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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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DOE OSTI2022

Comparison of temporal resolution selection approaches in energy systems models

Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Determining circuit model parameters from operation data for PV system degradation analysis: $\mathrm{PVPRO}$

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current-voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. Here this paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.

14 SOLAR ENERGY↗
DOE OSTI2022

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗
DOE OSTI2023

A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. Here, this paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
DOE OSTI2021

Query relaxation for portable brick-based applications

Semantic metadata standards pave the way for interoperability by providing building operators and application developers with common schemes to describe building resources. Applications can query building metadata models to retrieve the set of entities and relationships they need to operate, instead of hard-coding references to specific points and objects from the underlying data sources. Currently, querying such models requires the developer to be very specific when formulating queries in order to obtain meaningful answers (or any answer). The developer is inevitably expected to be familiar with the systems and components of the buildings being queried, as well as the schema used to represent them. The variety of buildings - both in the composition of their subsystems and in how they happen to be modeled - means that the developer will need to use multiple queries in order to retrieve necessary results. This is complex, time-consuming and error-prone. To address this limitation, we investigate query relaxation as a technique to facilitate discovery of meaningful building resources in a collection of ontology-based buildings data. We evaluate our query relaxation approach over a set of Brick models and demonstrate its use in the context of real-world building applications.

Brick↗
DOE OSTI2025

Turbulent Processes that Influence Boundary-Layer Cloud Structure (Final Technical Report)

Many models of the atmosphere simulate vertical profiles of observable fields, such as temperature, moisture, and vertical velocity. To assess the realism of such model output, it is important to evaluate the modeled profiles against observations. However, many extant evaluations consist of comparing time averaged profiles from the model and the observations. Such time averages can smooth out unrealistic features that appear in instantaneous profiles. In this way, the time averages may obscure certain model errors from detection. For instance, many models misrepresent turbulent processes, leading to profiles that are too smoothed out or too noisy. E.g., if a model has coarse vertical resolution and overly strong diffusion, then the model may be unable to produce observed sharp peaks. On the other hand, a model may have artificial approximations that produce spurious jumps in the vertical. Much can be learned by browsing through instantaneous snapshots of model output. But the volume of output produced by a global model is far too great to rely purely on visual inspection. In addition, we need a way to statistically characterize the profiles, so that anomalies can be detected more conveniently.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A zone-level occupancy counting system for commercial office spaces using low-resolution time-of-flight sensors

Understanding the locations of occupants in a commercial built environment is critical for realizing energy savings by delivering lighting, heating, and cooling only where it is needed. In this paper, we present an indoor occupancy counting system using a sparse array of inexpensive, low-resolution, and privacy-preserving time-of-flight sensors. We develop and validate an algorithm for zonal occupancy counting that can deal with multiple people walking underneath the sensors in arbitrary directions, and evaluate the system both in realistic simulations of office spaces and in a real-world installation. Finally, we found that our system has an error rate of around 0.4%, resulting in highly accurate person localization and zone counting using only a few sensors per space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cross-correlation of Planck CMB lensing with DESI-like LRGs

ABSTRACT Cross-correlations between the lensing of the cosmic microwave background (CMB) and other tracers of large-scale structure provide a unique way to reconstruct the growth of dark matter, break degeneracies between cosmology and galaxy physics, and test theories of modified gravity. We detect a cross-correlation between Dark Energy Spectroscopic Instrument (DESI)-like luminous red galaxies (LRGs) selected from DECam Legacy Survey imaging and CMB lensing maps reconstructed with the Planck satellite at a significance of S/N = 27.2 over scales ℓmin = 30, ℓmax = 1000. To correct for magnification bias, we determine the slope of the LRG cumulative magnitude function at the faint limit as s = 0.999 ± 0.015, and find corresponding corrections of the order of a few per cent for $C^{\kappa g}_{\ell }, C^{gg}_{\ell }$ across the scales of interest. We fit the large-scale galaxy bias at the effective redshift of the cross-correlation zeff ≈ 0.68 using two different bias evolution agnostic models: a HaloFit times linear bias model where the bias evolution is folded into the clustering-based estimation of the redshift kernel, and a Lagrangian perturbation theory model of the clustering evaluated at zeff. We also determine the error on the bias from uncertainty in the redshift distribution; within this error, the two methods show excellent agreement with each other and with DESI survey expectations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Projecting Future Energy Production from Operating Wind Farms in North America. Part I: Dynamical Downscaling

Abstract New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CF A ) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CF A increases in the Midwest, and CF A declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains. Significance Statement Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

A comprehensive review of dwell time optimization methods in computer-controlled optical surfacing

Dwell time plays a vital role in determining the accuracy and convergence of the computer-controlled optical surfacing process. However, optimizing dwell time presents a challenge due to its ill-posed nature, resulting in non-unique solutions. To address this issue, several well-known methods have emerged, including the iterative, Bayesian, Fourier transform, and matrix-form methods. Despite their independent development, these methods share common objectives, such as minimizing residual errors, ensuring dwell time's positivity and smoothness, minimizing total processing time, and enabling flexible dwell positions. This paper aims to comprehensively review the existing dwell time optimization methods, explore their interrelationships, provide insights for their effective implementations, evaluate their performances, and ultimately propose a unified dwell time optimization methodology.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling and Feedback Compensator Design for Power-Hardware-in-the-Loop System for Medium-Voltage Grid-Connected Power Converters

Hardware-in-the-loop (HIL) evaluation is a method in which the test subject is split into a physical part and a simulated part, and these parts are connected with interfaces to form a combined physical-numerical system. Power-hardware-in-the-loop (PHIL) systems, where actual hardware is connected to a real-time model, can exhibit issues of instability, inaccuracy and errors when operating in a closed-loop. The challenges of generating a PHIL setup are that, first, because of the limited dynamic response of the different parts of the system, the test results might be inaccurate, and, second, because of the high frequency noise introduced by the sensors to the closed-loop system, it can be difficult to design a compensator for the real-time emulator response, while stabilizing the closed-loop system at the same time. In this paper, different parts of a PHIL system are characterized, and the feedback compensator system design is proposed for the stable operation of the closed-loop PHIL system. The issues as observed in a PHIL system without any compensator are demonstrated using experimental results, and the effectiveness of a first order phase lead compensator is validated.

compensator design↗
DOE OSTI2022

A Logarithmic Bayesian Approach to Quantum Error Detection

We consider the problem of continuous quantum error correction from a Bayesian perspective, proposing a pair of digital filters using logarithmic probabilities that are able to achieve near-optimal performance on a three-qubit bit-flip code, while still being reasonable to implement on low-latency hardware. These practical filters are approximations of an optimal filter that we derive explicitly for finite time steps, in contrast with previous work that has relied on stochastic differential equations such as the Wonham filter. By utilizing logarithmic probabilities, we are able to eliminate the need for explicit normalization and can reduce the Gaussian noise distribution to a simple quadratic expression. The state transitions induced by the bit-flip errors are modeled using a Markov chain, which for log-probabilities must be evaluated using a LogSumExp function. We develop the two versions of our filter by constraining this LogSumExp to have either one or two inputs, which favors either simplicity or accuracy, respectively. Using simulated data, we demonstrate that the single-term and two-term filters are able to significantly outperform both a double threshold scheme and a linearized version of the Wonham filter in tests of error detection under a wide variety of error rates and time steps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Real-Time High-Accuracy Digital Wireless Time, Frequency, and Phase Calibration for Coherent Distributed Antenna Arrays

his work presents a fully-digital high-accuracy real-time calibration procedure for frequency and time alignment of open-loop wirelessly coordinated coherent distributed antenna array (CDA) modems, enabling radio frequency (RF) phase coherence of spatially separated commercial off-the-shelf (COTS) software-defined radios (SDRs) without cables or external references such as the global navigation satellite system (GNSS). Building on previous work using high-accuracy spectrally-sparse time of arrival (ToA) waveforms and a multistep ToA refinement process, a high-accuracy two-way time transfer (TWTT)-based time–frequency coordination approach is demonstrated. Due to the two-way nature of the high-accuracy TWTT approach, the time and frequency estimates are Doppler and multipath tolerant, so long as the channel is reciprocal over the synchronization epoch. This technique is experimentally verified using COTS SDRs in a lab environment in static and dynamic scenarios and with significant multipath scatterers. Time, frequency, and phase stability were evaluated by beamforming over coaxial cables to an oscilloscope which achieved time and phase precisions of ~60– 70 ps , with median coherent gains above 99% using optimized coordination parameters, and a beamforming frequency root-mean-square error (RMSE) of 3.73 ppb in a dynamic scenario. Finally, experiments were conducted to compare the performance of this technique with previous works using an analog continuous-wave two-tone (CWTT) frequency reference technique in both static and dynamic settings.

Clock synchronization↗
DOE OSTI2021

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant’s NO x emission rates is performed, directly comparing each modeling method’s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO x emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. Finally, these efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method’s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

42 ENGINEERING↗