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DOE OSTI2021

SWAT model application for evaluating agricultural conservation practice effectiveness in reducing phosphorous loss from the Western Lake Erie Basin

Lake Erie is threatened by eutrophication and harmful algal blooms due to excess nutrient loading from agricultural sources. Agricultural conservation practices (ACPs) have been developed and implemented to reduce nutrient losses but estimating ACP effectiveness is challenging. The Soil and Water Assessment Tool (SWAT) has been used to investigate ACP effectiveness for water quality improvement. Many SWAT applications have been developed by different investigators to evaluate ACP effectiveness for reducing nutrient, particularly phosphorus (P), loading in the agriculturally-dominated Western Lake Erie Basin (WLEB). Our objective is to establish what has been achieved by past modeling research and make suggestions for future applications and improvements. We synthesized the findings of 28 SWAT modeling studies within the WLEB. Models generally performed satisfactorily against accepted criteria for streamflow and sediment, but performance for P loads, like soluble reactive P, was mostly “unsatisfactory”. The “unsatisfactory” performance maybe due to imperfections and idealizations in model formulations and/or parameterization. Thus, simulations of P transport and transformation processes need improvement. In addition, model parameter selection is the key part of model set-up. Most SWAT modeling studies used default values during initial set-up, then performed calibration and validation. It was found that the calibrated P related parameter values varied widely across different studies, even within the same watershed with some values unrealistic for the study areas. The phenomena of different combinations of model parameters producing similar outputs indicates equifinality. Equifinality in the baseline model may impact results when ACPs are incorporated. Furthermore, the unrealistic values used in ACP assessment undermine the credibility of ACP effectiveness. Future model applications should try to re-examine the calibrated P parameters and make sure they are realistic for the study area as well as reduce equifinality by constraining the model with characterization of watershed conditions, better understanding of hydrologic processes, and parameter values based on real-world observations. As a result, future model applications should focus on improving P transport and transformation processes, using measured watershed characteristics for parameterization, and improving reflections of climate change, which could result in more accurate assessments of ACP effectiveness to meet targeted goals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Evaluation of a Third Generation Single-Component Water-Lean Diamine Solvent for Post-Combustion CO 2 Capture

Here this study describes a comprehensive evaluation of a recently developed water-lean diamine solvent, N1-(2-ethoxyethyl)-N2, N2-diisopropylethane-1,2-diamine (2-EEDIPEDA), to probe its potential post-combustion CO 2 capture performance. This evaluation includes (1) fundamental sorption characterization such as vapor-liquid equilibria, sorption kinetics and viscosity impacts; (2) continuous capture characterization using laboratory scale flow system using flue gas simulant; as well as (3) technoeconomic analysis of the capture process at industrial scale with projections for critical capture performance metrics. Using this evaluation, we compare the performance of 2-EEDIPEDA to our current leading water-lean amine-based solvent, 2-EEMPA, using the same baseline process plant configuration/pricing. From this comparison, we observe lower total cost of capture as low as $46.8/tonne CO 2 with 2-EEDIPEDA (20% cheaper than CANSOLV), as a direct result of solvent chemistry tuning leading to notable cost savings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A Novel High‐Performance Electrolyte for Extreme Fast Charging in Pilot Scale Lithium‐Ion Pouch Cells

Realizing extreme fast charging (XFC) in lithium-ion batteries for electric vehicles is still challenging due to the insufficient lithium-ion transport kinetics, especially in the electrolyte. Herein, a novel high-performance electrolyte (HPE) consisting of lithium bis(fluorosulfonyl)imide (LiFSI), lithium hexafluorophosphate (LiPF 6 ) and carbonates is proposed and tested in pilot-scale, 2-Ah pouch cells. Moreover, the origin of improved electrochemical performance is comprehensively studied via various characterizations, suggesting that the proposed HPE exhibits high ionic conductivity and excellent electrochemical stability at high charging rate of 6-C. Furthermore, the HPE-based pouch cells deliver improved discharge specific capacity and excellent long-term cyclability up to 1500 cycles under XFC conditions, which is superior to the conventional state-of-the-art baseline electrolyte.

25 ENERGY STORAGE↗

DOE OSTI2021

Measurement of radiolytic hydrogen generation and impact of drying treatments on reactor exposed and surrogate aluminum materials

Technical challenges associated with dry storage of Aluminum-clad Spent Nuclear Fuel (ASNF) include a need to better understand the potential extent of gaseous molecular hydrogen (H 2 ) production through radiolytic degradation of the aluminum (oxy)hydroxide films present on ASNF. The characterization of radiolytic gas generation from ASNF (oxy)hydroxide layers has been identified as a key knowledge gap which poses a technical challenge to the long-term storage of ASNF. Task 2 of the action plan for the extended (>50 years) dry storage of ASNF addresses this gap. Previous radiolysis studies performed under Task 2 established baseline estimates of H 2 generation rates from the attendant hydrated oxides. The results confirmed that net radiolytic H 2 production has a dependency on absorbed gamma dose, as well as relative humidity and cover gas composition (air, nitrogen, and argon). Further experiments revealed that the physisorbed water on the samples may significantly impact the radiolytic H 2 yield. This phenomenon complicates the determination of H 2 generation rates for hydrated oxides, which may lead to inaccurate modeling predictions of the long-term H 2 yields in sealed storage systems containing ASNF, particularly when compared to spent fuel casks which have undergone some drying process intended to remove physically- and chemically-bound water. This report describes the testing methods utilized and the hydrogen generation results obtained in an investigation of the effects of gamma irradiation on aluminum materials for a variety of (oxy)hydroxide surface compositions and drying conditions. The testing methods included small-area aluminum material testing in ampules, and large-area aluminum material testing in steel vessels. Test material preparation, irradiation, and radiolytic H 2 measurement methods are summarized. The measured H 2 concentrations, which reflect the variable initial hydrated inventory and drying treatments, are compared to one another as well as to previously published data, to identify the primary factors affecting radiolytic H 2 generation rates and potential equilibrium H 2 concentrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Longitudinal Plasma Proteomic Profiling Reveals Divergent Immune Responses in Durably Cured and Relapsed Pulmonary Tuberculosis

Background: Predicting the risk of tuberculosis (TB) relapse is vital to improving treatment outcomes. Although clinical risk factors of relapse are well characterized, the biological mechanisms driving relapse, particularly host immune responses, remain poorly understood. Elucidating these mechanisms is necessary to better predict relapse risk. Methods: We conducted a longitudinal, global proteomic study on 60 participants with active pulmonary TB, half who were durably cured and half who relapsed. Plasma was collected at seven time-points: at treatment initiation (baseline), during therapy, and 52 weeks post-baseline. Samples were analyzed by high-resolution LC-MS/MS. Results: 2,418 proteins were identified across all samples, with 1,756 being differentially expressed relative to baseline (unadjusted p < 0.05). 956 proteins were differentially abundant between cured and relapsed participants. Relapsed participants showed heightened humoral immunity throughout treatment, as well as upregulated complement activation and HDL particles. Cured participants exhibited elevated recovery-related pathways by week 4, including downregulated epithelial invasion and upregulated oxygen transport. Conclusions: Heightened humoral and innate immune responses were associated with relapse, whereas recovery signatures were associated with durable cure. These findings advance our understanding of host responses to treatment and provide a basis for developing blood-based biomarkers to identify patients at increased risk of relapse.

LC-MS/MS↗

DOE OSTI2026

Beam-beam backgrounds for the Cool Copper Collider

In this paper, we present a comprehensive characterization of beam-beam backgrounds for the Cool Copper Collider (C 3 ), a proposed linear e + e - collider designed for precision Higgs studies at center-of-mass energies of 250 and 550 GeV. Using a simulation pipeline based on the Key4hep framework, we evaluate incoherent pair production and hadron photoproduction backgrounds through the SiD detector for baseline, power-efficiency, and high-luminosity C 3 operating scenarios. The occupancy induced by the beam-beam background is evaluated for each scenario, validating the compatibility of the existing SiD detector design with operations at C 3 without substantial modifications. Furthermore, at the same time, the modular simulation framework and analysis methodology presented in this paper offer a versatile toolkit for background studies in future collider proposals, contributing to a common platform for different machine designs.

Analysis and statistical methods↗

DOE OSTI2023

Evolution of global snow drought characteristics from 1850 to 2100

Abstract Seasonal snow is an integral part of the global water supply and storage system. Snow droughts impact ecological, agricultural, and urban systems by altering the amount and timing of meltwater delivery. These droughts are characterized by a lack of on-the-ground snow (snow water equivalent, SWE) that can be caused by low total precipitation (dry drought) or low proportion of precipitation falling as snowfall (warm drought), often combined with an early melt. The standardized SWE index (SWEI) ranks the current status of SWE for a given location compared to a baseline condition and identifies the existence, but not the cause, of snow drought. In this work, we use estimates of SWE, temperature, and precipitation from nine coupled model intercomparison project phase 6 (CMIP6) models to quantify the frequency, severity, and type of snow droughts globally for historical and future scenarios. Compared to a historical baseline (1850–1900) total snow drought frequency more than doubles under socio-economic pathway (SSP)2-4.5 and SSP5-8.5; all of the increase in snow drought frequency comes from an increase in warm droughts. The probability distribution of future SWEI in major snowy basins around the world are likely to be centered on more negative values, which corresponds to more severe drought and, with only moderate changes in distribution spread, more frequent drought. CMIP6 simulations pinpoint snow drought as an emerging global threat to water resources and highlight the need to explore higher resolution future models that better capture complex mountain topography, wildland fires, and snow-forest interactions.

CMIP6↗

DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗

DOE OSTI2024

DMTN-221: Periodicity Analysis in Alert Production

The baselined timeseries features to be computed in Alert Production include a Lomb-Scargle periodogram ran on two classes of variable systems: RR Lyrae and Eclipsing Binaries. Based on a simulated LSST-like cadence light curves taken from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC) we perform an end-to-end test to characterize the periodicity recovery on the Alert Production multi-band light curves. In both variable classes, we found that a single-band Lomb-Scargle implementation yields to a low fraction of recovered periods, with a significant preference on the simple periodic phenomena such as RR Lyrae. We also investigated the results from a multi-band Lomb-Scargle and found an increased fraction of recovered periodicities above 15% for the eclipsing binaries, and over 80$\%$ for the RR Lyrae stars. Our findings suggest that a multi-band Lomb-Scargle should be implemented for searching periodic phenomena through AP. We also asses the computational and scientific performance of several configurations on simulated alert data and find that our current configuration scales linearly with the number of detections while assuming an heuristic frequency grid.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Bounded-Confidence Models of Multidimensional Opinions with Topic-Weighted Discordance

People’s opinions on a wide range of topics often evolve over time through their interactions with others. Models of opinion dynamics primarily focus on one-dimensional opinions, which represent opinions on one topic. However, opinions on various topics are rarely isolated; instead, they can be interdependent and correlated. In a bounded-confidence model (BCM) of opinion dynamics, agents are receptive to each other only if their opinions are sufficiently similar. Here, we extend classical agent-based BCMs—namely, the Hegselmann–Krause BCM, which has synchronous interactions, and the Deffuant–Weisbuch BCM, which has asynchronous interactions—to a multidimensional setting, in which the opinions are multidimensional vectors representing opinions of different topics and opinions on different topics are interdependent. To measure opinion differences between agents, we introduce topic-weighted discordance functions that account for opinion differences in all topics. We define regions of receptiveness for our models, and we use them to characterize the steady-state opinion clusters and provide an analytical approach to compute these regions. In addition, we numerically simulate our models on various networks with initial opinions drawn from a variety of distributions. When initial opinions are correlated across different topics, our topic-weighted BCMs yield significantly different results in both transient and steady states compared to baseline models, where the dynamics of each opinion topic are independent.

Mathematics and Computing↗

DOE OSTI2020

Toward Long-Term Accurate and Continuous Monitoring of Nitrate in Wastewater Using Poly(tetrafluoroethylene) (PTFE)–Solid-State Ion-Selective Electrodes (S-ISEs)

Long-term accurate and continuous monitoring of nitrate (NO 3 – ) concentration in wastewater and groundwater is critical for determining treatment efficiency and tracking contaminant transport. Current nitrate monitoring technologies, including colorimetric, chromatographic, biometric, and electrochemical sensors, are not feasible for continuous monitoring. In this work, we addressed this challenge by modifying NO 3 – solid-state ion-selective electrodes (S-ISEs) with poly(tetrafluoroethylene) (PTFE, (C 2 F 4 ) n ). The PTFE-loaded S-ISE membrane polymer matrix reduces water layer formation between the membrane and electrode/solid contact, while paradoxically, the even more hydrophobic PTFE-loaded S-ISE membrane prevents bacterial attachment despite the opposite approach of hydrophilic modifications in other antifouling sensor designs. Specifically, an optimal ratio of 5% PTFE in the S-ISE polymer matrix was determined by a series of characterization tests in real wastewater. Five percent of PTFE alleviated biofouling to the sensor surface by enhancing the negative charge (–4.5 to –45.8 mV) and lowering surface roughness (Ra: 0.56 ± 0.02 nm). It simultaneously mitigated water layer formation between the membrane and electrode by increasing hydrophobicity (contact angle: 104°) and membrane adhesion and thus minimized the reading (mV) drift in the baseline sensitivity (“data drifting”). Long-term accuracy and durability of 5% PTFE-loaded NO 3 – S-ISEs were well demonstrated in real wastewater over 20 days, an improvement over commercial sensor longevity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Trade-off Analysis of Operational Technologies to Advance Cyber Resilience through Automated and Autonomous Response to Threats

The advancement of cyber resilience requires a preliminary stage of characterizing the trade-off space of mitigation options and how these might affect the stability and determinism of an operational technology (OT). This first step will set the stage for the proper cyber-secure and cyber-resilient design and confirm the affects that can be considered and approved by the OT and the security groups. To provide a baseline for this discussion, this paper provides a consideration of the cyberphysical interactions, possible mitigation steps against certain attacks and their corresponding affects that lend to the security design planning and evaluation process. As an integral part of the proposed scheme this work introduces the concept of systemwide fuzzer, i.e., a tool that manipulates the system state in an effort to determine mitigation response sequences that minimize detriments and maximize benefit in accordance with specified operational requirements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Understanding neutrinos with accelerator beams and liquid argon time-projection chambers: ICARUS and DUNE

A global program of experiments has worked towards characterizing neutrino oscillation over the past few decades. However, important parameters remain to be measured, and mysteries remain to be elucidated. Current and upcoming experiments are targeting the open questions and probing the consistency of the neutrino oscillation paradigm. Likewise, the liquid argon (LAr) time-projection chamber (TPC) has emerged as a sensitive particle detection technology for neutrino experiments. A current generation of LAr TPC detectors are being used to study neutrinos while also gaining important experience in operating and analyzing with this technology. SBND, MicroBooNE, and ICARUS have collected or are collecting data from beams at Fermilab (near Chicago) to explore the possibility of a sterile neutrino and/or other beyond Standard Model (BSM) physics. SBND and ICARUS will be used to conduct a two-detector analysis as part of the Short Baseline Neutrino (SBN) Program. Additionally, these detectors are enabling important neutrino interaction studies necessary to prepare for the next generation of oscillation experiments. One such oscillation experiment that will come online over the next years is the Deep Underground Neutrino Experiment (DUNE), which will install multiple 10 kiloton LAr TPCs underground in South Dakota (south of Saskatchewan) to conduct oscillation measurements with neutrinos originating in a beamline at Fermilab. A detector complex will be installed at Fermilab as well, to study the beam before the expected flavour oscillations. This “near detector” will also employ a LAr TPC, with a segmented and pixel-based design, as well as other technologies to constrain uncertainties in the oscillation measurement by characterizing the beam and neutrino interactions. This talk will discuss the ICARUS and DUNE experiments, the LAr TPC detector technology, and the efforts to realize and leverage these experiments to better understand the properties of neutrinos.

Howard, Bruce L. [Fermilab]↗

DOE OSTI2020

ePROJECT BUILDER: PROMOTING WIDER ADOPTION OF ENERGY SAVINGS PERFORMANCE CONTRACTS THROUGH STANDARDIZATION AND TRANSPARENCY

The United States Federal Government has been conducting guaranteed savings energy savings performance contracts for over 20 years and now relies on ESPC for the majority of its energy efficiency work. Along with a related financed project type, these deals resulted in $4.2 billion of project investment in the five years ending in 2016, a pace that has even accelerated since. Measurement and verification (M&V) on the projects is the key to assuring savings realization and persistence. Perceived as a weakness or burdensome added cost in the early years of the program, M&V has become a strength. All energy conservation measures (ECMs) have some form of measurement – defined as a measured baseline establishment followed by at least one measurement of the main energy-saving parameter taken in the performance period for each ECM. The government’s in-house energy consulting office, the Federal Energy Management Program (FEMP), now recommends measurement of these “Option A” M&V ECMs throughout the contract term, usually annually. Moreover, a significantly higher percentage of projects are now characterized by more ambitious M&V, including Option B (all parameter measurement) for most generation (including renewable) and some efficiency measures, and more frequent Option C (whole facility utility bill analysis) for “deep retrofit” projects with multiple, interactive ECMs. Coincident with this progress in M&V has been a much greater embracing of ESPC by the federal agencies, resulting in the enormous rate of projects now executed. This paper traces the evolution of M&V in federal ESPC and argues that the heightened credibility of the savings has contributed significantly to the procurement vehicle’s long-term viability. This focus on savings integrity via M&V has been learned over two decades for U.S. federal ESPC, but countries with developing ESPC markets would be wise to emphasize it as their markets emerge, allowing them to avoid some of the “growing pains” experienced in the U.S.

Earni, Shankar↗

DOE OSTI2020

M&V in ESPC: The U.S. Federal Experience and Implications for Developing ESPC Markets

Coleman, Philip↗

DOE OSTI2020

M&V in ESPC: The U.S. Federal Experience and Implications for Developing ESPC Markets

The United States Federal Government has been conducting guaranteed savings energy savings performance contracts for over 20 years and now relies on ESPC for the majority of its energy efficiency work. Along with a related financed project type, these deals resulted in $4.2 billion of project investment in the five years ending in 2016, a pace that has even accelerated since.Measurement and verification (M&V) on the projects is the key to assuring savings realization and persistence. Perceived as a weakness or burdensome added cost in the early years of the program, M&V has become a strength. All energy conservation measures (ECMs) have some form of measurement – defined as a measured baseline establishment followed by at least one measurement of the main energy-saving parameter for a given ECM taken in the performance period. The government’s in-house energy consulting office, the Federal Energy Management Program (FEMP), now recommends measurement of these “Option A” M&V ECMs throughout the contract term, usually annually. Moreover, a significantly higher percentage of projects are now characterized by more ambitious M&V, including Option B (all parameter measurement) for most generation (including renewable) and some efficiency measures, and more frequent Option C (whole facility utility bill analysis) for “deep retrofit” projects with multiple, interactive ECMs. Coincident with this progress in M&V has been a much greater embracing of ESPC by the federal agencies, resulting in the enormous rate of projects now executed. This paper traces the evolution of M&V in federal ESPC and argues that the heightened credibility of the estimated (and guaranteed) savings has contributed significantly to the procurement vehicle’s long-term viability. This focus on savings integrity via M&V has been learned over two decades for U.S. federal ESPC, but countries with developing ESPC markets would be wise to emphasize it as their markets emerge, allowing them to avoid some of the “growing pains” experienced in the U.S.

Coleman, Philip↗

DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗