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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency’s Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (<10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts.

14 SOLAR ENERGY↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness - An Introduction to the Open-Source Tool and Use Cases

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility. RAVIS uses a technology suite that is assembled to provide optimum visualization facility while maintaining a wide pool of potential deployment and client environments. The tool is designed to take advantage of web application technologies, open source visualization libraries and tooling. Utilizing this technology will enable deployment in any environment, using any operating system, and is scalable to much higher spatial and temporal scales of visualization. As a prototype of the tool and demonstrating a use case of variable renewable integration, RAVIS currently integrates site-specific solar power forecasts in the California Independent System Operator (CAISO) and Mid-continent ISO (MISO) footprint from the IBM WattSun forecasting platform, and also superimposes market simulation data for CAISO footprint from as in-house NREL market clearing tool. The tool has the ability to alert the viewer for excessive up or down ramps for both individual solar sites as well as regionally aggregated net-load ramps, and alerts can also be qualified with respect to available flexible generation. This report will provide an introduction to the RAVIS tool, and summarize the above mentioned capabilities, typical use cases and possible extensions of the tool. The RAVIS development team believes there are likely to be high economic and reliability benefits of integrating probabilistic forecasts of variable renewables into control center visualizations and improved ramp events situational awareness for system operators and forecasting teams in the ISOs and electric utilities in comparison with their business as usual practices.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Hydroboost

HydroBoost is the most realistic revenue optimization tool for the hybridization of hydropower and battery energy storage systems to date. The innovative representation of how operators actually schedule hydropower in practice results in more realistic predictions of revenue and operations. Unlike other optimization tools, HydroBoost generates forecast energy prices with uncertainty to use in the optimization. This allows HydroBoost to give users a range of potential revenue with an upper bound using the perfect foresight pricing and a lower bound using a naive persistence forecast model. Additional forecast can be generated and used in the optimization, such as additive models, random forest, and neural networks to give further insight into potential revenue. HydroBoost has been designed to be applicable for both run-of-river and reservoir storage sites. The primary focus is on the day-ahead market and requires year-long data with an hour time-step. All time-series input and constraints are contained in an Excel worksheet for convince. The user will run the forecasting generation first with a Python script to give the optimization model the necessary requirements. Next the optimization is ran using Julia and results are generated and stored into a directory as csv files. HydroBoost includes an additional module to generate figures based on the results of the optimization simulation. The results help analyze the results and users to draw insights into how the hydro and battery systems are operated and the revenue each is producing. Additionally, the difference between the perfect foresight model and models that include forecast can easily be inspected.

Phillips, TylerB. [Idaho National Laboratory (INL)↗
DOE OSTI2024

Validation of Weather Forecasting Products

Meteorological modeling plays a pivotal role in operational safety and emergency response at the Savannah River Site (SRS). This study focuses on verifying the Regional Atmospheric Modelling System (RAMS) Version 4.3 through Mean Bias Error (MBE) and Root Mean Square Error (RMSE) analyses of temperature, dew point, and wind speed over a decade. Using observed data from SRS, we assessed RAMS' accuracy, revealing seasonal biases and error trends. Results indicate RAMS' strengths in mild weather conditions but challenges during seasonal extremes and wind speed predictions due to measurement disparities. Future research aims to expand verification to other models and parameters, advocating for enhanced forecasting accuracy crucial for safeguarding personnel and community well-being.

42 ENGINEERING↗
DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2024

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Talebpour, Mahdad↗
DOE OSTI2022

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

We report home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Scoping Analysis of Pebble-Bed Reactors for the Destruction of the Transuranic Inventory of LWR Spent Nuclear Fuel

With the forecasted increase in the construction and operation of nuclear reactors, there will be a corresponding increase in the quantity of spent nuclear fuel (SNF) that requires long-term storage. In SNF, transuranic isotopes contribute the most to the long-term radiotoxicity of the fuel and pose a proliferation risk. One option that has been explored to address these issues is the removal of the transuranic isotopes from SNF and the conversion of these isotopes into transuranic fuel (TRU fuel). Here, this work sought to determine how effective a micro-modular Pebble-Bed High-Temperature Gas-Cooled Reactor (PB-HTGR); the 10-MW High Temperature Gas-cooled Test Reactor (HTR-10); and a salt-cooled small-modular pebble-bed reactor (PBR), i.e. the generic Fluoride-cooled High-temperature Reactor (gFHR), are at reducing the inventory of transuranic isotopes while still maintaining the intrinsic safety features of the PBR designs, such as negative temperature coefficients of reactivity. Optimized pebble designs utilizing TRU fuel were found for both reactors through the adjustment for the packing fraction of fuel in each pebble. The Axial Zone Equilibrium Modeling (A-ZEM) method was used in this work to help select the optimized pebble design. Once an optimized pebble design was selected and an equilibrium model was produced, the results from the deep burn (DB) HTR-10 and gFHR designs were compared to the results of two models from the literature. While both the DB gFHR and the DB HTR-10 were able to reduce the weapons-usable transuranic inventory, the performance of these reactors did not match that of the small-modular PB-HTGRs in the literature. Therefore, a need was identified for further refinement of the gFHR design using TRU fuel, as the results for this model were more promising than those of the DB HTR-10, which was strongly limited by the high leakage intrinsic to micro-modular PB-HTGRs.

Transuranic fuel↗
DOE OSTI2025

Electric Grid Simulator For Human Factor Research

The developed code simulates real-time monitor and control for west area of IEEE 118-bus system. The 24-hour load profile for each bus is derived by scaling the system’s rated load in the PSSE sav file according to the California Independent System Operator’s Day-ahead load forecast for May 1, 2024. This simulator performs several critical functions: (1) Calculating time-series power flow every 4 seconds; (2) Updating and dispatching AGC signals every minute; (3) Conducting N-1 contingency analysis every 5 minutes. Additionally, the simulator can trip lines and subsequently update and dispatch AGC signals, running power flow analysis after each tripping event.

Huang, Jianqiao [Idaho National Laboratory (INL), ↗
DOE OSTI2024

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition

As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.

14 SOLAR ENERGY↗
DOE OSTI2020

A Combined Day-ahead and Real-time approach for Scheduling Real and Reactive power dispatch of Battery Energy Storage System

A combined day-ahead (look-ahead) and real-time optimization approach is proposed to schedule BESS real and reactive power dispatch. The look ahead scheduling agent maximizes profit and essentially performs the function of energy arbitrage by looking at forecasted prices, network constraints and BESS operating conditions to generate real power dispatch schedule. While this schedule is followed, the real time optimization agent computes an optimum reactive power set-point (for a particular objective function) using optimal power flow framework. The concepts are implemented by co-simulating legacy tools and demonstrated on a large real world distribution system. In addition to the profits from energy arbitrage, optimum reactive power set point computation results in achieving desirable network attributes like flat voltage profiles and minimizing voltage deviations from desired value.

Bhatti, Bilal A.↗
DOE OSTI2022

It's Not Just About the Megawatts - ARC Industry Forum, The Future of Power Generation

As the world transitions to less carbon intensive energy portfolios, much of the attention understandably centers on generating capacity – replacing megawatts of fossil fuel with megawatts of solar, wind, nuclear, hydroelectric, hydrogen, and more. The actual operation of a reliable grid needs more than just megawatts though: adequate quantities of a portfolio of essential reliability services; flexible and capable power delivery systems; governance to operate in 5-10 minute intervals, forecast hourly, and plan and build on a generational time horizon; and dependable and secure communications. We can choose our fuel mix for climate reasons, but the grid itself and the society that relies upon it impose constraints on those choices that we ignore at our peril.

13 HYDRO ENERGY↗
DOE OSTI2020

Numerical wave modeling for operational and survival analyses of wave energy converters at the US Navy Wave Energy Test Site in Hawaii

The US Navy Wave Energy Test Site (WETS) is currently operating three grid-connected berths off Marine Corps Base Hawaii in support of technological development through scaled-model testing and pre-commercial prototyping. The location on the east shore of Oahu is open to year-round wind waves and seasonal north swells as well as severe seas from subtropical systems and hurricanes. We have assembled a spectral wave model system comprising WAVEWATCH III and SWAN on a hierarchy of global, regional, and nearshore computational grids. With wind forcing from global forecast and rean-alysis datasets as well as their regional downscaling, the system produces operational 7-day wave forecasts and a long-term hindcast. The daily forecasts, validated with real-time buoy measurements, facilitate safe deployment, operation, and retrieval of wave energy converters (WECs). The wave hindcast from 1979 to 2017 defines the intra- and inter-annual variations for statistical analysis of sea states and energy resources. Due to limited occurrences of hurricanes in Hawaii waters, their wind forcing is determined parametrically with simulated tracks from climate model downscaling. Finally, systematic analysis of the hindcast and simulation datasets provides significant wave heights from trade winds, swells, subtropical systems, and hurricanes with return periods up to 100 years for WECs survival analysis.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗
DOE OSTI2022

Fast and accurate influenza forecasting in the United States with Inferno

Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. Forecasting challenges like FluSight should continue to monitor and evaluate how they can be modified and expanded to incentivize the development of forecasting models that benefit public health.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.

Lebakula, Viswadeep↗