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Virtual Trainings are the online version of the multi-day workshops known as In-Plants (INPLTs) offered by the DOE Better Plants program. ORNL has a 6-session weekly training on Renewable Energy Contracting Options and RECs starting August 5th 2025 (10 am ET) focused on options and resources for the manufacturing sector. This session is earmarked for Tuesday, August 26th, 10 AM ET.
Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.
Researchers in renewable energy are applying deep learning (DL) to a variety of problems from diverse renewable energy domains, such as biofuels, wind, solar, power systems, buildings, vehicles, and transportation systems. Improvements in accuracy may be demonstrated using DL in laboratory settings. However, the lack of interpretability of DL models poses a practical limitation to their utility in advancing scientific knowledge and in the deployment of DL models in safety-critical energy systems. In this article, we discuss explainable artificial intelligence (XAI) as one pathway toward more interpretable DL models. We explore a brief timeline of U.S. national laboratory interest in XAI, an overview and taxonomy of methods in the field of XAI, and a selection of applications across renewable energy research domains. We conclude by highlighting pivotal areas where XAI can accelerate innovation in artificial intelligence for renewable energy research and other essential future directions.
Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.
Toyota has announced plans for commercial fuel cell vehicle deployment in 2015. To fully realize the benefits of fuel cell vehicles (zero emission with no performance loss in terms of vehicle range and capability), hydrogen produced efficiently from renewable sources is necessary. Most of the hydrogen fueling stations today utilize hydrogen reformed from natural gas (produced onsite or delivered). This enables more stations to be deployed cost-effectively within a network. Producing and using cost-effective renewable hydrogen in fuel cell vehicles will enable realization of the full potential. A viable option of green hydrogen that reliably delivers on the full suite of benefits for Toyota fuel cell vehicle drivers is needed. NREL is in a unique position to analyze and optimize renewable hydrogen production scenarios using the Energy Systems Integration Facility (ESIF), a facility that is specifically designed to evaluate renewable energy integration technologies. As the U.S. Department of Energy's (DOE) primary national laboratory for renewable energy and energy efficiency research and development, NREL has extensive knowledge of photovoltaic systems as well as alternative renewable technologies for efficient and reliable production of green hydrogen.
The SmartFlower Renewable Energy Programming project brings renewable energy education to K-12 youth in the region. The motivation of the project was to pair an educational curriculum with Girl Scouts of the Colonial Coast’s newly acquired SmartFlower. A SmartFlower is a solar energy device shaped like a flower in which each petal is a solar panel. The ‘stem’ of the SmartFlower rotates throughout the day, so the petal panels follow the sun’s path and maximize sunlight absorption. The goal of the project was to create an educational curriculum for K-12 youth that centered the SmartFlower and other renewable energy related topics. The Girl Scouts of Colonial Coast team created two educational curriculums; each designed for a specific age group (K-5 and 6 th -12 th grade). Both curriculums walk participants through hands-on activities about the sun, renewable energy, and sustainability. The project adds to educational opportunities in the region and for Girl Scouts. As renewable energy becomes more prevalent and essential, youth must be informed on its’ mechanics and application. The project provides an age-appropriate educational tool that welcomes youth to visit the SmartFlower, understand its larger purpose, and consider the importance of renewable energy. The project was achieved on a modest budget of $16,672 and its sustainability beyond the project period has low to no cost for participants. The curriculum is free and accessible on our website and can be used as an evergreen educational tool. The SmartFlower Renewable Energy Programming project benefits the public as it provides a free, easily accessible education tool to children, families, caregivers, and local educators that is scientifically based and relevant to our energy landscape.
As countries accelerate their energy transitions, understanding how renewable energy (RE) systems structurally integrate into national economies is essential. This study presents a longitudinal economic input-output (EIO) analysis of the renewable energy sector in South Korea from 2016 to 2022. We develop a novel EIO-based framework that disaggregates the RE sector both by energy source (thermal, hydro, nuclear and renewable) and by industrial function (manufacturing, generation, and services), allowing for a detailed assessment of production dynamics, value-added creation, and import dependency. By quantifying backward and forward linkages and induced economic effects, the analysis reveals persistent structural vulnerabilities in renewable manufacturing and increasing sectoral interdependencies. Results reveal that while the renewable energy sector's production and value-added shares have increased, critical segments remain highly import-dependent, particularly in equipment manufacturing. The analysis highlights systemic gaps in domestic supply chain resilience and offers sector-specific insights for reducing vulnerability and enhancing energy security. Although applied to South Korea as a case study, the proposed framework is designed to be transferable to other national contexts where renewable energy planning requires economic structural insights. The findings offer policy-relevant guidance for enhancing domestic energy resilience and aligning industrial strategy with long-term decarbonization goals.
The United States is accelerating its shift toward a renewable energy system. However, renewable resources, which harness energy from the Earth system, are susceptible to both present-day climate variability and future climate change. For example, variations in regional climate can alter renewable energy production patterns and site viability. The use of high-resolution climate model projections can therefore facilitate and may be critical to long-term planning of renewable energy investments. However, climate models must first be validated for renewable resource assessment. This research employs multiple high-spatiotemporal-resolution datasets to assess the capability of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 North American Regionally Refined Model (E3SMv2-NARRM) for predicting multi-year climatological values of solar and wind energy capacity factors in the continental U.S., with a focus on regional and seasonal variability. Present-day E3SMv2-NARRM simulations are compared with reported utility-scale production data obtained from the Energy Information Administration (EIA). In addition, E3SMv2-NARRM data are evaluated against non-climate benchmark models from the National Renewable Energy Laboratory, including the Wind Integration National Dataset Toolkit and the National Solar Radiation Database (NSRDB), as well as three wind energy datasets from PLUSWIND. Our analysis indicates that solar capacity factors from E3SM closely match those from the NSRDB dataset. However, both datasets tend to overestimate values by 10% in comparison to EIA data. Furthermore, biases in wind capacity factors within E3SM are notably pronounced in the West Coast regions, where the seasonal cycle diverges from EIA data.
San Diego Gas & Electric Company (SDG&E) installed America’s first and largest utility-scale microgrid in Borrego Springs in 2013. The first generation Borrego Springs Microgrid utilized diesel generators to form and stabilize the microgrid island, with support from grid-scale batteries and local solar photovoltaic (PV) generation. In this project, SDG&E in partnership with National Renewable Energy Laboratory (NREL) demonstrated through modeling, simulation and utility field testing that blackstart and islanding of the microgrid can be led with 100% renewable, inverter based resources (IBRs), to help reduce community reliance on conventional generation resources. Through equipment upgrades, grid-forming island leader capability was transitioned to a battery IBR instead of the Borrego Springs Microgrid diesel generators. A new microgrid controller was integrated to the microgrid and programmed to control and manage multiple energy storage systems. Synchrophasor and other power quality data verified autonomous, high-speed response of the IBRs through blackstart, islanding, and load step testing. Results of project field evaluations provide distribution systems operators (DSO) with increased confidence that renewable, IBR can replace traditional generators to blackstart and island microgrids and rapidly establish stable island frequency with rapid changes in peak power demand. Importantly, the project validated the integration feasibility of a distributed energy resource management system (DERMS) controller that manages multiple grid-forming and grid-following IBRs, establishing a standard design interface to reduce the complexity of integrating new DERs in the future and supporting replication by the industry. As a result of learnings in this project, SDG&E has implemented the microgrid controller strategy at multiple other microgrid sites, thereby validating the replicability of the solution. Hardware-in-the-loop (HIL) simulations including power and controller HIL hardware — along with electromagnetic transient (EMT) simulations of Borrego Springs Microgrid —informed adjustments to inverter parameters and were important to characterize the performance of the IBRs in relevant operating conditions before deployment. The EMT and HIL simulations of islanding the entire community are important contributions in providing confidence in IBR performance prior to future islanding of the community in the field. High-fidelity EMT and/or HIL simulation of IBRs can de-risk field operations, and its relevance and importance as a tool is increasing as distribution grids and microgrids become more complex and dynamic with an increasing proportion of renewable generation, distributed energy storage, and two-way power and energy flows.
This guide was developed through the U.S. Department of Energy (DOE) Energy Transitions Initiative Partnership Project (ETIPP) technical assistance (TA) projects for Nikolski and St. George. Both communities had wind turbine projects that failed due to integration and maintenance issues. Due to these failures, Nikolski and St. George sought help to investigate renewable energy alternatives and associated integration strategies, with a focus on technologies that could be easily maintained within the community. The primary objective of this guide was to interview subject matter experts and document lessons learned to address potential renewable energy and storage integration issues in remote Alaskan communities. This guide is intended to provide information to rural Alaskan communities considering renewables with the purpose of sharing best practices and case studies to facilitate the successful implementation of future renewable energy projects.
Local renewable ammonia production using electrolytic hydrogen is an emerging approach to alleviate emissions attributed to synthetic nitrogen fertilizer production while also insulating against fluctuations in fertilizer prices and mitigating transportation costs and emissions. However, replacing ammonia currently produced using fossil fuels will not be immediate. To this end, we develop a supply chain transition model, which first optimizes the design and hourly operation of new renewable ammonia facilities to minimize production costs and then optimizes the annual installation timing, production scale, and location of these new renewable facilities along with ammonia transportation to meet county resolution demands. The objective is to augment and eventually replace conventional ammonia market imports in an economically competitive manner. We performed a case study for Minnesota's ammonia supply chain and found that a full transition to in-state renewable production by 2032 is optimal. This is incentivized by the U.S. federal government's clean hydrogen production credits. This transition results in 99 % reduction in carbon intensity along with stable supply costs below $475 per metric tonne. New renewable production facilities are an order of magnitude smaller than existing conventional plants. They use both wind and solar resources and operate dynamically to minimize expensive battery and hydrogen storage capacities.
As electrification advances and Cold Climate Heat Pump technology progresses, ensuring grid stability becomes increasingly critical for effective heating in cold climates. However, natural disasters, especially during winter, pose significant threats to grid stability, impacting the reliability of air-source heat pumps. Despite these challenges, the integration of renewable energy sources and storage solutions in heating systems has not been extensively studied within the context of resilience. Here, this paper delves into the literature on renewable-powered heat pumps to assess their potential in enhancing building resilience in U.S. cold climate zones, which are particularly susceptible to extreme weather and grid disruptions. By leveraging renewable sources—solar, geothermal, and water—in conjunction with heat pump technology and supported by thermal or battery storage, this approach aims to provide a dependable solution for maintaining indoor heating during grid failures. Our analysis begins with a review of various renewable energy sources suitable for heat pumps, followed by an exploration of their application in cold climate regions across the U.S., and discussions on potential integration strategies with heat pump systems. This study highlights the advantages and suitability of solar irradiance and geothermal resources, emphasizing the importance of tailored, site-specific assessments to maximize energy efficiency and resilience. Additionally, it outlines the economic and environmental considerations necessary for implementing such systems and identifies potential challenges and areas for future research to facilitate the broader integration of renewable energy in heating solutions for enhanced resilience.
As deployment of renewable resources and storage continue to significantly grow in the coming decades, these technologies will play increasingly important roles in maintaining power systems' resource adequacy. Few analyses so far offer comprehensive comparisons of forward-looking average and marginal capacity credits of variable renewable energy and storage in the U.S. interconnections across a wide range of possible futures. To fill this research gap, we quantify the average and marginal capacity credits of solar PV, onshore and offshore wind, and batteries between 2026 and 2050 across the U.S power systems to examine the temporal trends, spatial patterns, and trade-offs between these two capacity accreditation approaches. Across technologies, capacity credits of solar PV most clearly follow downward trends over time, reflecting the significant rise in solar PV generation share as the grid decarbonizes. While battery storages' generation shares also rise significantly over time, their capacity credits always remain stably high due to their capabilities to be dispatched strategically during critical periods to maintain reliability. On the other hand, capacity credits of wind technologies in general follow slight upward trends as their generation shares level off. There are strong spatial variabilities of both average and marginal capacity credits across technologies, but capacity credits of solar PV displaying the most obvious spatial patterns with high capacity credits concentrating in wind-rich, solar-poor regions in SPP, PJM, and MISO, suggesting potential reliability benefits of interconnection-wide planning for renewable energy deployments. Additionally, except for offshore wind, average capacity credits of all other renewable technologies tend to be higher than their marginal capacity credits, indicating that existing renewable resources tend to be accredited higher than new resources at almost any time.
In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals, multiple computational frameworks are used to locate suitable land for renewable infrastructure development, and to quantify spatiotemporal variations in the potential energy generation and financial viability of development sites across the Unites States. First, the Oak Ridge Siting Analysis for power Generation Expansion tool (OR-SAGE) is used to assess the suitability of land for potential wind and solar energy development across the contiguous U.S. From there, resource data from the National Solar Radiation Database (NSRDB) and the Wind Integration National Dataset (WIND) are used in concert with the National Renewable Energy Laboratory (NREL) Renewable Energy Potential (ReV) model to calculate the variation in potential generation capacity for each resource. Additionally, the capital and operational expenditures are calculated for an example configuration of each renewable technology. These measures are then used to calculate the levelized cost of energy (LCOE) of potential sites. All of these results are then processed and analyzed to determine where in the U.S. solar and wind energy are most viable. This viability is based on available generation potential, consistency and stability of energy generation over time, and economic viability with respect to LCOE.
Renewable hydrogen is receiving increasing attention for its potential as a flexible energy carrier in sectors such as transportation and industry. Specific cost and carbon intensity (CI) of renewable hydrogen production vary largely based on the location, owing to differences in renewable energy resources, as well as the supply chain dynamics. This study maps the techno-economic and life cycle greenhouse gas emissions of renewable hydrogen production in the Middle East and North Africa region, leveraging abundant solar and wind resources. The work investigates the variability in hydrogen costs and CI, optimally sizing proton-exchange membrane (PEM) electrolyzers to account for partial and cyclic loading, and explores standalone versus grid-connected systems. PEM capacity ratios of 52 %–63 % for photovoltaic (PV) systems and 28 %–82 % for wind systems were identified as optimal, with hydrogen production costs ranging from $\$3.8$-$\$4.8$/kg for PV and $2.0-$7.0/kg for wind. CIs span from 1.9 to 3.7 kg CO 2 ,eq /kg H 2 for PV and 0.4–7.7 kg CO 2,eq /kg H 2 for wind systems. The study highlights significant cost and CI reductions achievable with technological advancements and co-product revenue from oxygen and excess electricity sales.
Environmental pollution, water scarcity, resource shortage, and fossil fuel reliance have all represented threats to a sustainable future. Separation processes play a pivotal role in improving sustainability in fields such as industrial processes, resource recycling, wastewater treatment, and water desalination, among others. Electrochemical systems have gained increased attention as effective separation platforms, not only in performance but also as a potentially energy-efficient approach. However, the reliance on non-renewable energy sources, such as fossil fuels, for electricity generation limits the advancement toward a fully sustainable separation process. Integration of separation processes with eco-friendly renewable energy sources can increase overall sustainability and decrease carbon footprint. Here, in this review, we provide an overview of electrochemical separations and recent efforts to integrate them with renewable energy sources such as heat and solar energy. We discuss electrochemical separations, including electrosorption and electrodialysis, and research to transition these processes to thermoelectrochemical (TEC) and photoelectrochemical (PEC) platforms. Finally, we discuss current challenges and future prospects in direct integration of renewable energy sources with separation processes.
Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.
We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Renewable Energy Laboratory's (NREL) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.