Search NASA⌕ Search

SEARCH · Search NASA

Results for “New England”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Rolling Optimization of Transmission Network Recovery and Load Restoration Considering Hybrid Wind-Storage System and Cold Load Pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

chance-constrained optimization↗
DOE OSTI2025

A census of fish passage facilities at US hydropower developments across the conterminous United States

Hydropower provides reliable and secure electricity and contributes significantly to the flexibility and stability of the US electricity grid. Hydropower dams generally are aquatic barriers and hazards to migratory fish in rivers. Fish passage facilities mitigate risks from hydropower to migratory fish, but information on these facilities is incomplete for the conterminous United States (CONUS). Here, we present the first CONUS-scale dataset of fish passage facilities at US hydropower developments in over 30 years. The existence of fish passage facilities (presence or absence) was specified for 1909 hydropower features, 390 of which had at least one facility. Most features had a single passage facility that provided passage in only one direction, with downstream being more common than upstream passage. Bypasses and ladders accounted for 60 % of the fish passage facility types. While we documented 659 fish passage facilities across CONUS, facilities were most common in the New England, Pacific Northwest, Mid-Atlantic, and Great Lakes regions. In general, fish passage facilities were more common at features that were closer to the ocean, at lower elevations, and at shorter dams, but not related to installed electrical generation capacity. Hydrologic sub-basins containing salmonids also contained the largest number of hydropower features, but the proportion of features with passage was generally higher in sub-basins containing multiple migratory taxa. This census provides valuable information on existing fish passage mitigation and is a benchmark to gauge progress toward a modernized hydropower fleet that provides affordable, reliable energy while protecting fishery resources and river ecosystems.

13 HYDRO ENERGY↗
DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Reactivity of CO 2 with Utica, Marcellus, Barnett, and Eagle Ford Shales and Impact on Permeability

We report that in order to reduce greenhouse gas emissions while recovering hydrocarbons from unconventional shale formations, processes that make use of carbon dioxide to enhance oil recovery while storing carbon dioxide (CO 2 ) should be considered. Here, we examine samples from three shale basins across the United States (Utica and Marcellus Shales in the Appalachian Basin, Barnett Shale in the Bend Arch-Ft. Worth Basin, and Eagle Ford in the Western Gulf Basin) to address the following questions: (1) do changes from reaction with CO 2 and fluids at the micrometer and nanometer scale alter flow pathways and, in turn, impact hydrocarbon production, CO 2 storage, and seal integrity and (2) can CO 2 or fluid reactivity be predicted based on physical or chemical properties of shale formations? Experiments were conducted at 40 °C and 10.3 MPa to characterize the interaction between CO 2 and shale using X-ray diffraction (XRD), carbon and sulfur analysis, in situ Fourier transform infrared spectroscopy (FT-IR), feature relocation scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), mercury (Hg) intrusion porosimetry, and Brunauer-Emmett-Teller (BET) surface area and pore size analysis coupled with density functional theory (DFT) methods. Changes in mechanical, physical, and flow properties of shale cores due to CO 2 exposure were addressed using a New England Research Autolab 1500 and Xenon X-ray computed tomography (CT) scanning. Results showed that CO 2 did not promote significant reactivity with the shale if water was not present; only shales with swelling clays or residual interstitial pore water reacted with dry CO 2 to promote reactivity in shale. When water was added as a reactant, CO 2 formed carbonic acid and reacted with the shale to dissolve carbonate pockets, etched and pitted the shale matrix surfaces, and increased the microporosity and decreased nanoporosity. Porosity and permeability increased appreciably in core shale samples after exposure to CO 2 saturated fluid due to dissolution of carbonate. Shale mechanical properties were not altered. Trends were not observed that could tie CO 2 or fluid reactivity to physical or chemical properties of the shale formations at the basin scale from the samples we examined. However, if the shale contained significant amounts of carbonate and water was available to react with the CO 2 , pore sizes were altered in the matrix and permeability and porosity increased.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Misaligned Wind‐Waves Behind Atmospheric Cold Fronts

Abstract Atmospheric fronts embedded in extratropical cyclones are high‐impact weather phenomena, contributing significantly to mid‐latitude winter precipitation. The three vital characteristics of the atmospheric fronts, high wind speeds, abrupt change in wind direction, and rapid translation, force the induced surface waves to be misaligned with winds exclusively behind the cold fronts. The effects of the misaligned waves under atmospheric cold fronts on air‐sea fluxes remain undocumented. Using the multi‐year in situ near‐surface observations and direct covariance flux measurements from the Pioneer Array off the coast of New England, we find that the majority of the passing cold fronts generate misaligned waves behind the cold front. Once generated, the waves remain misaligned, on average, for about 8 hr. The parameterized effect of misaligned waves in a fully coupled model significantly increases the roughness length (185%), drag coefficient (19%), and air‐sea momentum flux (11%). The increased surface drag reduces the wind speeds in the surface layer. The upward turbulent heat flux is weakly decreased by the misaligned waves because of the decrease in temperature and humidity scaling parameters being greater than the increase in friction velocity. The misaligned wave effect is not accurately represented in a commonly used wave‐based bulk flux algorithm. Yet, considering this effect in the current formulation improves the overall accuracy of parameterized momentum flux estimates. The results imply that better representing a directional wind‐wave coupling in the bulk formula of the numerical models may help improve the air‐sea interaction simulations under the passing atmospheric fronts in the mid‐latitudes.

Oceanography↗
DOE OSTI2022

Simulation of sugar kelp ( Saccharina latissima ) breeding guided by practices to accelerate genetic gains

Abstract Though Saccharina japonica cultivation has been established for many decades in East Asian countries, the domestication process of sugar kelp (Saccharina latissima) in the Northeast United States is still at its infancy. In this study, by using data from our breeding experience, we will demonstrate how obstacles for accelerated genetic gain can be assessed using simulation approaches that inform resource allocation decisions. Thus far, we have used 140 wild sporophytes that were sampled in 2018 from the northern Gulf of Maine to southern New England. From these sporophytes, we sampled gametophytes and made and evaluated over 600 progeny sporophytes from crosses among the gametophytes in 2019 and 2020. The biphasic life cycle of kelp gives a great advantage in selective breeding as we can potentially select both on the sporophytes and gametophytes. However, several obstacles exist, such as the amount of time it takes to complete a breeding cycle, the number of gametophytes that can be maintained in the laboratory, and whether positive selection can be conducted on farm-tested sporophytes. Using the Gulf of Maine population characteristics for heritability and effective population size, we simulated a founder population of 1,000 individuals and evaluated the impact of overcoming these obstacles on rate of genetic gain. Our results showed that key factors to improve current genetic gain rely mainly on our ability to induce reproduction of the best farm-tested sporophytes, and to accelerate the clonal vegetative growth of released gametophytes so that enough gametophyte biomass is ready for making crosses by the next growing season. Overcoming these challenges could improve rates of genetic gain more than 2-fold. Future research should focus on conditions favorable for inducing spring reproduction, and on increasing the amount of gametophyte tissue available in time to make fall crosses in the same year.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Population Aging and Heat Exposure in the 21st Century: Which U.S. Regions Are at Greatest Risk and Why?

Abstract Background and Objectives The co-occurring trends of population aging and climate change mean that rising numbers of U.S. older adults are at risk of intensifying heat exposure. We estimate county-level variations in older populations’ heat exposure in the early (1995–2014) and mid (2050) 21st century. We identify the extent to which rising exposures are attributable to climate change versus population aging. Research Design and Methods We estimate older adults’ heat exposure in 3,109 counties in the 48 contiguous U.S. states. Analyses use NASA NEX Global Daily Downscaled Product (NEX-GDDP-CMIP6) climate data and county-level projections for the size and distribution of the U.S. age 69+ population. Results Population aging and rising temperatures are documented throughout the United States, with particular “hotspots” in the Deep South, Florida, and parts of the rural Midwest. Increases in heat exposure by 2050 will be especially steep in historically colder regions with large older populations in New England, the upper Midwest, and rural Mountain regions. Rising temperatures are driving exposure in historically colder regions, whereas population aging is driving exposure in historically warm southern regions. Discussion and Implications Interventions to address the impacts of temperature extremes on older adult well-being should consider the geographic distribution and drivers of this exposure. In historically cooler areas where climate change is driving exposures, investments in warning systems may be productive, whereas investments in health care and social services infrastructures are essential in historically hot regions where exposures are driven by population aging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events

As the electricity grid is moving towards a 100% Renewable Energy Source Bulk Power Grid, the overall operations of the power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to make sure the grid is operating reliably. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning analysis framework to deconstruct some primary drivers for price formation in modern electricity markets with high renewable energy and the outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data and in this paper it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England.

machine learning (ML), electricity markets, Renewa↗
DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection

The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.

Hossain, Md Rifat [Florida International Universit↗
DOE OSTI2021

Examining the Economic Optimality of Automatic Generation Control

The automatic generation control (AGC) system is temporally situated between economic dispatch and synchronous generator dynamics, and its primary role is to regulate frequency within and tie-line flows between control areas. Given appropriate choice of participation factors (feed-forward controller gains that govern the disaggregation of the area-level power requirement to individual generators), the AGC can be engineered to nudge system dynamics toward a steady-state operating point corresponding to economic optimality. This paper establishes necessary and sufficient conditions under which a widely accepted choice of participation factors guarantees the alignment of steady-state synchronous generator outputs with a global minimum of a prototypical economic dispatch problem. In so doing, it resolves several ambiguities and formalizes technical assumptions governing the role of the standard AGC architecture in the context of economic dispatch and steady-state operation. Numerical case studies tailored to a modified version of the New England 39-bus 10-machine test system validate the theoretical results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Trust-Region Approximation of Extreme Trajectories in Power System Dynamics

In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given a bounded set that represents parametric uncertainty. Furthermore, we show how this method, while remaining computationally efficient compared with sampling-based techniques, overcomes the limitations of previous sensitivity-based techniques to approximate the bounds of the trajectories when the local approximation loses validity because of the nonlinearity. We present several numerical experiments that showcase the accuracy and scalability of the technique, including a demonstration on the IEEE New England test system.

42 ENGINEERING↗
DOE OSTI2022

Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model

In this work, we propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a linear dynamical system model. This transformation allows us to analyze the evolution of the system dynamics through its Koopman eigenfunctions, eigenvalues, and modes. Of particular importance for this letter, the obtained linear dynamical system is a surrogate that enables the evaluation of parameter uncertainties by simply perturbing the initial conditions of the Koopman eigenfunctions associated with the pseudo-state variables. Simulations carried out on the New England test system reveal the excellent performance of the proposed method in terms of accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Forced Oscillation Source Location of Bulk Power Systems Using Synchrosqueezing Wavelet Transform

Forced oscillation source location (FOSL) plays a significant role in mitigating forced oscillations (FOs), which threaten the power system stability. Here, this paper proposes a data-driven approach for FOSL in power systems using synchrosqueezing wavelet transform (SWT). The proposed approach conducts SWT on the measured system responses to obtain the SWT matrix. Then, the SWT-based dissipating energy flow (DEF) model in time-frequency domain and dissipating energy spectrum (DES) model in frequency domain are derived from the traditional DEF model. Further, the characteristics of SWT-based DEF and DES are revealed by referring to the traditional DEF, and the FOSL criteria of the SWT-based DEF and DES can be hereby obtained. Using the obtained FOSL criteria, the FO source can be located from the measured responses. The performance of the proposed FOSL method is evaluated by simulation data of the WECC 179-bus test system and field-measurement PMU data of the ISO New England. The results confirm the accuracy and efficiency of the proposed method in the FOSL.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Forced Oscillation Source Location in Power Systems Using MVMD-assisted DEF in TF Plane

Fast and accurate forced oscillation source location (FOSL) is critical for mitigating forced oscillations in power systems. Here, to overcome the shortcomings of traditional dissipating energy flow (DEF) in decomposing the forced oscillation components from the measurement responses, a multivariate variational mode decomposition (MVMD)-assisted FOSL is proposed in this paper to locate the forced oscillation sources from measurements in time-frequency (TF) plane. The multi-channel measurement matrix of each generator is formed, then the multi-channel modes are decomposed from the formed multi-channel measurement matrix using the MVMD. Further, the intrinsic mode functions (IMFs) associated with the forced oscillation mode are distinguished from the decomposed modes according to the relative energy weights. With the distinguished IMFs, the MVMD-assisted DEF calculation method is developed to locate the forced oscillation source. Finally, the performance of the proposed method is evaluated using the simulation data of the WECC 179-bus test system and IEEE-NASPI Oscillation Source Location Contest as well as the actual PMU data of ISO New England. The results validated the effectiveness and accuracy of the proposed method in the FOSL.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

River Basin Export Reduction Optimization Support Tool; a tool to screen options for reducing nutrient loads while minimizing cost

Abstract Excess loading of nitrogen and phosphorus to river networks causes environmental harm, but reducing loads from large river basins is difficult and expensive. We developed a new tool, the River Basin Export Reduction Optimization Support Tool (RBEROST) to identify the least‐cost combinations of management practices that will reduce nutrient loading to target levels in downstream and mid‐network waterbodies. We demonstrate the utility of the tool in a case study in the Upper Connecticut River Basin in New England, USA. The total project cost of optimized lowest‐cost plans ranged from $18.0 million to $41.0 million per year over 15 years depending on user specifications. Plans include both point source and non‐point source management practices, and most costs are associated with urban stormwater practices. Adding a 2% margin of safety to loading targets improved the estimated probability of success from 37.5% to 99%. The large spatial scale of RBEROST, and the consideration of both point and non‐point source contributions of nutrients, make it well suited as an initial screening tool in watershed planning.

Chamberlin, Catherine↗
DOE OSTI2021

Individual and Collective Strategies to Limit the Impacts of Large Power Outages of Long Duration

As modern society becomes ever more dependent on the availability of electric power, the costs that could arise from individual and social vulnerability to large outages of long duration (LLD-outages) increases. During such an outage, even a small amount of power would be very valuable. Here, this article compares individual and collective strategies for providing limited amounts of electric power to residential customers in a hypothetical New England community during a large electric power outage of long duration. We develop estimates of the emergency load required for survival and assess the cost of strategies to address outages that last 5, 10, and 20 days in either winter or summer. We find that the cost of collective solutions could be as much as 10 to 40 times less than individual solutions (less than $2 per month per home). However, collective solutions would require community-wide coordination, and if local distribution system lines are destroyed, only individual back-up systems could provide contingency power until those lines are repaired. Costs might be reduced if more robust distributed generation were employed that could be operated continuously with the ability to sell power back to the grid. Our cost-effectiveness analysis only assesses what could be done, developing estimates of preparedness cost. A decision about what should be done would require additional input from a range of stakeholders as well as some form of analytical deliberative process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

LiDAR Measurements of Wind Shear Exponents and Turbulence Intensity Offshore the Northeast United States

This paper presents wind speed shear exponents and turbulence intensity measurements collected from LiDARs measuring wind speeds from 40m to 200m above sea-level and provides comparisons to industry design guidelines. The high-altitude wind speed data are unique and represent some of the first measurements made offshore in this part of the country, which is actively being developed for offshore wind. The data is used to support the New England Aqua Ventus I Floating Offshore Wind Farm to be located 17km offshore the Northeast United States. Multiple LiDAR measurements were made using a DeepCLiDAR floating buoy and LiDARs located on a nearby island. Here, the measured wind speed shear exponents are compared against industry standard mesoscale model outputs and offshore design codes including the American Bureau of Shipping, American Petroleum Institute, and DNV-GL guides. Significant variation in the vertical wind speed profile occurs throughout the year which is not addressed in design standards. Additionally, turbulence intensity measurements made from the LiDAR, although not widely accepted in the scientific community, are presented and compared against industry guidelines.

42 ENGINEERING↗
DOE OSTI2020

Variability in Wind Energy Generation across the Contiguous United States

ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Additionally, gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.

17 WIND ENERGY↗