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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Batteries Included: Top 10 Findings from Berkeley Lab Research on the Growth of Hybrid Power Plants in the United States

One of the most important electric power system trends of the 2010s was the rapid deployment of wind turbines and photovoltaic arrays, but a twist for the 2020s may be the rapid deployment of ‘hybrid’ generation resources. Hybrid power plants typically combine solar or wind (or other energy sources) with co-located storage. While hybridization helps to ease the challenge of balancing variable supply and demand, its relative novelty means that research is needed to facilitate integration and promote innovation. Combining the characteristics of multiple energy, storage, and conversion technologies poses complex questions for grid operations and economics. Project developers, system operators, planners, and regulators would benefit from better data, methods, and tools to estimate the costs, values, and system impacts of hybrid projects. This publication showcases some of Berkeley Lab’s robust research program intended to support private- and public-sector decision-making about hybrid plants in the United States. Our short briefing summarizes articles that we published between 2020 and 2022, links to the in-depth reports, and provides contact details for further engagement on the specific research topics: Growth: Developer interest in hybrid power plants is strong and growing Price vs. Value: PV+storage hybrids have low PPA prices and high value in some regions Market Drivers: Solar hybridization is driven by tax credits and other benefits Configuration Choices: Market prices have incentivized shorter duration batteries with PV Capacity Value: The capacity contribution of a hybrid is less than the sum of its parts Ancillary Services: AS markets are a valuable yet fleeting option for hybrids Market Participation: Hybrids can more flexibly engage with electricity markets Operations: The power system value of hybrids depends on how they are operated Distributed Hybrids: Growth of customer-sited PV+storage hybrids offers new opportunities Future Research: Where next? Priority areas for hybrid power research.

25 ENERGY STORAGE↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2025

High-power test of a C-band linear accelerating structure with an RFSoC-based LLRF system

Normal conducting linear particle accelerators consist of multiple rf stations with accelerating structure cavities. Low-level rf (LLRF) systems are employed to set the phase and amplitude of the field in the accelerating structure and to compensate for the pulse-to-pulse fluctuation of the rf field in the accelerating structures with a feedback loop. The LLRF systems are typically implemented with analog rf mixers, heterodyne-based architectures, and discrete data converters. There are multiple rf signals from each of the rf stations, so the number of rf channels required increases rapidly with multiple rf stations. With a large number of rf channels, the footprint, component cost, and system complexity of the LLRF hardware will increase significantly. To meet the design goals of being compact and affordable for future accelerators, we have designed the next-generation LLRF (NG-LLRF) with a higher integration level based on RFSoC technology. The NG-LLRF system samples rf signals directly and performs rf mixing digitally. Further, the NG-LLRF has been characterized in loopback mode to evaluate the performance of the system and has also been tested with a standing-wave accelerating structure, a prototype for the Cool Copper Collider (C 3 ) with a peak rf power level up to 16.45 MW. The loopback test demonstrated amplitude fluctuation below 0.15% and phase fluctuation below 0.15°, which are considerably better than the requirements of C 3 . The rf signals from the different stages of the accelerating structure at different power levels are measured by the NG-LLRF, which will be critical references for the control algorithm designs. The NG-LLRF also offers flexibility in waveform modulation, so we have used rf pulses with various modulation schemes, which could be useful for controlling some of the rf stations in accelerators. In this paper, the high-power test results at different stages of the test setup will be summarized, analyzed, and discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Preliminary Evaluation of Driver Fuel Plates and Reduced Sodium Temperature to Increase Fast Test Reactor Experimental Neutron Irradiation Capabilities

All sodium-cooled fast test reactors operated so far have used essentially the same fuel geometrical ar-rangement, namely cylindrical fuel pins assembled into a triangular array in hexagonal fuel assemblies. Fuel compositions and dimensions may vary but the overall geometrical pattern remains identical. Fur-thermore, most sodium-cooled reactors have operated, and still operate, with core-average inlet and outlet sodium temperatures of, respectively, 350-400°C and 500-550°C. The present preliminary study revisits the standard fuel assembly geometry as well as operating tempera-tures to evaluate potential impact on fast test reactor neutron irradiation capabilities. The reference fuel is U-20Pu-10Zr. Pin (reference) and plate geometries are considered together with two sets of core-average sodium inlet/outlet temperatures: 350/500°C (reference) and 200/400°C. More prototypic environments could be obtained inside ad-hoc irradiation testing vehicles that are independent from the reactor primary coolant. Everything else being the same, fuel temperature is significantly lower in plates than in pins. Therefore, plates could in principle accommodate higher plutonium content, thus, reducing the need for uranium enrichment or allowing the use of lower quality plutonium (U-xPu-10Zr thermal conductivity and solidus temperature decrease as x increases, hence, necessitating additional thermal margins). For the reference pin configuration, lowering the inlet/outlet sodium temperatures from the reference 350/500°C down to 200/400°C provides additional thermal margins that can be used to increase the peak fast flux from about 4.5 × 10 15 n/cm 2 -s to 6 ×10 15 n/cm 2 -s for the same core power of 300 MW. Assuming 300 Equivalent Fuel Power Days (EFPD) of operation per calendar year, a (steel) test article could accu-mulate up to 75 dpa/year. The use of fuel plates provides even more thermal margins which may allow the peak fast flux to reach values as high as 8 × 10 15 n/cm 2 -s for both sets of inlet/outlet temperatures. In this environment, a test article could accumulate up to 95 dpa/year assuming 300 EFPD/year, hence greatly accelerating irradiation testing. Allowing a fast test reactor to operate over a wide range of inlet/outlet temperatures could add significant flexibility to its neutron irradiation capabilities. Steel present in the driver fuel assemblies is not expected to accumulate more than 90 displacements per atom (dpa) over its lifetime. Hence, steel alloys that are not suited for very high dpa but can operate over a wide range of temperatures (from low to high) would be appropriate for this application. Finally, because pin and plate assemblies fit on the same grid plate, a fast test reactor could in principle start-up with standard fuel pin assemblies and, later on, move to a plate-type Mark-II fuel to further in-crease its neutron irradiation capabilities while potentially reducing the level of uranium enrichment needed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Optimising the processing and storage of visibilities using lossy compression

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.

Techniques: interferometric↗
DOE OSTI2021

Ultra-Fast Vertical Ordering of Lamellar Block Copolymer Films on Unmodified Substrates

To utilize the full potential of block copolymer (BCP) thin films for use in technological devices ranging from ion conduction membranes, transistors to nanowire array antennas, rapid self- assembly of lamellar block copolymers (l-BCPs) with vertically oriented lamellar domains on a variety of unmodified substrates is needed. l-BCPs have an inherently larger interfacial area for transport compared to their cylindrical counterpart. Our observations demonstrate that the as-cast weakly ordered vertically oriented state of l-BCP films of polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) from directional evaporation of select solvents, act as “seed templates” for their ultra-fast evolution (~30 s) into well-ordered vertically oriented nanostructures, using a thermal gradient-based Cold Zone Annealing (CZA) technique. Furthermore, vertical lamellae are obtained on unmodified substrates, Quartz and Kapton, and the kinetics of l-BCP ordering is much faster by CZA as compared to the isotropic oven annealing. The rapid ordering kinetics of vertical l-BCPs is tested and found applicable to different molecular weights and film thicknesses ranging from 20 nm to 480 nm, which ultimately flip over to their equilibrium parallel morphology at upper limits of annealing times. This rapid ordering strategy for vertical orientation of l-BCPs using roll-to-roll compatible CZA would be highly relevant for fundamental studies of interfacial transport as well as for industrial applications from membranes to nanowires.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enhanced Light Outcoupling from OLEDs Fabricated on Novel Low-Cost Patterned Plastic Substrates of Varying Periodicity

OLEDs continue to make strides in display applications, but their commercial utilization in solid-state lighting (SSL) is lagging. An ongoing challenge, in particular for manufacturing, is the need for enhanced efficiency and hence the necessity to increase in an inexpensive approach the extraction of the light generated inside the OLED into the forward (viewing) hemisphere. In conventional OLEDs fabricated on a transparent flat anode coated on glass, the external quantum efficiency (EQE) is only ~20%. About 50% of the light is lost to internal waveguiding in the high refractive index (RI) organic + ITO anode layers and to surface plasmon polaritons (SPPs) at the organic/metal cathode interface. Another ~30% of the light is externally waveguided in the substrate to its edges. While extraction of the externally waveguided light is commonly addressed by adding a microlens array (MLA) or a scattering layer at the substrate’s air-side, light outcoupling increases by only ~1.6-1.7x (vs up to 2.5x in improving from ~20% to ~50%). The use of a hemispherical lens or an index matching fluid (IMF) at the substrate/photodetector (PD) interface increases the outcoupling by at least 2x; these approaches however, are not viable industrially, and even a MLA is sometimes undesirable due to its non-planar, scattering structure. In multi-stack tandem OLEDs, where the metal cathode is far from the emitting zone(s), the impact of photons loss to SPPs decreases. Our project addressed the ~50% loss to the internally waveguided light and SPPs. We evaluated OLEDs fabricated on patterned or planarized plastic substrates manufactured in a cost-effective approach compatible with a roll-to-roll (R2R) process. The OLEDs were either (i) patterned to various degrees depending on the pitch a and height or depth h of the pattern features or (ii) planar, with a pattern buried under a flat high RI planarization layer. We demonstrated that the outcoupling from green patterned OLEDs reaches ~50% by mitigating plasmon–related loss and internal waveguiding, even without the addition of a MLA, a hemispherical lens, or IMF. Simulations conducted in parallel with the experimental effort demonstrated how diffraction by conformally corrugated OLEDs increases the outcoupling to >60%. Structures with varying pitch values were also simulated indicating that combining domains of varying pitch could increase outcoupling to 55-60%. Experimentally, we additionally assessed the role a and h in determining not only the OLED efficiencies, but also their structural properties, i.e., the uniformity and conformality throughout the OLED stack. As planar OLEDs are preferred over corrugated devices, we studied different patterns in plastic substrates that were planarized by a high RI formulation. Planar green OLEDs on such structures showed enhanced efficiencies with EQEs larger than 60% with the addition of an IMF (to extract the substrate mode) at the substrate/Si PD interface. White OLEDs showed EQEs of 45.5%. Plastic substrates are currently less attractive than glass substrates due to drawbacks such as permeability to water vapor and oxygen, and in some cases thermal instability. Plastic substrates however, are flexible and easy to handle unlike thin flexible glass, and once transparent thin barrier films are available, they will become more attractive; they are already of interest in medical applications. Importantly, as it is easy to generate various patterns in different plastic materials, they provide excellent means for assessing and optimizing enhancing extracting structures. Such structures can also be transferred to glass substrates with some process modifications. The technical effectiveness and economic feasibility of the project lie in the patterning of the extracting plastic substrates in an approach that is scalable to R2R manufacturing. R2R processes are of drastically lower-cost than batch or single-unit fabrication. The patterned plastic can be a part of an integrated substrate either plastic or glass, which includes also a MLA or a planar layer with embedded scattering particles, as well as a conductive metal mesh/electrode design. SSL is environmentally-friendly and as OLED SSL becomes more efficient it will reduce electricity consumption, and hence lighting cost, as well as produce less expensive attractive lighting fixtures. Our university-industry collaboration is hence of major benefit to the public as it demonstrates the feasibility of manufacturing optimized extracting substrates for highly efficient OLEDs for SSL in a future R2R process, which would drastically reduce the manufacturing cost and increase production in the USA. Moreover, newly developed methods by our team allow low-cost roll manufactured substrates to be transferred to flexible or rigid glass substrates, which solves the plastic substrate barrier issues, and when combined with device encapsulation will increase the OLEDs’ environmental stability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Scaling Equitable Finance

Driven by dramatic declines in up-front cost, the U.S. solar photovoltaics (PV) industry has taken off over the past decade, growing from 1 gigawatt of installed capacity in 2009 to 89 gigawatts in 2020—or enough capacity to power roughly 19 million homes. The industry is expected to double in size over just the next 5 years.1 Much of the growth has been driven by large, utility-scale projects that can produce 5 mega- watts or more of power—enough to power at least 1,000 homes. The cost of electricity produced by these projects has decreased by more than 70 percent since 2010. As of Q3 2020, development costs of large, util- ity-scale solar PV power plants were under $1 per watt, down by more than 70 percent from 2010.2 A robust array of investors has come forward to efficiently deliver capital to these kinds of utility-scale projects including large banks, insurance companies, pension funds, and others. But low- and moderate-income communities, including communities of color, are at risk of being left behind in the transition to clean energy. Mission- driven solar project developers and financial institu- tions have been working alongside energy justice advocates to open up solar access for these communi- ties, using strategies ranging from community solar, to solar installations on affordable multifamily housing, to distributed solar and storage programs, and more. Their goals go beyond simply generating more green energy to advancing social equity by: • empowering communities to control their energy future • stabilizing energy prices, saving money, and build- ing wealth for low-income families • creating quality jobs • improving health by reducing pollution • providing energy resilience for vulnerable communities Mission-driven actors are successfully deploying a wide variety of strategies to meet these goals, from helping low-income homeowners get solar—and some- times battery storage, to developing solar projects serv- ing affordable rental housing and community facilities, to building larger “shared solar” projects to which households from across the community can subscribe. However, the financing ecosystem does not work nearly as well for these “mission driven” solar proj- ects as it does for utility-scale projects. For home rooftop solar, even if low-income consumers have a home and suitable roof, they may fail to qualify for federal tax incentives, lack adequate credit to qualify for a loan—or the mission-driven lenders seeking to serve them may not be adequately capitalized to make long-term loans. For mission-driven commercial or community-scale projects, assembling nearly every component of the project capital stack—whether bridging early-stage costs, attracting tax credit equity investors, securing long-term debt, or coming up with sponsor equity and filling gaps—can present challenges. A variety of obstacles contribute to the scarcity of financing for low-income solar, including small project sizes, lack of developer balance sheet capacity, both real and perceived issues with credit risk, elevated technical assistance needs, and greater subsidy requirements to pursue goals such as deep energy affordability, climate resilience, or job creation. Still other obstacles are regulatory: for example, not all states allow community solar projects or Power Purchase Agreements, common strategies used for providing low-income solar—and the potential for regulations to shift over time creates risks that mission-driven projects can ill afford. This report synthesizes information garnered from 47 key informant interviews, four focus group discus- sions involving 60 stakeholders, and a review of the substantial existing literature on low-income solar finance to assess the current landscape of mission- driven solar development in the United States, examine the roles that community-based financial institutions could play, and recommend public invest- ments and policy changes that could help to scale the provision of equitable solar finance. Key recommen- dations for policymakers and funders in the renew- able energy and community development fields that emerge from this process include the following: • Help to capitalize and support community-based lenders to provide flexible, low-cost, and long- term financing to mission-driven solar projects— including providing guarantees or other forms of credit enhancement. • Provide federal support for equitable solar, including a grant-in-lieu-of-credits option for the Investment Tax Credit to improve access to this critical government subsidy. • Develop pools of government and philanthropic support that can complement financing from community-based lenders to complete the capi- tal stack for mission-driven projects, as well as to support education and technical assistance to both consumers and potential project sponsors. • Create a national Renewable Energy Credits pro- gram that includes social equity targets to provide a baseline of support for clean energy generation. • Change utility regulations to remove barriers to low-income solar projects; lower permitting costs; provide greater certainty for developers, consumers and owners; and measure progress toward equity in renewable energy policy implementation.

14 SOLAR ENERGY↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗