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All sources NASA NTRS DOE OSTI

DOE OSTI2022

CO 2 to Fuels Through Novel Electrochemical Catalysis (Final Report)

Electrochemical upgrading of CO 2 into hydrocarbon fuels via CO 2 –H 2 O co-electrolysis using solid oxide electrolysis cells (SOECs) presents an encouraging approach to cost-effective storage of renewable power. In this study, we harness the unique property of protonic ceramic electrolysis cells (PCEC) and demonstrate direct electro-catalytic production of CH 4 from CO 2 –H 2 O in a unit-cell stack configuration. The unit-cell stack was developed to evaluate the scalability and the applicability of PCECs from a broader system-level viewpoint. Tradeoffs between operating conditions and performance metrics were explored and thoroughly characterized. An exceptional CH 4 -yield ratio of 34.6% from only CO 2 –H 2 O reactants was achieved under an electrolysis current of 1 A cm –2 at 450 °C. With exhaust-H 2 recycle, this value was boosted to greater than 70%. In addition, the electrochemical co-conversion of CO 2 –H 2 O offers a higher CH 4 -yield ratio compared to traditional thermo-catalytic conversion of CO 2 –H 2 under certain operating conditions, indicating possible electrochemical promotion of catalytic CO 2 methanation. The demonstrated good performance is a promising result for further scale-up and for the practical utilization of electrochemical CO 2 upgrade using PCECs. Techno-economic analyses reveal operating conditions that yield cost-competitive levelized cost of fuel production.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS2016

Watershed-Scale Modeling of Land-Use and Altered Environment Impacts on Aquatic Weed Growth in the Delta

The California Sacramento-San Joaquin River Delta is the hub for California's water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, and water quality have all been suspected as playing role in the dramatic expansion of invasive aquatic plants and their impact on ecosystems of the San Francisco Bay / California Delta complex. NASA Ames Research Center, USDA-Agricultural Research Service, the State of California, UC Davis, and local governments have partnered under a USDA sponsored project (DRAAWP) to develop science-based, adaptive-management strategies for invasive aquatic plants in Sacramento-San Joaquin Delta. Critical to developing management strategies is to understand how the Delta is affected by both the magnitude of fluctuations in land-use and climate / drought induced altered environments and how the plants respond to these altered environments. We utilize the Soil Water Assessment Tool (SWAT), a watershed-scale model developed to quantify the impact of land management practices in large and complex watersheds on water quality, as the backbone for a customized Delta model - Delta-SWAT. The model uses land-use, soils, elevation, and hydrologic routing to characterize pesticide and nutrient transport from the Sacramento and San Joaquin rivers watersheds and loading into the Delta. Land-use within the Delta, as well as water extraction to supply those functions, and the resulting return of water to Delta waterways are included in Delta-SWAT. Hydrologic transport within the Delta has required significant attention to address the lack of elevation driven transport processes. Delta-SWAT water quality trend estimates are compared with water quality monitoring conducted throughout the Delta. Aquatic plant response to water quality and other environmental factors is carried out using a customized model component. Plant response to the range of water quality factors, response times, and altered temperature and light regimes of the Delta have required gap-filling studies to provide model parameters. Delta-SWAT provides a tool for evaluating temporal and spatial effects of land-use and altered environments in the Delta and contributing watersheds on aquatic weed growth. Using Delta-SWAT for simulation modeling allows evaluation of historic and current conditions as well as consideration potential climate change and management practice outcomes. Delta-SWAT adds to the scientific understanding of dynamics in the Delta and enhances development of science-informed, management strategies and practices.

Ecosystem↗

NASA NTRS2016

Isokinetic TWC Evaporator Probe: Development of the IKP2 and Performance Testing for the HAIC-HIWC Darwin 2014 and Cayenne 2015 Field Campaigns

A new Isokinetic Total Water Content Evaporator (IKP2) was downsized from a prototype instrument, specifically to make airborne measurements of hydrometeor total water content (TWC) in deep tropical convective clouds to assess the new ice crystal Appendix D icing envelope. The probe underwent numerous laboratory and wind tunnel investigations to ensure reliable operation under the difficult high altitude/speed/TWC conditions under which other TWC instruments have been known to either fail, or have unknown performance characteristics. The article tracks the testing and modifications of the IKP2 probe to ensure its readiness for three flight campaigns in 2014 and 2015. Comparisons are made between the IKP2 and the NASA Icing Research Tunnel reference values in liquid conditions, and to an exploratory technique estimating ice water content from a bulk ice capture cylinder method in glaciated conditions. These comparisons suggest that the initial target of 20% accuracy in TWC has been achieved and likely exceeded for tested TWC values in excess of about 0.5/cu gm. Uncertainties in the ice water content reference method have been identified. Complications are introduced in the necessary subtraction of an independently measured background water vapor concentration, errors of which are small at the colder flight temperatures, but increase rapidly with increasing temperature, and ultimately limit the practical use of the instrument in a tropical convective atmosphere to conditions colder than about 0 C. A companion article in this conference traces the accuracy of the components of the IKP2 to derive estimated system accuracy.

Meteorological Instruments↗

NASA NTRS2016

Isokinetic TWC Evaporator Probe: Development of the IKP2 and Performance Testing for the HAIC-HIWC Darwin 2014 and Cayenne-2015 Field Campaigns

A new Isokinetic Total Water Content Evaporator (IKP2) was downsized from a prototype instrument, specifically to make airborne measurements of hydrometeor total water content (TWC) in deep tropical convective clouds to assess the new ice crystal Appendix D icing envelope. The probe underwent numerous laboratory and wind tunnel investigations to ensure reliable operation under the difficult high altitude/speed/TWC conditions under which other TWC instruments have been known to either fail, or have unknown performance characteristics. The article tracks the testing and modifications of the IKP2 probe to ensure its readiness for three flight campaigns in 2014 and 2015. Comparisons are made between the IKP2 and the NASA Icing Research Tunnel reference values in liquid conditions, and to an exploratory technique estimating ice water content from a bulk ice capture cylinder method in glaciated conditions. These comparisons suggest that the initial target of 20 percent accuracy in TWC has been achieved and likely exceeded for tested TWC values in excess of about 0.5 gm (exp -3). Uncertainties in the ice water content reference method have been identified. Complications are introduced in the necessary subtraction of an independently measured background water vapour concentration, errors of which are small at the colder flight temperatures, but increase rapidly with increasing temperature, and ultimately limit the practical use of the instrument in a tropical convective atmosphere to conditions colder than about 0 degrees C. A companion article in this conference traces the accuracy of the components of the IKP2 to derive estimated system accuracy.

Meteorological Instruments↗

DOE OSTI2026

Ultra-clear Aerogels for Insulated Glass in Refrigeration Doors (CRADA Final Report)

Display refrigerators commonly employ glass doors to provide product visibility; however, these doors exhibit significantly poorer thermal insulation than solid doors, resulting in increased heat transfer and higher energy consumption. To address this limitation, this project investigated the use of silica aerogel as a transparent insulating medium for refrigerator glass doors, with the objective of enhancing thermal performance without compromising visual clarity. In Phase I of the project, we assessed the feasibility of silica aerogel insulation through laboratory and full-scale studies, including material characterization, durability testing, and heat transfer measurements, along with the development and performance evaluation of prototype glass doors under realistic operating conditions. In Phase II, the project focused on scaling the technology toward practical deployment. This included scaling up the manufacturing process to achieve a surface area roughly 20 times larger for commercial applications, developing full-scale prototypes that integrated silica aerogel into standard display refrigerator doors, and conducting field evaluations to assess thermal performance, energy savings, and operational durability under real-world conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗

NASA NTRS1983

On the nonlinear versus linearized lower boundary conditions for topographically forced stationary long waves

For quasi-geostraphic stationary long waves forced by topography, the nonlinear lower boundary condition is derived in terms of the geopotential height and compared with the linearized version. The common practice of replacing terms describing the flow over and around a mountain by upstream zonal flow over the mountain and evaluating the resulting condition at sea level is found to be a good approximation for the cases considered and does not need to be modified as sometimes suggested. Specifically, it is found that this approximation does not affect, for most cases, the lower boundary condition expressed in terms of the geopotential height provided that the stationary wave is not near resonance. At resonance, the eddy advection terms may become important for large-amplitude waves when dissipation and surface diabatic heating are taken into account.

Tung, K. K.↗

DOE OSTI2025

Fusion Model for Metagenomics

This work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples using computer vision models and image representations of the abundance profiles. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.

Valdes, CamiloA [Lawrence Livermore National Labor↗

NASA NTRS2003

Gibbs-Thomson Law for Singular Step Segments: Thermodynamics Versus Kinetics

Classical Burton-Cabrera-Frank theory presumes that thermal fluctuations are so fast that at any time density of kinks on a step is comparable with the reciprocal intermolecular distance, so that the step rate is about isotropic within the crystal plane. Such azimuthal isotropy is, however, often not the case: Kink density may be much lower. In particular, it was recently found on the (010) face of orthorhombic lysozyme that interkink distance may exceed 500-600 intermolecular distances. Under such conditions, Gibbs-Thomson law (GTL) may not be applicable: On a straight step segment between two corners, communication between the comers occurs exclusively by kink exchange. Annihilation between kinks of opposite sign generated at the comers results in the grain in step energy entering GTL. If the step segment length l much greater than D/v, where D and v are the kink diffusivity and propagation rate, respectively, the opposite kinks have practically no chance to annihilate and GTL is not applicable. The opposite condition of the GTL applicability, l much less than D/v, is equivalent to the requirement that relative supersaturation Delta(sub mu)/kT much less than alpha/l, where alpha is molecular size. Thus, GTL may be applied to a segment of 10(exp 3)alpha approx. 3 x 10(exp -5)cm approx 0.3 micron only if supersaturation is less than 0.1%, while practically used driving forces for crystallization are much larger. Relationships alternative to the GTL for different, but low, kink density have been discussed. They confirm experimental evidences that the Burton-Cabrera-Frank theory of spiral growth is growth rates twice as low as compared to the observed figures. Also, application of GTL results in unrealistic step energy while suggested kinetic law give reasonable figures.

Chernov, A. A.↗

NASA NTRS1992

Teamwork for Oversight of Processes and Systems (TOPS). Implementation guide for TOPS version 2.0, 10 August 1992

As the nation redefines priorities to deal with a rapidly changing world order, both government and industry require new approaches for oversight of management systems, particularly for high technology products. Declining defense budgets will lead to significant reductions in government contract management personnel. Concurrently, defense contractors are reducing administrative and overhead staffing to control costs. These combined pressures require bold approaches for the oversight of management systems. In the Spring of 1991, the DPRO and TRW created a Process Action Team (PAT) to jointly prepare a Performance Based Management (PBM) system titled Teamwork for Oversight of Processes and Systems (TOPS). The primary goal is implementation of a performance based management system based on objective data to review critical TRW processes with an emphasis on continuous improvement. The processes are: Finance and Business Systems, Engineering and Manufacturing Systems, Quality Assurance, and Software Systems. The team established a number of goals: delivery of quality products to contractual terms and conditions; ensure that TRW management systems meet government guidance and good business practices; use of objective data to measure critical processes; elimination of wasteful/duplicative reviews and audits; emphasis on teamwork--all efforts must be perceived to add value by both sides and decisions are made by consensus; and synergy and the creation of a strong working trust between TRW and the DPRO. TOPS permits the adjustment of oversight resources when conditions change or when TRW systems performance indicate either an increase or decrease in surveillance is appropriate. Monthly Contractor Performance Assessments (CPA) are derived from a summary of supporting system level and process-level ratings obtained from objective process-level data. Tiered, objective, data-driven metrics are highly successful in achieving a cooperative and effective method of measuring performance. The teamwork-based culture developed by TOPS proved an unequaled success in removing adversarial relationships and creating an atmosphere of continuous improvement in quality processes at TRW. The new working relationship does not decrease the responsibility or authority of the DPRO to ensure contract compliance and it permits both parties to work more effectively to improve total quality and reduce cost. By emphasizing teamwork in developing a stronger approach to efficient management of the defense industrial base TOPS is a singular success.

Strand, Albert A.↗

DOE OSTI2022

Rationalizing nitrogen-doped secondary carbon particles for practical lithium-sulfur batteries

Nanostructured carbon host materials are widely used to improve both sulfur utilization rate and reaction kinetics in lithium-sulfur (Li-S) batteries. However, high complexity/cost of materials synthesis and difficulty in processing nano-materials into high-mass-loading electrodes are still significant barriers to the development of low-cost and high-energy Li-S batteries. In this study, we reported a generic and scalable synthesis approach to prepare nitrogen-doped secondary carbon particles. By using nitrogen-containing precursor as an integration reagent, the nanosized Ketjen Black particles were integrated into micron-size secondary ones and nitrogen-doped (NKB) simultaneously through a one-step heat treatment. With NKB as an example material, the effects of particle integration degree on the secondary particles’ structures, pore volume and connectivity, sulfur loading capability, and cell performance were studied and discussed. At an optimal integration condition, the NKB particles had significantly improved particle dimensions with well-maintained high specific surface area and pore volume. Here, contributed by the micron size and high pore volume, the NKB/S were successfully used for high-sulfur-loading cathodes (4–7 mg s /cm -2 ) and were able to deliver a specific capacity of ~1100 mAh g -1 at a low-porosity (50%) and lean-electrolyte conditions (E/S =4 µL/mg -1 ). Feasibility of the materials for practical use was validated through scaling up synthesis (40 g/batch), large-area electrode coating, and practical pouch cell (1.6 Ah) assembly and test.

25 ENERGY STORAGE↗

DOE OSTI2025

Local practically safe extremum seeking with assignable rate of attractivity to the safe set

We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured, static objective function while maintaining a measured, static metric of safety (a control barrier function or CBF) to be positive in a practical sense. We ensure that for trajectories with safe initial conditions, the violation of safety can be made arbitrarily small through appropriately chosen design constants. We also guarantee an assignable “attractivity” rate: from unsafe initial conditions, the trajectories approach the safe set, in the sense of the measured CBF, at a rate no slower than a user-assigned rate. Similarly, from safe initial conditions, the trajectories approach the unsafe set, in the sense of the CBF, no faster than the assigned attractivity rate. The feature of assignable attractivity is not present in the semiglobal version of safe extremum seeking, where the semiglobality of convergence is achieved by slowing the adaptation. We also demonstrate local convergence of the parameter to a neighborhood of the minimum of a quadratic objective function constrained to the safe set with a linear CBF. The ASfES algorithm and analysis are multivariable, but we also extend the algorithm to a Newton-Based ASfES scheme which we show is only useful in the scalar case. The proven properties of the designs are illustrated through simulation examples.

42 ENGINEERING↗

DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A Guide to Nonaqueous Electrochemistry of f-Element Complexes

Electrochemistry is a powerful tool for assessing and understanding the redox chemistry of molecular complexes. Cyclic voltammetry enables the f-element community to study molecules in unusually high or low oxidation states, which potentially pose important broad-scope questions of electronic structure. In the pursuit of boundary-pushing compounds, reactive air- and moisture-sensitive species are often encountered, which can be challenging to characterize, especially when they are chemically incompatible with certain solvents, electrolytes, or electrodes or when their potentials lie outside of common electrochemical windows. Nonaqueous solvents and pseudo reference electrodes complicate many of the standard practices in acquiring high-quality and reproducible electrochemical data. This guide presents a detailed discussion of selecting appropriate cell conditions and referencing and addresses metrics for evaluating electrochemical and chemical reversibility. These methodological approaches have been extended to best practices for the electrochemical analysis of radioactive transuranic complexes.

Electrodes↗

DOE OSTI2026

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗

DOE OSTI2025

Data-driven multi-element substitution of TiFe alloys for tunable thermodynamics and enhanced activation behaviour for hydrogen storage

Due to their high volumetric hydrogen storage capacity under moderate storage conditions, TiFe alloys have been widely investigated as candidates for practical solid-state hydrogen storage. Partially substituting Ti or Fe sites can improve the key characteristics of TiFe alloys, such as the first hydrogen absorption step (activation) and the equilibrium hydrogen pressure (thermodynamic properties). However, the selection of substitution elements has heavily relied on intuition and trial-and-error. Also, conventional substitution strategies have mainly focused on single-element substitution within the TiFe alloy, limiting the design space and tunability for target applications. Here, to address this limitation, we report a multi-element substitution strategy motivated by an efficient, data-driven machine learning (ML) approach combined with corroborating density functional theory (DFT) calculations. Our models successfully predict experimentally measured hydride stability in five selected alloys using only compositional descriptors. Most importantly, the multi-element substitution leads to enhanced activation properties compared to pure TiFe, achieving near room-temperature activation behaviour. This work provides a method for on-demand tuning of hydrogen storage and activation properties, which may have broad implications for data-driven discovery of energy storage materials.

Cho, YongJun [Korea Advanced Institute Science and↗

DOE OSTI2024

Stability Considerations for Virtual Capacitor Control in Constant Power DC Loads

In a DC grid, constant power loads (CPLs) necessitate a large bus capacitor to prevent negative impedance instability. Some of the capacitance can be realized virtually through CPL control to reduce the bus capacitor size. Here, this paper analytically studies system stability with virtual capacitor control. We demonstrate that the effectiveness of virtual capacitor control is highly sensitive to the implementation of the derivative action required to emulate the capacitor. For the first time, we theoretically demonstrate that a virtual capacitor can stabilize the system without any physical bus capacitor, provided that a set of analytically derived conditions are met. However, this stabilization is not achievable in practice due to the influence of non-idealities. In an example system requiring a $14$ mF capacitor to stably support the CPL, we show that stability can be achieved through virtual capacitor control, using only a 2 mF physical capacitor to account for the switching ripple non-idealities. Hardware-in-the-loop tests verify the analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process (e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the CAISO system. Furthermore, results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗