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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Enclosure for light detection and ranging unit and related methods

This disclosure provides systems, methods, and apparatus related to light detection and ranging. In one aspect, a method comprises providing an apparatus. The apparatus comprises a LiDAR unit and a LiDAR enclosure. The LiDAR enclosure comprises a four-sided container having a square or rectangular cross-section with a first open end and a second open end comprising two opposite ends of the four-sided container. The LiDAR unit is attached to a mounting area inside the four-sided container. A reflective material is disposed on an interior of a second side and a third side of the four-sided container. Laser light is generated with the LiDAR unit. The laser light that has interacted with environment surrounding the LiDAR enclosure and that has been reflected back to the LiDAR unit is detected. The detected laser light is processed to construct an image of the environment surrounding the LiDAR enclosure.

Bandstra, Mark S.↗
DOE OSTI2012

Caustic Recycling Pilot Unit to Separate Sodium from LLW at Hanford Site - 12279

As part of the Department of Energy (DOE) sponsored Advanced Remediation Technologies initiative, a scheme was developed to combine Continuous Sludge Leaching (CSL), Near-Tank Cesium Removal (NTCR), and Caustic Recycling Unit (CRU) using Ceramatec technology, into a single system known as the Pilot Near-Tank Treatment System (PNTTS). The Cesium (Cs) decontaminated effluent from the NTCR process will be sent to the caustic recycle process for recovery of the caustic which will be reused in another cycle of caustic leaching in the CSL process. Such an integrated mobile technology demonstration will give DOE the option to insert this process for sodium management at various sites in Hanford, and will minimize the addition of further sodium into the waste tanks. This allows for recycling of the caustic used to remove aluminum during sludge washing as a pretreatment step in the vitrification of radioactive waste which will decrease the Low Level Waste (LLW) volume by as much as 39%. The CRU pilot process was designed to recycle sodium in the form of pure sodium hydroxide. The basis for the design of the 1/4 scale pilot caustic recycling unit was to demonstrate the efficient operation of a larger scale system to recycle caustic from the NTCR effluent stream from the Parsons process. The CRU was designed to process 0.28 liter/minute of NTCR effluent, and generate 10 M concentration of 'usable' sodium hydroxide. The proposed process operates at 40 deg. C to provide additional aluminum solubility and then recover the sodium hydroxide to the point where the aluminum is saturated at 40 deg. C. A system was developed to safely separate and vent the gases generated during operation of the CRU with the production of 10 M sodium hydroxide. Caustic was produced at a rate between 1.9 to 9.3 kg/hr. The CRU was located inside an ISO container to allow for moving of the unit close to tank locations to process the LLW stream. Actual tests were conducted with the NTCR effluent simulant from the Parsons process in the CRU. The modular CRU is easily scalable as a standalone system for caustic recycling, or for NTTS integration or for use as an In-Tank Treatment System to process sodium bearing waste to meet LLW processing needs at the Hanford site. The standalone pilot operation of the CRU to recycle sodium from NTCR effluent places the technology demonstration at TRL level 6. Multiple operations were performed with the CRU to process up to 500 gallons of the NTCR effluent and demonstrate an efficient separation of up to 70 % of the sodium without solids precipitation while producing 10 M caustic. Batch mode operation was conducted to study the effects of chemistry variation, establish the processing rate, and optimize the process operating conditions to recycle caustic from the NTCR effluent. The performance of the CRU was monitored by tracking the density parameter to control the concentration of caustic produced. Different levels of sodium were separated in tests from the effluent at a fixed operating current density and temperature. The voltage of the modules remained stable during the unit operation which demonstrated steady operation to separate sodium from the NTCR effluent. The sodium transfer current efficiency was measured in testing based on the concentration of caustic produced. Measurements showed a current efficiency of 99.8% for sodium transfer from the NTCR effluent to make sodium hydroxide. The sodium and hydroxide contents of the anolyte (NTCR feed) and catholyte (caustic product) were measured before and after each batch test. In two separate batch tests, samples were taken at different levels of sodium separation and analyzed to determine the stability of the NTCR effluent after sodium separation. The stability characteristics and changes in physical and chemical properties of the NTCR effluent chemistry after separation of sodium hydroxide as a function of storage time were evaluated. Parameters such as level of precipitated alumina, total alkalinity, analysis of Al, Na, K, Cs, Fe, OH, nitrate, nitrite, total dissolved and undissolved solids, viscosity, density, and other parameters of the NTCR effluent were measured. Changes in rheology and properties of NTCR stream to support downstream handling of the effluent after sodium separation was the basis for the analysis. The results show that the NTCR effluent is stable without the precipitation of aluminum hydroxide after 70% of the sodium was separated from the effluent. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Robotic Crane System for the Deconstruction of Chernobyl Unit 4 - 18583

The Chernobyl Nuclear Power Plant (ChNPP) Unit 4 accident occurred on April 26, 1986 leaving a severely damaged reactor building and a highly radioactive area. To minimize the long-term radiological impact to personnel and the environment, Unit 4 was immediately enclosed by a structure named the Object Shelter (OS). To transform the OS into an ecologically safe system, the Shelter Implementation Plan (SIP) was developed in 1997. The most significant SIP tasks was the design and construction of the New Safe Confinement (NSC). The NSC is a 36,000 tonne arch enclosure that includes a suite of process equipment to address the ruins of Unit 4 while assuring the safety of workers, public, and the environment. The NSC has a span of 257 meters, length of 164 meters, and height of 110 meters and is designed for a 100 years life. The New Safe Confinement was slid from its erection area to directly over Unit 4 (a distance of 327 meters) in November 2016. One of the major mechanical handling systems installed in the NSC is the Main Cranes System (MCS), designed and fabricated by PaR Systems (USA). The planned deconstruction activities require the handling of shielded waste disposal casks containing fuel as well as transporting extremely large structural elements -remotely controlled using video surveillance equipment. The lifting activities required the cranes to be tele-robotically controlled to prevent inadvertent collisions, expedite repetitive and high accuracy functions, protect the equipment and structures within the NSC as well as increase the overall effectiveness of the MCS. In addition, due to the elevated radiation levels, remote deconstruction activities will need to be performed. The MCS has design provisions included for a tele-robotically controlled manipulator and a suite of tooling to be used for multiple activities including concrete coring, cutting, debris collection and other heavy demolition activities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Dismantling of the Spent Resins Hot Compaction Unit at Tihange NPP, Belgium - 19139

Design, construction and commissioning of the Spent Ion Exchange Resins Hot Compaction Unit at Tihange Nuclear Power Plant took place from 2005 till 2011. In this conditioning process, batches of radioactive resin beads were first dried to less than 4% moisture by a conical vacuum dryer. Resins were then poured in compactable 200-liter drums and super-compacted. Resulting pellets were emplaced in standard 400-liter drums, the packages qualified for final disposal in Belgium, and eventually grouted. The Belgian Waste Management Agency (ONDRAF/NIRAS) was consulted before initiating the project and issued a statement of no objection. The technology turned out to be used for the first time in the particular case of bead shaped resins (unlike powdered resins) and with a grouting requirement. Finally, commissioning tests ended in a safe and reproducible waste form, compliant with prevailing Waste Acceptance Criteria. Volume Reduction Factors of about 2 were recorded after drying and compaction. New tests were carried out to support the Qualification File of the process, which was submitted to ONDRAF/NIRAS. After performing many trials using inactive resins, the Operator dried two batches of radioactive resins and super-compacted four drums: this was a planned qualification step, aiming at the validation of process parameters. In the meantime, ONDRAF/NIRAS made parallel progress in the design of the future Near Surface Repository, where conditioned resins will be disposed of, and found some incompatibility between encapsulated dried resin beads and water intrusion hazards inside the repository. Based on the risk that dried resin beads will swell if they absorb water, causing mechanical stresses likely to compromise the integrity of the disposal site, ONDRAF/NIRAS informed Tihange NPP that the Hot Compaction Unit would not be qualified as spent resins conditioning process. Further to this irrevocable decision, the Operator undertook three actions: - The first one was the qualification and purchase of waste containers to provide additional temporary storage capacity on site for its operational spent resins; - The second one is the selection of a new spent resins conditioning process and the performance of the related qualification tests to demonstrate it meets the updated Waste Acceptance Criteria; - The last one is the dismantling of the Hot Compaction Unit to clear the space and enable the installation of new equipment. This latter task was carried out in 2018, involving decontamination and free release measurements of all the materials, a very limited fraction of it having to be further processed as radioactive waste. Nevertheless, Tihange NPP wishes to keep the super-compactor and its hydraulic unit for a possible resale or reuse for the treatment of mixed waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids

Integration of renewable generation, which is often intermittent and decentralized, substantially increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.

Holt, Timothy↗
DOE OSTI2025

Habitat-specific seasonal densities of sympatric raccoons and opossums in the southeastern United States

Raccoon and opossum densities have implications for rabies management, but estimates of seasonal densities of both species are lacking for rural nonagricultural habitats of the southeastern United States, a core portion of their geographic range. Consequently, it remains unclear whether the densities of 1 species limit the other, which is possible considering their substantial niche overlap. We carried out a mark–recapture study of raccoons and opossums in 4 rural nonagricultural habitats (bottomland hardwood forest, riparian forest, upland pine forest, and isolated wetlands) in South Carolina, United States (2020 to 2022), and combined this with previous data from the same habitats (2017 to 2019) to estimate habitat-specific spring and fall densities. Raccoon densities ranged from 5.17 ± 0.96 animals/km 2 (bottomland hardwood fall) to 1.63 ± 0.83 animals/km 2 (upland pine spring) and were on average 19% higher in fall compared to spring. Opossum densities ranged from 10.35 ± 1.98 animals/km 2 (bottomland hardwood fall) to 1.11 ± 1.55 animals/km 2 (upland pine spring) with divergent seasonal patterns among habitats. These low densities across all habitats compared to other studies are likely the result of low resource availability, consistent with other habitats that have minimal anthropogenic influence. We observed a positive association between raccoon and opossum densities across trapping grids, suggesting that raccoons do not suppress opossum densities, but that densities of both species increase with increasing resource availability. Furthermore, our results can be used to inform oral rabies vaccination efforts such as refining bait densities and timing of bait distribution in these habitats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Inertia Estimation and Trend Analysis of the United States Power Grid Interconnections

The transition from conventional to modern power systems is causing an increase in integration of inverter-based resources (IBRs). This generally leads to a decrease in total system inertia, which in-turn increases the system’s rate-of-change-of-frequency (RoCoF) during disturbances. This poses a threat to the frequency stability of the system and may falsely trigger protective devices. To monitor system status and plan for integrating renewable energy sources like photovoltaic, wind, and energy storage systems, a realistic study of inertia estimation and analysis in the United States (US) over the past decade is needed. This paper uses field-measured phasor measurement unit (PMU) data collected throughout the US from 2013 to 2023 via the Frequency Monitoring Network (FNET/GridEye) operated by the University of Tennessee, Knoxville (UTK) and Oak Ridge National Laboratory (ORNL). The collected PMU frequency data is utilized to estimate the system inertia of the three US interconnections: Eastern, Western, and Texas. Various RoCoF time windows are investigated for estimating the inertia of each interconnection by maximizing the correlation coefficient between the measured RoCoF and power mismatch. The resulting inertia trends over the past decade show approximately a 6% decline in inertia in the Eastern interconnection, a 15% decline in inertia in the Western interconnection, and a 16% increase in inertia in Texas. Key insights into how inertia is changing amidst the complex energy landscape are extracted using the fuel mix trend data. This provides valuable information for future energy strategies and planning.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Life Cycle Analysis of Natural Gas Supply Chain and End Use Applications in the United States

Natural gas (NG) plays a crucial role in current and future energy systems in the United States due to its abundance and affordability. In this study a life cycle analysis of the NG supply chain in the United States was conducted using Argonne's R&D GREET model, examining stages from recovery to distribution using reported field data processed and documented by National Energy Technology Laboratory. Supply chain emissions were evaluated across multiple spatial scales, including national average, overall regional production, region-to-region, and basin-to-region scenarios. The GHG intensity of the U.S. average NG supply chain was estimated at 10.3 kg CO 2 e/MMBtu (lower heating value), with a range across regions from 7.8 kg CO 2 e/MMBtu (Northeast) to 15.1 kg CO 2 e/MMBtu (Pacific). The analysis further assessed how upstream NG emissions influence the life cycle GHG emissions of key end-use applications, including electricity generation (0.044–0.086 kg CO 2 e/kWh from upstream NG in combined cycle facilities), hydrogen production (1.04–2.20 kg CO 2 e/kg H 2 for steam methane reforming [SMR] and 1.06–2.23 kg CO 2 e/kg for autothermal reforming [ATR]), and transit bus operation utilizing compressed natural gas fuel (0.19–0.37 kg CO 2 e/mile) and hydrogen fuel (0.12–0.25 kg CO 2 e/mile for hydrogen produced in SMR and ATR).

compression↗
DOE OSTI2026

A Decomposition-Based Learn-To-Optimize Approach with Feasibility Layer Assistance for Sub-Hourly Unit Commitment

Sub-hourly unit commitment (UC) with 15-min intervals is gaining significant attention as a way to respond rapidly to the fluctuations in electricity supply and demand introduced by renewable resources. However, the increased temporal resolution and complex inter-temporal dependencies pose substantial computational challenges for traditional optimization methods. To this end, this paper explores a decomposition-based learn-to-optimize approach. Building on recent advances in machine learning, our method revisits the long- overlooked Lagrangian relaxation framework, which is a classical decomposition technique that enables tractable subproblem solving. These smaller subproblems are inherently well-suited for machine learning, as their reduced dimensionality and structural regularity allow predictive models to efficiently learn and generalize solution patterns. We thus propose a generic predictive model, which embeds Gated Recurrent Units (GRUs) and Attention in the encoder-decoder structure, and integrate a rule-based feasibility layer to capture temporal dependencies, reduce training effort, and improve feasibility w.r.t. unit-level constraints. Our method has been validated on the IEEE 118-bus system, demonstrating promising performance in solving sub-hourly UC problems efficiently and feasibly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Improved Genetic Algorithm approach to the Unit Commitment/Economic Dispatch problem

The deployment of new technologies, the importance of accurately modeling the dynamics of the generating units and the introduction of new policies are making the solution of the Unit Commitment/Economic Dispatch problem more and more complicated.In the present scenario, traditionally followed scheduling criteria might not lead to the optimal fleet configuration any more. In addition, most of the widely used techniques have limited capabilities at modeling the nonlinear dynamics of committed power plants. When realistic power systems comprising of several tens of generating units are modeled, the resulting optimization problem turns to be computationally intensive for the current computing capabilities. In this paper, an improved version of a GA-based optimization algorithm is presented. A detailed methodology aimed at obtaining a more efficient version of the GA, and a more detailed and accurate description of the flexible operation flexibility of the power plants is described.

genetic algorithm↗
DOE OSTI2024

Techno-economic analysis and network design for CO 2 conversion to jet fuels in the United States

The conversion of carbon dioxide (CO 2 ) into jet fuel holds significant potential for reducing CO 2 emissions, providing an alternative to carbon-based resources, and offering a renewable means of energy storage. The objective of this study is to conduct a techno-economic analysis and optimize the supply chain network for converting CO 2 to jet fuel in the United States, aiming to minimize total costs while assessing the environmental and economic feasibility of two CO 2 conversion pathways. This first pathway is based on Fischer-Tropsch synthesis (FTS), and the other one is based on the valorization and upgrading of light methanol (MeOH). Incorporating spatial and techno-economic data, a mixed-integer linear programming model was developed to select source plants and conversion pathways, locations of conversion refinery sites, and the amount of captured CO 2 across the United States. The optimal results indicate that the FTS pathway is adopted at all selected refineries when the hydrogen price is 1000 dollars/t and the operating cost, mainly electricity used in conversion, is reduced to 5 % of its current level. Under this scenario, the total annual profit is 8 billion dollars, and the net carbon emissions are -88,783,284 tons. The sensitivity analyses reveal that the prices of electricity and hydrogen significantly contribute to total production costs. The CO 2 recycle percentage of the FTS pathway influences the choice of applied pathways at refineries. Additionally, a higher conversion rate holds a substantial promise for reducing the total production cost and can make the MeOH pathway a viable choice.

10 SYNTHETIC FUELS↗
DOE OSTI2025

gRASPA

GPU Monte Carlo Simulation Code with a taste of RASPA We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.

Li, Zhao [Purdue/Northwestern/Notre Dame Universit↗
DOE OSTI2024

Leaf-level physiological strategies related to productivity and plasticity of Populus in the Southeastern United States

Introduction: Populus and its hybrids are attractive bioenergy crops and the southeastern United States has broad ability to supply bioenergy markets with woody biomass. Breeding and hybridization have led to superior eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars adapted to a wide variety of site types not suited for agricultural production. In order to maximize productivity and minimize inputs, genotypes need to efficiently use available site resources and tolerate environmental stresses. In addition, we need to determine plasticity of traits and their coordination across sites to select traits that will broadly characterize genotypes. Therefore, our study objectives were to determine (1) which leaf traits were correlated with growth, (2) if traits and genotypes exhibited significant plasticity across sites, and (3) how traits were coordinated within and across sites and Populus taxa. Methods: We measured trees at two sites in northeastern Mississippi, United States: one upland and one alluvial terrace site. Genotypes included eastern cottonwoods as well as F 1 crosses of eastern cottonwood and P. maximowiczii (Henry), P. nigra (L.) and P. trichocarpa (Torr. & Gray). Results: We found that sites differed in which leaf traits were correlated with productivity; with water use efficiency specifically being positively correlated with growth at an alluvial terrace site, but negatively correlated with growth at an upland site. Tree height growth, leaf isotope composition (δ 13 C and δ 15 N), as well as leaf mass per area (LMA) exhibited the least plasticity across sites, while physiological gas exchange parameters and leaf nitrogen concentration exhibited the highest plasticity. Broadly across taxa, leaf carbon isotope ratios were correlated with intrinsic water use efficiency, and stomatal conductance was positively correlated with photosynthetic nitrogen use efficiency across sites, while leaf nitrogen isotope ratios exhibited contrasting relationships with leaf nitrogen concentration. Discussion: Overall, these results allow us to refine selections of productive genotypes based on site conditions and site-specific relationships with physiological parameters to better match Populus taxa with sites and landowner objectives.

bioenergy feedstocks↗
DOE OSTI2026

Integration of GOES Data for Solar Resource Assessment of the Contiguous United States

The National Solar Radiation Database (NSRDB), produced by the National Laboratory of the Rockies (NLR), provides high-resolution solar resource data for the contiguous United States (CONUS) using Geostationary Operational Environmental Satellite (GOES) East and West observations. This study evaluates the integration of multi-satellite data within the GOES-East/West overlap regions, where conventional longitude-based selection methods often produce an artificial boundary seam. Our results demonstrate that an advanced blending algorithm, which incorporates sun-satellite scattering angles and satellite viewing zenith angles, improves NSRDB accuracy and creates a spatially continuous dataset. Validation against ground-based irradiance measurements reveals reductions in both percentage error (PE) and normalized Root Mean Square Error (nRMSE), particularly in the central United States. The dynamical integration of multi-satellite data provides a robust foundation for more precise modeling of solar resource and improved spatiotemporal analysis of solar ramp across the CONUS.

14 SOLAR ENERGY↗
DOE OSTI2016

Cutting planes for the multistage stochastic unit commitment problem

As renewable energy penetration rates continue to increase in power systems worldwide, new challenges arise for system operators in both regulated and deregulated electricity markets to solve the security-constrained coal-fired unit commitment problem with intermittent generation (due to renewables) and uncertain load, in order to ensure system reliability and maintain cost effectiveness. In this paper, we study a security-constrained coal-fired stochastic unit commitment model, which we use to enhance the reliability unit commitment process for day-ahead power system operations. In our approach, we first develop a deterministic equivalent formulation for the problem, which leads to a large-scale mixed-integer linear program. Then, we verify that the turn on/off inequalities provide a convex hull representation of the minimum-up/down time polytope under the stochastic setting. Next, we develop several families of strong valid inequalities mainly through lifting schemes. In particular, by exploring sequence independent lifting and subadditive approximation lifting properties for the lifting schemes, we obtain strong valid inequalities for the ramping and general load balance polytopes. Lastly, branch-and-cut algorithms are developed to employ these valid inequalities as cutting planes to solve the problem. Our computational results verify the effectiveness of the proposed approach.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

MPC solution for optimal load shifting for buildings with ON/OFF staged packaged units: Experimental demonstration, and lessons learned

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. In this work, two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Investigation of precooling unit design options in hydrogen refueling station for heavy-duty fuel-cell electric vehicles

Precooling gaseous hydrogen fuel to a cold temperature before refueling a heavy-duty (HD) hydrogen fuel cell electric vehicle (HFCEV) is essential to avoid overheating the vehicle tank, as well as achieving a high state of charge (SOC). Because a large volume of hydrogen is dispensed during each fill, the need for a shorter fill time amplifies the need to precool each load for refueling a HFCEV. Thus, the design and operation of a precooling unit (PCU), as well as the associated capital and operating costs, plays a pivotal role in any plans for heavy-duty hydrogen refueling stations. Here, in this paper, we present a thermodynamic and technoeconomic analysis of a PCU in a gaseous hydrogen refueling station (HRS) for HD HFCEVs. By employing Argonne National Laboratory's hydrogen station cost optimization and performance evaluation (H2SCOPE) model, the refueling of 50 kg of hydrogen on-board type IV tank at ambient temperatures of 15–45 °C and varying fill rates is simulated. The required degree of precooling temperature to obtain either 100% or the maximum possible SOC is obtained from the simulation. Additionally, the simulation results demonstrate that the average flow rate of hydrogen is approximately 40% lower than the maximum flow rate during a typical fill; which motivates further evaluation of the instantaneous hydrogen mass flow rate profile and suggests the scope of improving precooling unit design. Accordingly, a hybrid strategy of precooling hydrogen has been proposed to address the cooling load by sizing the refrigeration unit for the average flow rate of hydrogen, while supplementing the above average peak hydrogen flow cooling load through thermal buffering. The combined technique enables the downsizing of the original PCU capacity by 25–40% and demonstrates a potential cost reduction of the PCU by approximately 30%, which translates to an installed cost reduction of ∼$125,000 per dispenser.

25 ENERGY STORAGE↗