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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A trilevel model against false gas-supply information attacks in electricity systems

The interdependence between natural gas and electricity systems is increasing rapidly due to the growing reliance on natural gas-fired generating units. Availability of natural gas for gas-fired generating units can impact the secure operation of electricity systems. Fuel supply shortage for gas-fired units can be caused by uncertain interruptible supply contracts and incorrect supply information. This article proposes a trilevel min-max-min defender-attacker-operator optimization problem to provide the power system operator a screening methodology that allocates a limited budget for best protecting critical fuel supply information, and also the strategies to sign firm supply contract to reduce natural gas supply uncertainties. We utilize a column and constraint generation (C&CG) algorithm to solve the proposed problem. We illustrate the effectiveness of this trilevel formulation using a case study based on IEEE 24-node test system.

42 ENGINEERING↗
DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗
DOE OSTI2022

Economic dispatch for electricity merchant with energy storage and wind plant: State of charge based decision making considering market impact and uncertainties

Here this paper investigates how the market impact of electricity merchants and uncertainty of wind generation affect their co-optimized scheduling policy, specifically for merchants who have both energy storage and wind plants. In the existing literature, merchants' trading actions are usually assumed not to affect market prices; however, a large-scale energy storage merchant's actions can affect market prices. To this end, we approximate the electricity price by a linear function of the quantity of power traded by the merchant in the reward function to achieve decision-making incorporating the market impact. This paper utilizes the dynamic programming approach to analyze merchants' optimal multi-period decision-making incorporating market impact, uncertain wind generation, and energy storage constraints. First, our results demonstrate that for a merchant with co-located energy storage facilities and wind power plants, the energy storage's feasible state of charge (SOC) range can be segmented into four possible sub-ranges by three analytically developed SOC reference points. The unique optimal trading decision can be achieved by comparing the current energy inventory and the SOC references of the next period. Second, our results show that market impact and uncertainties substantially change the optimal storage scheduling policy by impacting the values of the reference points. To mitigate the negative effect of the merchant's market impact on buying and selling actions, the merchant may reduce the amount of generating or pumping electricity each period to maximize profit. Moreover, we identify and investigate the trade-off between market price and transaction quantity. Our findings provide co-optimized scheduling guidance for electricity merchants with co-located energy storage and renewable power plants systems.

17 WIND ENERGY↗
DOE OSTI2024

MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade

The Massachusetts Institute of Technology General Circulation Model (MITgcm) is widely used by the climate science community to simulate planetary atmosphere and ocean circulations. A defining feature of the MITgcm is that it has been developed to be compatible with an algorithmic differentiation (AD) tool, TAF, enabling the generation of tangent-linear and adjoint models. These provide gradient information which enables dynamics-based sensitivity and attribution studies, state and parameter estimation, and rigorous uncertainty quantification. Importantly, gradient information is essential for computing comprehensive sensitivities and performing efficient large-scale data assimilation, ensuring that observations collected from satellites and in-situ measuring instruments can be effectively used to optimize a large uncertain control space. As a result, the MITgcm forms the dynamical core of a key data assimilation product employed by the physical oceanography research community: Estimating the Circulation and Climate of the Ocean (ECCO) state estimate. Although MITgcm and ECCO are used extensively within the research community, the AD tool TAF is proprietary and hence inaccessible to a large proportion of these users. The new version 2 (MITgcm-AD v2) framework introduced here is based on the source-to-source AD tool Tapenade, which has recently been open-sourced. Another feature of Tapenade is that it stores required variables by default (instead of recomputing them) which simplifies the implementation of efficient, AD-compatible code. The framework has been integrated with the MITgcm model’s main branch and is now freely available.

Adjoints↗
DOE OSTI2022

Soil organic carbon accrual due to more efficient microbial utilization of plant inputs at greater long-term soil moisture

High long-term soil moisture may either stimulate or inhibit soil organic carbon (SOC) losses through changes to mineral and chemical composition, and resultant organo-mineral interactions. Yet, the trade-off between mineralization and accrual of SOC under long-term variation in unsaturated soil moisture remains uncertain. We tested the underexplored relationships between long-term soil moisture and organo-mineral chemical composition and its implications for SOC persistence in an experimental field in New York, USA, with differences in long-term mean soil volumetric water content (0–0.15 m depth) ranging from 0.40 to 0.63 (v/v) during the growing season. Long-term soil moisture across 20 subplots on four fallow plots were positively correlated with SOC (R 2 = 0.23; P = 0.019, n = 20), mineral-associated organic matter (MAOM) content (g fraction/g soil) (R 2 = 0.44; P = 0.001; n = 20) and occluded particulate organic matter (oPOM) content (R 2 = 0.18; P = 0.033; n = 20). Higher long-term soil moisture was associated with a decrease in the relative content of sodium pyrophosphate extractable Fe (R 2 = 0.33; P < 0.005; n = 20), an increase in sodium dithionite extractable Fe (R 2 = 0.44; P < 0.001; n = 20), and an increase in SOC retention by non-crystalline Al pools (R 2 = 0.51; P = 0.0002 for sodium pyrophosphate extracts, R 2 = 0.41; P = 0.0014 for hydroxylamine hydrochloride extracts; n = 20 for both). Increasing long-term soil moisture was associated with a four-fold increase in microbial biomass C (per unit SOC) and lower metabolic quotient (R 2 = 0.56, P < 0.001). MAOM fractions of high-moisture soils had lower C:N (from C:N 9.5 to 9.0, R 2 = 0.27, P = 0.011, n = 20). Consistent with decreasing C:N, increasing decomposition with increasing moisture was reflected by a 15% and 10% greater proportion of oxidized carboxylic-C to aromatic-C and O-alkyl C, respectively, as measured with 13 C NMR, and a more pronounced FTIR signature of N-containing proteinaceous compounds in high-moisture MAOM fractions, indicative of microbial metabolites and transformation products. A partial least squares regression showed that SOC content increased with greater long-term moisture (P = 0.019), pyrophosphate-extractable Al (P = 0.0001), and exchangeable Ca (P = 0.013). Here our results show that higher long-term soil moisture resulted in SOC accrual by enhancing conversion of plant inputs into microbial biomass that interacts with reactive minerals.

58 GEOSCIENCES↗
DOE OSTI2020

Modeling study of deep direct use geothermal on the West Virginia university campus-morgantown, WV

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use (DDU) system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. Consequently, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided only the single permeability model is needed; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the prediction, therefore, heterogeneity is included in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis will be used for further economic studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Numerical simulation of asteroid geometry variance on airburst threat

For an atmospheric airburst the primary source of concern when assessing uncertainty is the size and velocity. Determining these properties provides the basis for threat assessment, as the total energy of the asteroid may then be estimated, and the threat investigated thoroughly. Even with clarity as to how much energy an asteroid may deposit, a great deal of uncertainty still exists for the actual energy deposition process. One such source of uncertainty is the geometry of the incoming asteroid. The geometry of an asteroid will alter the stress distribution during entry, which adds uncertainty to when fracture will occur. Here, in this study, we use Smoothed Particle Hydrodynamics to model the atmospheric airburst of Tunguska-scale asteroids with varying geometric profiles, including a sphere, ellipsoid, binary and superellipsoid. Each asteroid is modeled as a homogenous structure with strength. We assess uncertainty through a series of planar 2D simulation cases for each geometry, comparing the source of stochasticity across geometries. A single 3D airburst simulation for each geometry is also analyzed. Additionally, the 3D cases are compared to the highly uncertain Tunguska event, predicting variance in burst height across geometries, but all bounded by theoretical burst heights proposed for Tunguska.

Airburst↗
DOE OSTI2021

Risk-Averse Bi-Level Stochastic Network Interdiction Model for Cyber-Security Risk Management

Security of cyber networks is crucial; recent severe cyber-attacks have had a devastating effect on many large organizations. The attack graph, which maps the potential attack paths of a cyber network, is a popular tool for analyzing cyber system vulnerability. In this study, we propose a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse, resource constrained cyber network defender to optimally deploy security countermeasures that protect against attackers with an uncertain budget. This risk- averse conditional-value-at-risk (CVaR) model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop a customized constraint and column generation algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Numerical experiments demonstrate that the acceleration techniques enable the solution of relatively large problems within a reasonable amount of time: applying all the acceleration techniques also reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, computational results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

Bhuiyan, Tanveer H.↗
DOE OSTI2022

Tri-level hybrid interval-stochastic optimal scheduling for flexible residential loads under GAN-assisted multiple uncertainties

Various building loads, such as heating, ventilation, and air conditioners (HVACs), electric water heaters (EWHs), and electric vehicles (EVs), can introduce opportunities for improving the flexibility of electricity consumption while satisfying the needs of building owners as well as benefiting the resilience of distribution system. To utilize such flexibility, a tri-level distribution market framework is established, including residential consumers, load aggregators (LAs), and the distribution system operator (DSO). In this work, the uncertainties from all three levels are considered. The random consumption behavior at the consumer level is modeled as a Gaussian noise that is also aggregated and transmitted to the LA level. The weather temperature in the LA level is forecasted as an interval, and the photovoltaic (PV) power in the market-clearing level is modeled by a set of power scenarios generated by Generative Adversarial Networks (GANs). Then, a hybrid interval-stochastic programming is proposed to transform the uncertain problems in the first two levels into deterministic ones. For real-time implementations, a rolling horizon optimization (RHO) scheme is employed to continuously optimize the power consumption based on the latest operating information. Finally, case studies on a modified IEEE 69-bus system validate the effectiveness of the proposed uncertainty modeling strategies and the RHO scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2023

Selecting representative geological realizations to model subsurface CO 2 storage under uncertainty

Carbon capture and storage (CCS) is one of the quickest and most effective solutions for reducing carbon emissions. The majority of subsurface storage occurs in saline aquifers, for which geological information is lacking which in turn results in geological uncertainty. To evaluate uncertainty in CO 2 injection projections, the use of multiple geological realizations (GRs) has been practiced very commonly. In this approach, hundreds or thousands of high-resolution GRs is used that quickly becomes computationally expensive. This issue can be addressed with representative geological realizations (RGRs) that preserve the uncertainty domain of the ensemble GRs. Here, in this study, we propose the use of unsupervised machine learning (UML) frameworks, including dissimilarity measurement, dimensionality reduction, clustering and sampling algorithms ta select a predetermined number of RGRs. We compare the simulation outputs of the RGR sets and the ensemble using the Kolmogorov–Smirnov (KS) test to select the best UML. The UML frameworks and their associated selection processes are evaluated using a saline aquifer with a single CO 2 injection well and 200 GRs with varying uncertain petrophysical characteristics. The best UML framework is selected to use only 5% of the GRs while maintaining the uncertainty domain of the ensemble GRs. In addition, the best UML framework is tested using a saline aquifer with three CO 2 injection wells and varied GRs. The results show that our proposed UML framework can be used to choose RGRs, capturing the whole uncertainty domain. Our approach leads to a significant reduction in the computational cost associated with scenario testing, decision-making, and development planning for CO 2 storage sites under geological uncertainty.

58 GEOSCIENCES↗
DOE OSTI2024

Evaluating offshore legacy wells for geologic carbon storage: A case study from the Galveston and Brazos areas in the Gulf of Mexico

In this article, federal offshore waters in the Gulf of Mexico are of interest for large-scale geologic carbon storage (GCS). However, more than 80,000 offshore oil and gas wells exist in the region, which could impact the integrity of sealing intervals. In this study, we propose a screening methodology for ranking offshore legacy wells based on the challenge they may present to GCS. The methodology relies on the review of well regulatory records to 1) identify leakage pathways and assess the potential hazards that wells pose to planned GCS operations, 2) evaluate well features that impact the accessibility of wells to determine the feasibility of potential corrective actions, and 3) rank wells based on the overall challenge they may pose for GCS. We demonstrate our framework by evaluating the construction and abandonment of 156 wells across eight areas of interest (AOIs) in shallow federal waters along the Texas Gulf Coast. The majority (99.3 %) of wells considered were constructed and plugged in a manner that did not isolate prospective GCS targets in the Upper and Lower Miocene formations and may potentially require a challenging or uncertain corrective action prior to GCS. Dataset trends suggest that the observed well construction and plugging designs may be common in shallow offshore federal waters along the Texas Gulf Coast. Consequently, operators pursuing offshore GCS projects in the region may consider selecting areas that avoid challenging wells or performing robust evaluations of legacy well leakage risks to plan corrective action prior to CO 2 injection.

58 GEOSCIENCES↗
DOE OSTI2021

A probabilistic creep model incorporating test condition, initial damage, and material property uncertainty

Uncertainty is prevalent in the creep resistance of alloys, where at elevated temperature and low pressure, rupture can range across logarithmic decades. In this study, a probabilistic continuum-damage-mechanics (CDM)-based model is derived to capture the uncertainty of creep resistance. To meet this objective, creep data for alloy 304 Stainless Steel is gathered. A constitutive model, “Sinh”, is calibrated deterministically to determine the statistical variability of the material properties. Three sources of uncertainty are injected into the model: test condition (stress and temperature), initial damage, and material properties. Probabilistic simulations are carried out by (a) calibrating probability distribution functions (pdfs) for each source of uncertainty (b) randomly sampling the pdfs using Monte Carlo methods and (c) executing simulations to replicate the uncertain creep behavior. A sensitivity analysis is performed to evaluate the relative effect of each source of uncertainty. In full probabilistic simulations, the cumulative uncertainty of creep behavior is evaluated. The probabilistic model accurately predicts the creep deformation and rupture of the available experiments. The probabilistic model is validated for interpolation but lacks extrapolation ability. Several future works are proposed to further improve the model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation and development of flow condensation correlations using the data from low GWP refrigerants in an axial micro-fin aluminum tube

To mitigate global warming, the world is transitioning to refrigerants with low global warming potential (GWP). Supporting this shift requires a model that can accurately predict the heat transfer and pressure drop of new refrigerants, crucial for designing efficient heat exchangers. Existing models, however, are largely based on currently deployed refrigerants and primarily developed for unexpanded micro-fin tubes with spiral angles of 6° to 30°. Their applicability to new refrigerants, especially in expanded micro-fin tubes, is uncertain. This study assesses the performance of four well-known condensation models for six emerging refrigerants—R-32, R-454B, R-454C, R-455A, R-1234yf, and R-1234ze(E)—against experimental data. Initially, the Han and Lee (2005) model shows the best prediction accuracy with a mean absolute deviation (MAD) of 22.1 %. To enhance the accuracy of heat transfer models for new refrigerants and geometries with large temperature glides, two approaches are proposed. Here, the first approach applies a simple correction factor, reducing the MAD of the Cavallini et al. (2009) model from 68.2 % to 15.4 %. The second approach uses the variable metric method for minimization, fitting new constants to the data. This optimization results in the Kedzierski and Goncalves (1997) model achieving the highest accuracy, with a MAD of 13.1 %. For pressure drop models, the Cavallini et al. (1997) model is the most accurate with a MAD of 6.4 %, followed by the Haraguchi et al. (1993) model with a MAD of 9.4 %. Due to its simplicity, the Haraguchi et al. (1993) model is a practical option for predicting frictional pressure drop.

Axial micro-fin tubes↗
DOE OSTI2022

Association Between Regional Nodal Irradiation and Breast Cancer Recurrence-Free Interval for Patients With Low-Risk, Node-Positive Breast Cancer

Randomized clinical trials have shown that regional nodal irradiation (RNI) in patients with unselected N1 breast cancer improves breast cancer-specific survival. However, the benefit of RNI in women with biologically low-risk N1 breast cancer is uncertain. We conducted a population-based study to determine whether RNI is associated with improved breast cancer recurrence-free interval (BCRFI) in this population.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Decomposing sources of value for electricity and negative emissions technologies in net-zero power systems

Deep decarbonization of the US power system would require rapid deployment of variable renewable energy (VRE) resources, which are projected to provide a substantial share of electricity generation at the time of net-zero emissions. However, the exact share of generation met by VRE and the roles of other technologies in supplying key electricity services—energy and firm capacity—remain uncertain. This study employs a detailed model of the US power sector to decompose the provision and value of electricity services, including negative emissions, by technology across a range of deep decarbonization scenarios. Results indicate that while technology deployment and the share of services provided by each technology vary significantly depending on future technological and market conditions, the value composition and future roles of individual technologies remain consistent. These findings offer guidance for research and development priorities and provide insights to inform electricity policy and planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗