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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Utilizing coal-derived solid carbon materials towards next-generation smart and multifunction pavements

This project focused on developing an eco-friendly, multifunctional pavement system, called Coal-Derived Carbon Enabled Smart Pavement (CDC-SP), using coal-derived materials. The system utilizes coal-derived pyrolyzed char as a key component to create electrically conductive asphalt concrete for smart pavements that offer self-heating, self-sensing, and self-healing capabilities. These functions are made possible by the conductive properties of coal-char, which enable Ohmic heating for snow and ice deicing, piezoresistivity for self-sensing, and induction heating for self-healing of cracks caused by stress or aging. The team successfully created CDC-SP samples with over 50% coal char composition, demonstrating desirable mechanical properties such as rutting resistance, moisture susceptibility, and cracking resistance. These samples also exhibited strong electrical and thermal conductivity, making them ideal for heating applications. Extensive tests confirmed the pavement’s effectiveness in melting ice and snow and maintaining durability under environmental conditions. The CDC-SP presents a promising approach to integrating U.S. domestic coal resources into infrastructure projects, providing environmental and economic benefits by enhancing pavement performance while utilizing low-cost coal-derived materials. Further research and compliance with industry standards are recommended before commercial scaling.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Utility-Scale Solar, 2024 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2024 Edition” presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC (PV plants of 5 MWAC or less, including residential rooftop systems, are covered separately in Berkeley Lab’s companion annual report, Tracking the Sun). Key findings from this year’s report include: -18.5 GWAC of new utility-scale PV capacity came online in 2023, bringing cumulative installed capacity to more than 80.2 GWAC across 47 states. Installed costs continued to fall in 2023. Relative to 2022, capacity-weighted averages decreased by 8% to -$\$1.43$/WAC (or $\$1.08$/WDC). Costs, based on a 7.1 GWAC sample of 76 plants completed in 2023, have fallen by 75% (averaging 10% annually) since 2010. Plant-level capacity factors vary widely, from 6% to 36% (on an AC basis), with a sample median of 24%. -Levelized cost of energy (LCOE) of new 2023 projects increased slightly to $\$46$/MWh prior to the application of tax credits but continued to fall to $\$31$/MWh when accounting for federal incentives. PPA prices have largely followed the decline in solar’s LCOE over time, but newly signed longer-term PPA prices have increased since 2021, to an average of $\$35$/MWh (levelized, in 2023 dollars). -Solar’s average energy and capacity value (i.e., ability to offset costs of other power generation sources) across the U.S. was $\$45$/MWh in 2023. Solar’s average market value was lowest in CAISO ($\$27$/MWh), the market with the greatest solar generation share, and highest in ERCOT ($\$67$/MWh). -Newer solar projects had greater market value in 2023 than their generation costs, yielding $\$1.1$ billion in benefits. Projects built in 2022 delivered on average $\$15$/MWh more market value than their costs in 2023. -Solar’s combined value from wholesale electricity markets, public health and climate damage reduction were greater than generation costs and incentives, yielding $\$13.7$ billion in net benefits in 2023. We estimate U.S. health benefits of $\$24$/MWh and reduced global climate damages of $\$101$/MWh. -Adding battery storage is one way to increase the value of solar. Deployment of 52 new PV+battery hybrid plants set a record with 5.3 GW installed in 2023. Our public data file tracks metadata and PPA prices from more than 100 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -Looking ahead, a massive pipeline of at least 1,085 GW of solar capacity dominates the nation’s interconnection queues at the end of 2023. Nearly 571 GW, or 53%, of that total was paired with a battery – in CAISO it was a staggering 98%. Historically only 10% of the requested solar capacity is built. -For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗
DOE OSTI2025

Demonstration of Utility Managed Smart Charging for Multiple Benefit Streams (Final Report)

In the summer of 2020, the U.S. Department of Energy (DOE) awarded funding to Exelon’s Maryland utilities—Baltimore Gas and Electric (BGE), Delmarva Power & Light (DPL), and Potomac Electric Power Company (Pepco)—to implement the Smart Charge Management (SCM) pilot. This initiative aimed to design and implement managed electric vehicle (EV) charging strategies, evaluate the grid impacts of EV charging, and assess the utilities' ability to control EV load based on real-time grid conditions. The SCM pilot explored four aspects for continued improvement: (1) cybersecurity and managed charging functionality testing of two vendor platforms—WeaveGrid (telematics-based) and Shell Recharge Solutions (network-based)—which pursued charge scheduling and optimization through distinct approaches; (2) an analysis by Argonne National Laboratory (ANL) modeling team of three potential SCM enrollment scenarios within BGE and Pepco service territories over the next decade to assess future scalability; (3) employing customer engagement strategies, including surveys and a responsive pricing approach; and (4) the launch and implementation of pilots in Exelon’s Maryland territories in collaboration with WeaveGrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Impacts of PV Module Connector Failures on Cost and Performance of Utility Scale Photovoltaic Systems

The reliability, cost and performance of electrical connectors are a concern in all types of electrical systems, and demands on connectors used on photovoltaic (PV) systems include that connectors maintain electrical conductivity and physical strength, endure ultraviolet sunlight and high ambient temperature, and resist moisture and chemical intrusion over a very long (>25 year) performance period. Connector failures increase operation and maintenance (O&M) costs and reduce plant production, but connector failure can also cause safety and liability problems, which are of greater concern. This work results from a three-year collaboration between Sandia National Laboratories (SNL), the Electric Power Research Institute (EPRI), and the National Renewable Energy Laboratory (NREL) and funded by the U.S. Department of Energy (DOE) Solar Energy Technology Office (SETO) under Agreements #39035 and #38531 "Connector Reliability Across the US Solar Sector." a multi-pronged investigation of PV connector health across the US (see https://energy.sandia.gov/pvconnectors/). This report presents derivation of a Techno-Economic Analysis (TEA) that models failure modes and frequencies (how often failure occurs), estimates O&M costs and lost production associated with connector failures, and then calculates the effect that PV module connectors can have on Levelized Cost of Energy (LCOE). The model is informed with initial data from quantitative assessment of failure rates, root causes and mechanisms, in-situ diagnostics and data collection, lab-based forensics, and interviews with PV connector manufacturers and plant operators. SNL conducted site inspections at multiple utility-scale sites in different climates and subjected field samples of new, used, and degraded connectors to visual and electrical characterization. EPRI conducted metallurgical analysis of the pin and sleeve conductors to study failure-induced morphological and compositional changes. There is in general a shortage of statistically valid data, but data from PVROM database maintained by SNL was sufficient to ascertain failure rates and lost production as well as provide qualitative insight in its curated maintenance records. This report details the structure of the mathematical model but the sources of data to inform the model will continue to evolve. Analysis of a 100 MW PV plant is provided as an example of the use of the model, with results indicating that connectors are responsible for Annualized O&M Costs of $\$$71,933/year; Annualized Unit O&M Costs of $\$$0.72/kW/year; that a Reserve Account of $\$$187,220 should be available to fund repairs related to connectors; that connectors add $\$$1,494,004 to the Net Present Value of the O&M Costs (project life); and that O&M related to connectors adds about $\$$0.00088/kWh to the Levelized Cost of Energy. The impact of this model is to provide a tool to make the US solar sector more robust by quantifying and monetizing the reliability risks to utility-scale PV systems posed by poorly installed, mismatched and/or poorly designed and manufactured connectors. The TEA provides a model incorporating failure statistics, O&M cost data, and lost production into a single figure of merit, informing decisions and enabling practitioners to optimize cost and performance trade-offs. Stakeholders include connector manufacturers, system designers and equipment specifiers, standards bodies, installers and O&M providers, investors and insurance underwriters. This report supports continued growth of PV predicated on assurances that properly installed and maintained PV system connectors are safe and reliable. The project team is proposing future work including accelerated testing of connectors and expanding the approach taken here to other PV system components, such as TEA for rapid shut-down devices.

14 SOLAR ENERGY↗
DOE OSTI2024

Regulators’ Financial Toolbox: Leveraging Software as a Service, Cloud Computing, and Artificial Intelligence in Electric Utilities

The rapid evolution of Software as a Service (SaaS), cloud computing, and artificial intelligence (AI) is transforming the electric utility industry, reshaping operations, customer engagement, and financial models. This webinar introduced how utilities can deploy advanced software solutions and AI-driven analytics to improve grid efficiency, optimize asset management, and accurately forecast demand.

Bartlett, Phillip↗
DOE OSTI2025

Oak Ridge National Laboratory EAGLE-I TM : Modeling Electric Utility County Customers for Situational Awareness

During natural hazard events (hurricanes, wildfires, earthquakes, etc.) and recent man-made events (e.g., cyber attacks), the exchange of near real-time, spatially refined data within the response community is critical. The EAGLE-I$^{TM}$ platform is one tool that facilitates this data for decision makers within the energy sector. While much information can be collected and integrated into the system directly, other pertinent data must be augmented by other derived data products to enhance the information and allow for a consistent evaluation of on-the-ground conditions. One such data set that requires the addition of other derived data is the electric utility customer outage data that is aggregated to the county level within the EAGLE-I application. Without a county customer data set, outages can only be compared on total counts, which gives greater importance to higher population outages. Including an electric utility customer data set at the county level allows for these outage counts to be converted to percent outages and brings a consistent classification of outages and equal importance to all outages. To achieve this, several available data sets were combined and spatial disaggregation techniques were employed to model customer estimates at the county scale. This paper presents the approach to produce this data for the United States and lessons learned from working with these disparate data sets. Data validation is provided, where possible, and limitations of the model and possible improvements are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

EvaluateFlux, VARPOW, VARPEAK, and DIF3DtoVTK Utility Programs for DIF3D (Rev. 1)

The DIF3D-VARIANT solver is part of the DIF3D code which was developed at ANL for engineering level predictive analysis of nuclear reactor cores. Over the course of the last 20 years, the DIF3D-VARIANT option of DIF3D has become increasingly more used because of improvements in computer memory capabilities and performance. This is beneficial to the users of DIF3D as DIF3D-VARIANT is able to apply both p- and h-spatial mesh refinement strategies in diffusion and transport theory. Given its greater use, utility programs were created to assist in post-processing the DIF3D output. This document discusses the basic execution process of DIF3D and the output files it creates but is primarily focused on being the manual for the utility programs EvaluateFlux, VARPOW, VARPEAK, and DIF3DtoVTK.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Results of Existing CO 2 Source and Sink Databases Analysis

The “Southeast Regional CO 2 Utilization and Storage Acceleration Partnership” (SECARB-USA) project supported the U.S. Department of Energy (DOE) Office of Fossil Energy's (FE) mission to help the United States meet its need for secure, affordable, and environmentally sound fossil energy supplies by utilizing the advancements made since 2003 by the Regional Carbon Sequestration Partnership (RCSP) Initiative to continue to identify and address knowledge gaps.

42 ENGINEERING↗
DOE OSTI2026

Improved Microalgal Carbon Utilization Efficiency via Integrated CO 2 Electro-Conversion to Formate and Microalgal Sequestration

This project developed a process to convert industrial carbon dioxide (CO 2 ) emissions into high-value, sustainable products through genetically engineered algae cultivation. While traditional microalgae cultivation depends on sparging CO 2 gas through water, this method is often inefficient because much of the gas escapes into the atmosphere before the algae can consume it. To overcome this challenge, the project designed an integrated system that first uses a CO 2 to formic acid electrolyzer to convert CO 2 into water-soluble formic acid/formate, then introduces formic acid/formate into the algae pond for cultivation, which allows the algae to access and utilize nearly all of the provided carbon, greatly increasing the efficiency of carbon utilization. The project team has successfully scaled up the CO 2 to formic acid electrolyzer from lab-scale to 1000 cm² and demonstrated industrially relevant current densities with the scaled-up electrolyzers using a CO 2 source that simulates industrial CO 2 waste.

42 ENGINEERING↗
DOE OSTI2025

Demand response roadmap for the New Jersey Board of Public Utilities [Slides]

The New Jersey Board of Public Utilities (BPU) requested technical assistance from Berkeley Lab to inform its strategy for advancing demand response through its demand-side program planning process. Berkeley Lab developed this roadmap to delineate milestones for the portfolio's development and to identify the utility investments and BPU actions that achieve those milestones.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Carbon Capture and Utilization for Protein and Fatty Acids

The unlimited release version of the final report for the "Carbon Capture and Utilization for Protein and Fatty Acids" project. This project advanced an integrated open raceway algae cultivation and processing system to engineering scale for carbon capture and utilization (CCU) from the flue gas of a naphtha-fired power plant.

02 PETROLEUM↗
DOE OSTI2026

Grid Topology Discovery Algorithm Evaluation of Suitability for Utility Deployment (CRADA 606 Final Report)

This work presents the results of a field-informed demonstration aimed at evaluating the practical suitability of a topology discovery algorithm for utility environments. We demonstrated an algorithm that uses a graph-theory-informed state estimation approach for model selection. In collaboration with Survalent and Peninsula Light Co., the algorithm was applied to real feeder models and field measurements from supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) systems to identify the operational topology of a power distribution system. The demonstration assessed the algorithm’s performance under realistic data conditions, including sparse and noisy measurements, and examined its ability to identify the most likely network configurations. The results confirmed that the approach can effectively narrow down feasible topologies, providing operators with improved situational awareness of network status. Key lessons learned emphasize the need for systematic data validation and strategic sensor placement to enhance observability. These insights inform future deployment strategies and guide refinements for broader adoption in utility operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

EDXplorer: A Utility for APA Analysis

The automated particle analysis (APA) method of scanning electron microscopy (SEM) energy dispersive X-ray spectroscopy (EDS/EDX) is a useful tool for analyzing the elemental and morphological data of particulate samples. Often, such datasets have many thousands of particles, and it can be difficult to sift through the data to find meaningful trends. EDXplorer is a software utility for processing APA data. They enable the user to easily load data and determine the important components and aspects of the datasets using a powerful and versatile library of data plotting functions, mainly centered around scatter plots and histograms. These programs are intended to fill a void in the data processing of data from certain instruments, where often the user must rely on their own code or other software that is not user-friendly. EDXplorer is intended as a general plotting utility for browsing through data and discovering data trends.

Moseley, Duncan [ORNL] (ORCID:0000000343518347)↗
DOE OSTI2026

Least-cost Optimal Distribution Grid Expansion (LODGE): Utility Pilots

The Least-cost Optimal Distribution Grid Expansion (LODGE) model provides the optimal portfolio of distribution system upgrades—e.g., voltage regulators, feeder reconductoring, transformer upgrades and non-wires alternatives (NWA), such as strategic siting of storage and distributed generation—to interconnect distributed energy resources (DERs) and enable load growth. It can be used to assess grid infrastructure costs and explore policy and regulatory solutions for distribution planning and DER valuation.In 2025, Berkeley Lab conducted three pilot analyses to validate LODGE results with empirical utility data before the model’s first release in 2026. The pilots, done with utilities in Washington, Colorado, and New Mexico, provide examples that illustrate how the model works, what it can do, and the value of the analysis.

Heleno, Miguel↗
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
DOE OSTI2026

Africa Battery Energy Storage Systems (BESS) Capacity Building Utility-Scale Storage: BESS Valuation, Tariffs, and Remuneration [Slides]

Utility-scale Battery Energy Storage Systems (BESS) are key to enhancing grid reliability, integrating renewable energy, and providing operational flexibility. Designing effective valuation, remuneration, and tariff frameworks is essential to ensure both system benefits and financial viability for developers. This presentation outlines a structured methodology for evaluating BESS projects, covering policy and legal considerations, cost and revenue analysis, benchmarking, financial sensitivity, and risk assessment, while ensuring alignment with public interest. It also explores valuation of multiple storage services - bulk energy, ancillary services, and infrastructure support - and monetization strategies through capacity payments, energy tariffs, tolling, arbitrage, and non-wires alternative payments. Technical factors, including round-trip efficiency, degradation, and storage duration, are integrated into financial and operational modeling to quantify both system-wide and project-level benefits. Through case studies and simulation-based approaches, this framework provides regulators, utilities, and developers with practical guidance for tariff design, payment structures, and investment decisions, maximizing the economic and societal value of BESS deployment.

25 ENERGY STORAGE↗