166,000 users annually. This database represents the state of the art in satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that enables improvements in accuracy with the deployment of the next-generation geostationary satellites. Making the highest quality, state-of-the-art, regularly updated data sets available on a timely basis for users reduces costs of solar deployment by providing accurate information for siting studies and system output prediction, and thereby reduces levelized cost of energy. Also, high-resolution information from the NSRDB enables moving beyond levelized cost of energy when valuing the impact of renewables on the grid. Additionally, the NSRDB enables the integration of large amounts of solar on the grid by providing critical information about solar availability and variability that is used to enhance grid reliability and power quality.

14 SOLAR ENERGY↗
DOE OSTI2022

National-scale impacts on wind energy production under curtailment scenarios to reduce bat fatalities

Wind energy often plays a major role in meeting renewable energy policy objectives; however, increased deployment can raise concerns regarding the impacts of wind plants on certain wildlife. Particularly, estimates suggest hundreds of thousands of bat fatalities occur annually at wind plants across North America, with potential implications for the viability of several bat species. One approach to reducing bat fatalities is shutting down (or curtailing) turbines when bats are most at risk, such as at night during relatively low wind speed periods throughout summer and early autumn. While curtailment has consistently been shown to reduce bat fatalities, the lost power production reduces revenues for wind plants. This study conducted simulations with a range of curtailment scenarios across the contiguous United States to examine sensitivities of annual energy production (AEP) loss and potential impacts on economic metrics for future wind energy deployment. We found that AEP reduction can vary across the country from less than 1% to more than 10% for different curtailment scenarios. From an estimated 2891 gigawatts (GW) of simulated economically viable wind capacity (measured by a positive net present value), we found the mid curtailment scenario (6.0 m/s wind speed cut-in from July 1 through October 31) reduced the quantity of economic wind capacity by 274 GW or 9.5%. Our results indicate that high levels of curtailment could substantially reduce the future footprint of financially viable wind energy. In this context, future work that illuminates cost-effective strategies to minimize curtailment while reducing bat fatalities would be of value.

17 WIND ENERGY↗
DOE OSTI2022

Mexico Clean Energy Report

This report provides an assessment of Mexico's clean energy resource potential and pathways for rapidly deploying renewable energy technologies to enable Mexico to reach its goal of 35% renewable energy by 2024 within the current legal and regulatory framework. An appendix to the report includes the results of the 2024 Renewable Energy Integration Study, and technology chapters on wind, solar, geothermal, hydropower, transport electrification, and green hydrogen, detailing current state, resource potential, and deployment opportunities and recommendations. Additional sections include transmission, a section on the challenges and solutions for the integration of variable generation resources, and a summary of benefits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
DOE OSTI2022

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗