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We collect the data from the electricity balancing authorities (BAs) that operate the grid. A balancing authority ensures, in real time, that power system demand and supply are finely balanced. This balance is needed to maintain the safe and reliable operation of the power system.
Long-wave infrared (LWIR) picosecond pulses with multi-terawatt peak power have recently become available for advanced high-energy physics and material research. Multi-joule pulse energy is achieved in an LWIR laser system via amplification of a microjoule seed pulse with high-pressure, mixed-isotope CO 2 amplifiers. A chirped-pulse amplification (CPA) scheme is employed in such a laser to reduce the nonlinear interaction between the optical field and the transmissive elements of the system. Presently, a research and development effort is underway towards an even higher LWIR peak power that is required, for instance, for promising particle acceleration schemes. The required boost of the peak power can be achieved by reducing the pulse duration to fractions of a picosecond. For this purpose, the possibility of reducing the gain narrowing in the laser amplifiers and post-compression techniques are being studied. Another direction in research is aimed at the increased throughput (i.e., repetition rate), efficiency, and reliability of LWIR laser systems. The transition from a traditional electric-discharge pumping to an optical pumping scheme for CO 2 amplifiers is expected to improve the robustness of high-peak-power LWIR lasers, making them suitable for broad implementation in scientific laboratory, industrial, and clinical environments.
Evolution of today’s predominantly alternating current (ac) transmission grid in to mixed ac transmission – high-voltage direct current (HVdc) systems is expected with the decreasing costs of and advancements in power electronics technologies. The recent technological advancements include the development of more efficient and compact voltage source converters (VSCs) that can provide fast response. Because of their controllability and fast response, HVdc systems provide the ability to enhance reliability of the ac transmission grid through frequency support, voltage control, and congestion relief. The HVdc systems can also provide other ancillary services to enhance the economics and reliability of the ac transmission grids, and to optimize their performance. The tools, models, and methods developed to evaluate their benefits will be key enablers to understanding the value proposition introduced by the fast-acting HVdc systems. The main motivation of this work was to develop models and methods to explore and quantify the technical and economic benefits of HVdc systems and fast response VSC technologies in the future grid of United States. The models and methods explore the benefits of HVdc systems through provision of controlled active/reactive power and fast response. The fast response can provide much-needed multiobjective services to the grid (e.g., frequency response, voltage control) while moving blocks of energy. The models and methods can be applied to study other power electronics technologies and scenarios such as those undertaken in ongoing studies led by the North American Electric Reliability Corporation (NERC) Inverter-based Resource Performance Task Force (IRPTF). The IRPTF studies identify the problems associated with power electronics-based technologies as they are integrated in to the bulk power system (or the ac transmission system).
Uncertainty quantification plays a pivotal role in improving the accuracy and reliability of inverter operation within modern power systems that are increasingly dominated by inverter-based resources (IBRs). IBRs, especially those operating under grid forming (GFM) control, rely heavily on a complex set of control parameters and system measurements to maintain voltage, frequency, and power balance. Traditional deterministic modeling approaches often fail to capture these parameter deviations, potentially resulting in suboptimal control actions, reduced system stability, or even instability under high penetration of IBRs. In this paper, we demonstrate the application of model calibration and uncertainty quantification (UQ) principles to an integrated transmission and distribution (T&D) model involving a GFM converter and provide a framework for prioritizing control improvements, guiding robust design, and informing adaptive strategies that can accommodate real-time variability in system conditions. The proposed approach could be valuable in enhancing the robustness of current and future power systems under increased IBR penetrations.
As an important part of pulsed power systems, high-voltage and high-current triggered spark gap switch and its trigger system are expected to achieve a compact structure. In this paper, a high-voltage, high-current, and compact electromagnetically actuated spark gap switch is put forward, and it can be applied as a part of an intense electron-beam accelerator (IEBA). A 24 V DC power supply is used to trigger the switch. The characteristics of the switch were measured for N{sub 2} when the gas pressure is 0.10–0.30 MPa. The experimental results showed that the voltage/pressure (V/p) curve of the switch was linear relationship. The operating ranges of the switch were 21%–96%, 21%–95%, 21%–95%, 19%–95%, 17%–95%, and 16%–96% of the switch's self-breakdown voltage when the gas pressures were 0.10, 0.14, 0.18, 0.22, 0.26, and 0.30 MPa, respectively. The switch and its trigger system worked steadily and reliably with a peak voltage of 30 kV, a peak current of 60 kA in the IEBA when the pressure of N{sub 2} in the switch was 0.30 MPa.
Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. In this paper, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory in terms of structure, dynamics, and speciation.
One in a series of Issue Papers addressing Electricity System Adequacy, this report focuses on Challenges Facing the Nuclear Power Industry. Early proclamations of a nuclear renaissance yield to predictions of the industry's decline, while proposed carbon regulations offer potential optimism to use nuclear as a clean energy source. Yet, nuclear plants are currently 'at-risk' of early retirement (for economic reasons) due to electricity market design issues and competition with low priced natural gas. Concerns are identified as baseload generation shifts from coal and nuclear to increasing natural gas and renewables which may impact on the reliability of the power system.
Methane, the primary component of natural gas, is an energy-rich fuel by weight but exhibits lower energy density by volume than diesel and gasoline; efforts to increase its “volumetric” energy density have been focused on the design and synthesis of a porous scaffold that can bind the methane favorably. Novel approaches typically invoke the highly designable class of materials known as metal-organic frameworks (MOFs) owing to the relative difficulty of controlling carbon structure and composition precisely, without the use of a metal-based node. In this project, we instead explore the concept of carbon templating to achieve a porous scaffold with atomistically thin walls separated at a distance of ~1.2 nm, ideal for the formation of precisely two layers of methane per pore. This bottom-up templating approach permits the use of any small molecular precursor to build up the carbon scaffold, begging the question: which other elements (besides carbon) can be used to tune the surface for optimal methane binding without compromising the structure of the overall material? Boron and nitrogen were both explored and nitrogen was identified as the ideal agent for increasing methane binding to the optimal strength for use at near room temperature. Densification of the resulting materials by mechanical compaction was then explored to produce free-standing pellets with the highest methane “delivery” capacities of any known material. Here we define the amount “delivered” as the amount stored minus the amount remaining in the tank at 73 psi, the lowest useful pressure for vehicular transportation applications. The key features of the resulting methane storage system are rapid discharging and refueling speeds, extremely long cycle life (infinite cycle life for high-purity methane), a near-constant heat of refueling (making the heat generated upon refueling easier to manage), and a low maximum pressure of operation (<1500 psi), enabling the use of all-metal Type I cylinders and significantly reducing the costs of conversion of existing vehicles to cleaner burning, abundantly available natural gas). The technical effectiveness of this approach was further improved by exploring several cost-reduction strategies and investigating the reliability of such storage systems in the presence of “real” natural gas mixtures which contain a mixture of gaseous species.
Purpose: Radiographic changes after lung stereotactic body radiation therapy (SBRT) have been categorized into 4 groups: modified conventional pattern (A), mass-like fibrosis; (B), scar-like fibrosis (C), and no evidence of increased density (D). The purpose of this study was to assess the interrater reliability of this categorization system in patients with early-stage non-small cell lung cancer (NSCLC). Methods and Materials: Seventy-seven patients were included in this study, all treated with SBRT for early-stage (T1/2) NSCLC at a single institution, with a minimum follow-up of 6 months. Six experienced clinicians familiar with post-SBRT radiographic changes scored the serial posttreatment CT images independently in a blinded fashion. The proportion of patients categorized as A, B, C, or D at each interval was determined. Krippendorff's alpha (KA), Multirater kappa (M-kappa), and Gwet's AC1 (AC1) scores were used to establish interrater reliability. A leave-one-out analysis was performed to demonstrate the variability among raters. Interrater agreement of the first and last 20 patients scored was calculated to explore whether a training effect existed. Results: The number of ratings ranged from 450 at 6 months to 84 at 48 months of follow-up. The proportion of patients in each category was as follows: A, 45%; B, 16%; C, 13%; and D, 26%. KA and M-kappa ranged from 0.17 to 0.34. AC1 measure range was 0.22 to 0.48. KA increased from 0.24 to 0.36 at 12 months with training. The percent agreement for pattern A peaked at 12 month with a 54% chance of having >50% raters in agreement and decreased over time, whereas that for patterns B and C increased over time to a maximum of 20% and 22%, respectively. Conclusion: This post-SBRT radiographic change categorization system has modest interrater agreement, and there is a suggestion of a training effect. Patterns of fibrosis evolve after SBRT and alternative categorization systems should be evaluated.
Fast non-volatile memory (NVM) offers game-changing opportunities to rethink traditional system architectures; to address their energy inefficiencies; to leverage the new devices for greater performance; and to exploit the new capability of non-volatility to enhance system resilience, even in the face of larger system scale and degraded reliability of components. However, the devil is in the details, and to reap such potential benefits we must address challenges that arise when the contents of memory survive application failure, when software becomes responsible for managing the contents of CPU caches, and when memory accelerators blur the line between hardware and software. Building on the accomplishments of the Blackcomb Project, funded in 2010, we have identified, evaluated, and optimized promising NVM hardware and software technologies that are essential to provide the necessary memory capacity, performance, resilience, and energy efficiency in exascale systems.
This report was written as part of a United States Department of Energy (DOE), Office of Nuclear Energy, Advanced Reactor Technologies program funded project to re-create the capabilities of the legacy Centralized Reliability Database Organization (CREDO) database. The CREDO database provided a record of component design and performance documentation across various systems that used sodium as a working fluid. Regaining this capability will allow the DOE complex and the domestic sodium reactor industry to better understand how previous systems were designed and built for use in improving the design and operations of future loops. The contents of this report include: overview of the current state of domestic sodium reliability databases; summary of the ongoing effort to improve, understand, and process the CREDO information; summary of the initial efforts to develop a unified sodium reliability database called the Sodium System Component Reliability Database (NaSCoRD); and explain both how potential users can access the domestic sodium reliability databases and the type of information that can be accessed from these databases.
As newer cells structures come online, the pressing need to replace silver in the metallization pastes has renewed interest in alternative technologies employing base metals. Copper typically leads the charge with its abundance and lower cost but has faced numerous obstacles from relatively higher oxidation and diffusion rates which can damage the lifetime of the devices. In this study, a low-cost alternative to silver metallization pastes has been shown on PERC cells. The screen printable copper paste can be fired in air at temperatures >500 degrees C, and the impact of processing conditions and equipment on the performance and reliability of 274 cm2 cells have been evaluated. Through damp heat testing of micro-modules using 16 cm2 cells, routes that can lead to both the failure and success of durable contacts have been demonstrated.
Electron Cyclotron Heating (ECH) is needed for plasma heating, current drive, plasma stability control, and other applications in fusion energy sciences research. The program of fusion energy sciences supported by U. S. DOE, Office of Science, Fusion Energy Sciences relies on the development of ECH technology to meet the needs of several plasma devices working at the frontier of fusion energy sciences research. The largest operating ECH system in the world is at DIII-D, consisting of six 1 MW, 110 GHz gyrotrons capable of ten second pulsed operation, plus two newer gyrotrons. The ECH Technology Development research program investigated the options for upgrading the DIII-D 110 GHz ECH system. Options included extending present-day 1 MW technology to 1.3 – 1.5 MW power levels or developing an entirely new approach to achieve up to 2 MW of power per gyrotron. The research consisted of theoretical research and designs conducted by Communication and Power Industries of Palo Alto, CA working with MIT. Results of the study would be validated in a later phase by research on short pulse length gyrotrons at MIT and long pulse / cw gyrotrons in industry. This research follows a highly successful program of development that has led to the highly reliable, six megawatt ECH system at the DIII-D tokamak. Eventually, gyrotrons at the 1.5 megawatt to multi-megawatt power level will be needed for heating and current drive in large scale plasmas including ITER and DEMO.
Hydroxymethylfurfural (HMF) is one of the important renewable platform compounds that can be obtained from biomass feedstocks through glucose conversion catalyzed by Brønsted and Lewis acids. However, it is challenging to enhance the HMF yield due to side reactions. In this study, a systematic approach combining theory and experiment was performed to investigate the influence of Lewis acids and organic solvents on the HMF yield. For the Lewis acid effect, a relationship between chemical hardness and experimental HMF yields was found in the rate-limiting step of glucose-to-fructose isomerization for six metal chlorides; HMF production was promoted when the metal chloride and a substrate had a similar chemical hardness. To study the organic solvent effect, a multivariate model was developed based on the insights gained from the mechanistic study of fructose dehydration, to predict HMF yields in a given water-organic cosolvent system. It showed a reliable accuracy in evaluating HMF yields with a mean absolute error (MAE) of 3.0% with respect to experimental HMF yields for 13 solvents, and also predicted HMF yields with a MAE of 10.7% for four new solvents. Chemical interpretation of the model revealed that it is desirable to use a solvent capable of stabilizing the carbocation intermediates with low proton transfer activity and high hydrogen bond basicity, to maximize the HMF yield. This multivariate model informs experimentalists about rational selection of solvents with very low computational costs needed to calculate only six variables for each solvent. It can be expanded to other catalytic systems such as heterogeneous Brønsted–Lewis bifunctional catalysts and enables optimization of reaction conditions to obtain other useful platform molecules through biomass conversion.
Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.
Nuclear energy is the most reliable and environmentally sustainable energy source available today. In the United States (U.S.), nuclear-generated power accounts for approximately 20% of total electricity and over 55% of clean energy. New advanced reactors have enormous potential to help further decarbonize the energy market, enhance grid resiliency, create new jobs, and build a stronger economy. More than 50 new reactors are being developed in the U.S., and the federal government realizes an urgent need to deploy nuclear technologies to meet the country’s energy, environmental, and national security goals. As such, the U.S. Department of Energy (DOE) launched multiple programs to support advanced reactor deployment. The research described in this report explores implementation strategies for the Reliability and Integrity Management Program that directly supports the DOE goal to enable the near-term deployment of the advanced reactor technologies. The project is conducted under the Regulatory Development Program for advanced reactors sponsored by the DOE.
Asset valuation by electric utilities is becoming increasingly difficult in the rapidly changing electric sector. Rapid deployment of variable generation and inverter-based storage systems along with uncertain demand growth, climate, policies, and other factors create a challenging environment for understanding the value proposition of a new potential asset. This report describes an effort between the Tennessee Valley Authority (TVA) and three U.S. Department of Energy laboratories to perform a detailed review of utility modeling and analysis practices for asset valuation and identify challenges and opportunities for advancing its methods into the future. It focuses on a case study of new potential pumped storage hydropower (PSH) because of growing interest in new PSH capacity to provide energy balancing, firm capacity, and a range of ancillary services. Staff from the DOE labs conducted systematic interviews about current practices in capacity expansion modeling, production-cost modeling, hydrological modeling, and transmission stability modeling while also discussing how scenario analysis is conducted and how models and data are integrated. The effort resulted in a set of model, integration, and scenario recommendations that could be valuable to TVA, other utilities, system operators, and other stakeholders conducting integrated grid analysis. Individual model recommendations suggest exploring computational tradeoffs with detail and resolution across spatiotemporal structure, supply- and demand-side details, transmission overlays, market interactions, and ancillary services. Automated processes to pass data between models and conduct larger scenario suites could also enhance valuation practices by enabling a more consistent study of asset value across a broader range of uncertain future grid conditions where PSH could be particularly valuable. TVA and other industry stakeholders can learn from and adapt applied research-grade methods developed by DOE laboratories and other research institutions to improve decision making and accelerate progress towards a reliable, economic, sustainable energy system.
Solar photovoltaics (PV) are the fastest growing renewable energy technology for clean, inexpensive, and sustainable electricity generation. Along with numerous technical roadmaps to improve system cost, performance and reliability, the PV industry should also plan to handle large volumes of silicon panel waste, which is initially estimated to be ~13 million metric tons (MT) by 2050 in the U.S. alone. Understanding the magnitude of material needs and how material flows throughout the PV panel life cycle could respond to design, operational and different end-of-life (EOL) circular pathways will help transition into a circular, resource-conserving economy. Herein, we introduce a dynamic material flow analysis (DMFA) framework based on electricity generation to quantify time-series stocks and flows of bulk PV materials (e.g., solar glass and aluminum frames) throughout the life cycles of utility-scale silicon PV systems in the U.S. in the period 2000-2100. We apply the model to a range of scenarios to understand how material demands depend on selected PV-related parameters, different material circularity strategies, and recent module design trends (e.g., bifacial, frameless). We found that float glass and aluminum in PV installations would likely reach 100 million MT and 12 million MT by 2100, respectively, in the baseline scenario. The most influential parameters for PV installation and subsequent waste reduction are found to be module lifetime, module efficiency, annual degradation, and material reduction. Module recycling and component remanufacturing were found to be the most effective material circularity strategies for waste minimization. Panel reuse has negligible savings on waste under current module efficiencies compared to replacements with newer generations with higher efficiency. Ongoing trends to produce larger power frameless modules could save 10 million MT of glass and ~9 million MT of aluminum. Our results enable advanced planning for future materials needs and provide insight into potential opportunities to minimize waste.