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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool (SoDa) v0.1

SoDa is an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant's 30-min power output, based on the configuration of the panel. We use a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, with parameters for the cloudy states trained on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU).

Carreno, IgnacioLosada↗
DOE OSTI2022

National Community Solar Partnership Credit Ready Solar Initiative

This fact sheet discusses the Credit Ready Solar Initiative, which will leverage working groups, technical assistance, training, and strategic partnerships to build a pipeline of credit-ready community solar projects and broad access to a community solar marketplace that connects owners, developers, lenders, and philanthropy to support more equitable deployment of community solar.

solar, solar energy, community solar, national com↗
DOE OSTI2021

Solar Energy Technologies Office Workforce Request for Information and Convenings (Summary)

On May 4, 2021, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) published a Request for Information (RFI) on programs that support the development of a diverse and skilled clean energy workforce. The purpose of the RFI was to solicit feedback from industry, academia, government agencies, worker organizations (including unions), and other stakeholders on issues related to the employment needs of the solar industry, and the perceived value of different workforce development programs, training strategies, and tools. To supplement the RFI, SETO hosted four virtual convenings that brought together the utility-scale solar industry, the distributed generation solar industry, and labor and other workforce training organizations to hear direct feedback on the questions in the RFI. In addition, SETO held listening sessions with about a dozen other organizations and staff who could not participate in the virtual convenings. Altogether, SETO received 45 responses from the RFI and heard directly from 80-100 other stakeholders via the convenings and listening sessions. This document summarizes the stakeholder feedback that SETO received as a result of this process. While both the RFI and convening series were focused on solar deployment and solar industry members, much of this information is relevant across clean energy technologies and programs. It is important to recognize that DOE is intentionally reviewing our workforce development programming and support to focus on clean energy careers more holistically.

14 SOLAR ENERGY↗
DOE OSTI2023

CyberStrike STORMCLOUD

This presentation will be shared at the "Securing Solar for the Grid (S2G)" workshop at RE+ 2023. It provides an overview of the work INL and Sandia have done to develop CyberStrike STORMCLOUD, a training program was designed to enhance the ability of renewable energy and operators to prepare for a cyber incident impacting industrial control systems with specific considerations of the architectures and limitations of renewable energy.

14 SOLAR ENERGY↗
DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗
DOE OSTI2023

High-Resolution Synthetic Solar Irradiance Sequence Generation: An LSTM-Based Generative Adversarial Network

The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources' primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy Laboratory. The results of the case studies demonstrate the proposed approach's capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.

dynamic scheduling↗
DOE OSTI2023

Blackfeet Community College Renewable Energy Project

The Blackfeet Community College (BCC), under Topic Area 1.a. Energy Generating System(s), received funds to install solar photovoltaic (PV) systems to support four existing campus buildings. Blackfeet Community College seeks to bring renewable energy benefits to the Blackfeet Reservation Community.

14 SOLAR ENERGY↗
DOE OSTI2025

Muckleshoot Indian Tribe-Energy Deployment (MITED) Project

The Muckleshoot Indian Tribe (MIT) collaborated with our Project Partner, GRID Alternatives (GRID), to install 132 kilowatts of direct current (kW-DC) of rooftop solar on three Tribal facilities. The three facilities are the Tribe’s Youth Drop-In Center, Canoe Shed, and Water Treatment Facility. The solar PV systems were originally anticipated to offset approximately 45% of the aggregate annual electricity usage of the three buildings. A major aspect of the MIT-ED project was providing hands-on paid training to five Muckleshoot Building Maintenance workers in solar PV installations, operations, and maintenance. The scope of work aligns with the Tribe’s goals of building local capacity and providing real world work experience and potential career opportunities in solar PV to its citizens. The Building Maintenance Department committed five of its current FTE employees to the project. GRID provided guidance for the MIT project team on identifying paid trainees as well as end goals of skill development through training, including a long-term Operations and Maintenance (O&M) plan tailored to the Tribe’s goals of local capacity building and stewardship of natural resources.

14 SOLAR ENERGY↗
DOE OSTI2023

Children Of the Sun Solar Initiative (COSSI) (Final)

The Children Of the Sun Solar Initiative (COSSI) project is a pivotal step towards energy independence for the Spokane Tribe and the Spokane Indian Housing Authority (SIHA). It involves evaluating the solar potential of 32 SIHA and Tribal buildings, with a goal of achieving energy autonomy through renewable sources. By installing 652.5 kW of solar photovoltaic (PV) systems, the project significantly reduces carbon emissions, lowers energy costs, and enhances sustainability. Key Points: (1) COSSI's mission is to empower the Spokane Tribe and SIHA with clean energy solutions, aligning with their Long-Term Energy Vision. (2) The project includes solar installations on 32 buildings, generating approximately 700,000 kWh annually. (3) It also involves collaboration with SIHA's Weatherization Program and offers training opportunities for tribal members. (4) The installations aid energy efficiency and cost reduction for high-energy-use structures. (5) The initiative advances the Tribe's economic infrastructure, fostering renewable resources and sustainable practices. (6) COSSI serves as a model for leveraging federal funding and tax credits to establish clean energy projects, while generating training and job prospects. (7) The project contributes to energy independence through the adoption of evolving solar technologies. Overall, COSSI is a forward-looking effort to usher in clean energy, enhance infrastructure, and inspire similar projects across tribal and housing authorities.

14 SOLAR ENERGY↗
DOE OSTI2022

Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.

08 HYDROGEN↗
DOE OSTI2021

Direct Observation of Photoexcited Charge Carrier Dynamics and Electrostatic Fields at Photocatalytic Semiconductor/liquid Interfaces

Photoelectrochemical conversion of solar energy to fuels at semiconductor-liquid interfaces involves a complex set of processes, which include band bending, built-in electric fields, surface recombination of photoexcited carriers, and ultimately charge transfer to the ions in solution, in which the electrostatic fields within the depletion region of the semiconductor plays an important role. There are loss mechanisms associated with each of these key components in the overall photoelectrochemical conversion efficiency. Direct probe of these elementary processes is key to the rational improvement of photoeletrochemical cells and electrodes. In this collaborative project, Tianquan Lian at Emory University, Steve Cronin at University of Southern California and their students and postdoctoral fellows carried out a series of studies that are aimed at direct observation of photoexcited charge carrier dynamics at photocatalytic semiconductor/liquid interfaces. The collaborative work led to the development of transient reflectance spectroscopy as a tool for in situ probing of carrier dynamics in planar photoelectrode/electrode interface; and the demonstration that this tool can be used to study oxide protected GaP to identify key efficiency limiting factors in these photoelectrodes. This work has led to the development a tool for in situ probing of carrier dynamics in planar photoelectrodes, which will facilitate the mechanistic study of photoelectrochemical cells. This work informs the development of better photoelectrode materials for solar water splitting and CO 2 reduction. This work has contributed to the training of three PhD students in the area of photoelectrochemistry, ultrafast spectroscopy and solar energy conversion

14 SOLAR ENERGY↗
DOE OSTI2022

Community-Scale AHA Go Solar Initiative and Net Zero Initiative

The Saint Regis Mohawk Tribe is a sovereign, federally acknowledged Indian Tribe. The Tribal Council created the Akwesasne Housing Authority (AHA) by ordinance in July 1984 and has designated the AHA as its agency for purposes of administering the Tribe’s Indian Housing Block Grant under the Native American Housing and Self-Determination Act of 1996. The Go Solar project will install approximately 615 kilowatts (kW) of solar photovoltaic (PV) facilities to serve housing related buildings on the Tribe’s reservation and use net metering programs to reduce the energy costs for AHA and Tribal members by up to $\$$4,417,501 over the life of the project. AHA will initiate four (4) net zero buildings: two (2) new net zero low-income housing buildings located at Sunrise Acres III, which include the Veterans Supportive Housing Building and the Seniors Supportive Housing building, and one (1) older, existing net zero building used by the Akwesasne Boys & Girls Club (ABGC Building), and (1) Tribal Administration Building.

14 SOLAR ENERGY↗
DOE OSTI2024

Resources, Training, and Education Under the Heliostat Consortium: Industry Gap Analysis and Building a Resource Database

Concentrating solar power is not a widely deployed or known technology area, and the heliostat workforce community in the United States is currently small, with knowledge and expertise not widely available. The resource, training, and education (RTE) topic within the Heliostat Consortium (HelioCon) was established to address this. RTE encompasses resources, practices, and programs to ensure that (1) newcomers to the heliostat development community have an adequate knowledge base and training to conduct R&D efforts, (2) outsiders to the field are provided with resources and opportunities to join the workforce, and (3) the workforce community is a productive, healthy, and fulfilling environment for all workers. In the first year of the project, a roadmap study was conducted, in which the major gaps in RTE were identified by consulting experts in the industry, with the top gap being the lack of public accessibility to concentrating solar-thermal power (CSP) knowledge. Here, to address this, the HelioCon team has been developing a centralized web-based resource database, containing a reference library, educational videos, lists of components suppliers and software/metrology tools, a power tower plant database, and information on existing standards/guidelines.

14 SOLAR ENERGY↗
DOE OSTI2022

Rooftop Solar PV Quality and Safety in Developing Countries - Key Issues and Potential Solutions

To scale solar photovoltaic (PV) deployment in developing countries, the technology must be safe and reliable, meeting both customer and utility expectations. However, challenges exist in achieving these goals. Because PV systems are novel and complex, the majority of consumers are unable to distinguish between low- and high-quality systems; many may invest based on price alone. Suboptimal PV system performance and safety incidents can have downstream impacts on the solar industry and customer adoption because of unmet expectations and negative publicity. Rooftop solar system components vary in quality, and inadequate training could lead to poor installation practices. And even if inspection checklists, certification procedures, and standards are available, they may not be widely used in countries if they are not mandatory, the workforce is not aware of them, or installers lack the technical capacity to comply. Despite the numerous solar quality and safety challenges developing countries may face, lessons learned and best practices from around the world can address them.

14 SOLAR ENERGY↗
DOE OSTI2022

Solar Ready Wisconsin

Through the Solar Ready Wisconsin project, the MREA was able to facilitate collaboration between Wisconsin’s leading technical college training programs, increase student work experience opportunities, and facilitate workforce training on high impact/high visibility PV installations in Wisconsin communities. The workforce development partnership offered credit-bearing advanced installation trainings at WI technical colleges, allowing students to participate in hands on installation training opportunities. Students also participated in internships with a network of PV contractors developed through MREA’s regional network of members, contracted instructors, and program partners.

14 SOLAR ENERGY↗
DOE OSTI2024

Use of Satellite, Surface Observations and Numerical Weather Prediction Model Data to Improve Cloud Base Height and Cloud Base Vertical Velocity Estimation

Cloud base height (CBH) and cloud base vertical velocity (CBVV) are important variables that impact the overall climate in a region as they influence the formulation, longevity, and evolution of clouds. Retrieval of both parameters have long used ground instrumentation (e.g., Doppler lidar (DL), ground base radar); however, retrieving CBH from satellites is particularly challenging given that space-based instruments only observe cloud tops. In this manuscript, CBH is retrieved using a multi-linear regression equation, while CBVV used a random forests model. Both retrievals combine satellite and numerical weather prediction data. The satellite data used are the Visible Infrared Imaging Radiometer Suite imagery, while measurements of CBH and CBVV include DL and radiosonde data at the Southern Great Plains (SGP) Atmospheric Radiation Measurement observatory. Data from 83 summer days (May-August) in 2018–2021 featuring cumulus clouds forced by solar heating were examined and used to train the models, with years 2022–2023 used for validation. Various spatial domains were defined with one large (2.4° longitude by 2.0° latitude) SGP domain being split into smaller sections (smallest being 0.99° and 0.61° longitude and latitude respectably). CBH and CBVV values obtained from the DL as compared to the models show root mean square errors between 150 and 200 m, with CBVV values between 0.45 and 1 ms -1 . Finally, it was found that the CBH formulation performs well over all domains, while the CBVV retrievals become less accurate due to more turbulence being introduced into the observations as the number of DL stations decreases in the smaller domains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Reinforcement Learning for Distribution Grid Optimization (PyCIGAR) v0.1

PyCIGAR is a python software package that merges off-the-shelf reinforcement learning libraries (RLLib and Ray) with electric power distribution system simulation tools (OpenDSS and a custom power flow solver built by LBL). PyCIGAR enables the training of neural networks to optimize the behavior of different components in the electric distribution grid, such as control systems in photovoltaic rooftop solar inverters and electric battery storage systems. The software package has been used to train neural networks to update settings in photovoltaic rooftop solar inverter control systems to mitigate cyber attacks on other solar photovoltaic rooftop devices.

Arnold, Daniel↗