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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Renewable Electricity Futures Study, Volume 3: End-Use Electricity Demand

The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. gridwith high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energyfutures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growthassumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Renewable Electricity Futures Study, Volume 4: Bulk Electric Power Systems: Operations and Transmission Planning

The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. gridwith high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energyfutures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growthassumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Stochastic Methods Applied to Power System Operations with Renewable Energy: A Review

Renewable energy resources have been rapidly integrated into power systems in many parts of the world, contributing to a cleaner and more sustainable supply of electricity. Wind and solar resources also introduce new challenges for system operations and planning in terms of economics and reliability because of their variability and uncertainty. Operational strategies based on stochastic optimization have been developed recently to address these challenges. In general terms, these stochastic strategies either embed uncertainties into the scheduling formulations (e.g., the unit commitment [UC] problem) in probabilistic forms or develop more appropriate operating reserve strategies to take advantage of advanced forecasting techniques. Other approaches to address uncertainty are also proposed, where operational feasibility is ensured within an uncertainty set of forecasting intervals. In this report, a comprehensive review is conducted to present the state of the art through Spring 2015 in the area of stochastic methods applied to power system operations with high penetration of renewable energy. Chapters 1 and 2 give a brief introduction and overview of power system and electricity market operations, as well as the impact of renewable energy and how this impact is typically considered in modeling tools. Chapter 3 reviews relevant literature on operating reserves and specifically probabilistic methods to estimate the need for system reserve requirements. Chapter 4 looks at stochastic programming formulations of the UC and economic dispatch (ED) problems, highlighting benefits reported in the literature as well as recent industry developments. Chapter 5 briefly introduces alternative formulations of UC under uncertainty, such as robust, chance-constrained, and interval programming. Finally, in Chapter 6, we conclude with the main observations from our review and important directions for future work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Coordination and Control of Flexible Building Loads for Renewable Integration; Demonstrations using VOLTTRON

Renewable energy resources such as wind and solar power have a high degree of uncertainty. Large-scale integration of these variable generation sources into the grid is a big challenge for power system operators. Buildings, in which we live and work, consume about 75% of the total electricity in the United States. They also have a large capacity of power flexibility due to their massive thermal capacitance. Therefore, they present a great opportunity to help the grid to manage power balance. In this report, we study coordination and control of flexible building loads for renewable integration. We first present the motivation and background, and conduct a literature review on building-to-grid integration. We also compile a catalog of flexible building loads that have great potential for renewable integration, and discuss their characteristics. We next collect solar generation data from a photovoltaic panel on Pacific Northwest National Laboratory campus, and conduct data analysis to study their characteristics. We find that solar generation output has a strong uncertainty, and the uncertainty occurs at almost all time scales. Additional data from other sources are also used to verify our study. We propose two transactive coordination strategies to manage flexible building loads for renewable integration. We prove the theories that support the two transactive coordination strategies and discuss their pros and cons. In this report, we select three types of flexible building loads—air-handling unit, rooftop unit, and a population of WHs—for which we demonstrate control of the flexible load to track a dispatch signal (e.g., renewable generation fluctuation) using experiment, simulation, or hardware-in-the-loop study. More specifically, we present the system description, model identification, controller design, test bed setup, and experiment results for each demonstration. We show that coordination and control of flexible loads has a great potential to integrate variable generation sources. The flexible loads can successfully track a power dispatch signal from the coordinator, while having little impact on the quality of service to the end-users.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Cost of Wind Energy Review: 2024 Edition [Slides]

The primary elements of this analysis include: Estimated LCOE for (1) a representative land-based wind energy project installed in a moderate wind resource in the United States, (2) a representative fixed-bottom offshore wind energy project installed in the U.S. North Atlantic, and (3) a representative floating offshore wind energy project installed off the U.S. Pacific Coast. It also updates the LCOE estimates for representative residential-, commercial-, and large-scale distributed wind projects installed in a moderate wind resource in the United States. A sensitivity analyses is included that shows the range of effects that basic LCOE variables could have on the cost of wind energy for land-based and offshore wind projects and provides updated Fiscal Year 2024 values for land-based and offshore wind energy used for Government Performance and Results Act (GPRA) reporting and illustrated progress toward established GPRA targets.

17 WIND ENERGY↗
NASA NTRS1982

Computing the Power-Density Spectrum for an Engineering Model

Computer program for calculating of power-density spectrum (PDS) from data base generated by Advanced Continuous Simulation Language (ACSL) uses algorithm that employs fast Fourier transform (FFT) to calculate PDS of variable. Accomplished by first estimating autocovariance function of variable and then taking FFT of smoothed autocovariance function to obtain PDS. Fast-Fourier-transform technique conserves computer resources.

Dunn, H. J.↗
NASA NTRS2008

Exploration Supply Chain Simulation

The Exploration Supply Chain Simulation project was chartered by the NASA Exploration Systems Mission Directorate to develop a software tool, with proper data, to quantitatively analyze supply chains for future program planning. This tool is a discrete-event simulation that uses the basic supply chain concepts of planning, sourcing, making, delivering, and returning. This supply chain perspective is combined with other discrete or continuous simulation factors. Discrete resource events (such as launch or delivery reviews) are represented as organizational functional units. Continuous resources (such as civil service or contractor program functions) are defined as enabling functional units. Concepts of fixed and variable costs are included in the model to allow the discrete events to interact with cost calculations. The definition file is intrinsic to the model, but a blank start can be initiated at any time. The current definition file is an Orion Ares I crew launch vehicle. Parameters stretch from Kennedy Space Center across and into other program entities (Michaud Assembly Facility, Aliant Techsystems, Stennis Space Center, Johnson Space Center, etc.) though these will only gain detail as the file continues to evolve. The Orion Ares I file definition in the tool continues to evolve, and analysis from this tool is expected in 2008. This is the first application of such business-driven modeling to a NASA/government-- aerospace contractor endeavor.

Source record↗
NASA NTRS2021

A sampling-based optimization approach to handling environmental uncertainty for a planetary lander

Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness

Zilberstein, Shlomo↗
NASA NTRS2021

A Sampling-Based Optimization Approach to Handling Environmental Uncertainty for a Planetary Lander

Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness

Chien, Steve↗
DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗
DOE OSTI2019

Earth Source Heat: A Cascaded Systems Approach to DDU of Geothermal Energy on the Cornell Campus

This report documents the completion of the DOE study DE-EE0008103: Earth Source Heat: A Cascaded Systems Approach to DDU of Geothermal Energy on the Cornell Campus. This study involved a comprehensive evaluation of the potential for Earth Source Heat (ESH), Cornell’s specific application of Deep Direct Use (DDU) geothermal energy, to create a viable renewable source of thermal energy for its Ithaca, NY campus district heating system. The study included stochastic modeling that married each of two potential subsurface resources (two specific geological reservoirs) to documented campus heating operations based on hourly campus heating profiles and proposed integrated equipment controls (variable speed pumps and heat pumps). The outputs of the subsurface modeling were used as inputs for a Cornell-developed surface heat use Excel-based modeling program (the program was named “Modeled ENergy Use”, or “MEnU”). This allowed Cornell to compute realistic annual totals of extractable and useable heat for the campus, including details like heat losses, minimum summer demand, and controllable building temperature settings. The Cornell team then evaluated the costs and benefits of each modeled scenario. The primary evaluation was a standard “single bottom line” economic calculation for the Levelized Cost of Heat (LCOH). This represents the annualized cost (per unit heat) for Cornell. Additional valuations were also computed based on the benefit to the environment and to the regional economy (i.e., economic benefits external to campus). The results of this study demonstrate that if suitable reservoir flow can be attained, Earth Source Heat would be a viable technology to supply Cornell’s district heating system. This viability is based on the following criteria: (1) The modeled system produced a total useable heat output that exceeded the minimum annual campus heat load determined at the onset of the project (i.e., offsetting at least 20% of the annual campus thermal load). The output range is substantial depending on both the subsurface resource and the surface applications; at the high end, our modeled solutions produced up to ~70% of existing campus heat load with a single well pair producing 70 kg/s with integrated high-temperature heat pumps, and (2) Modeling demonstrated that multiple geothermal reservoirs could provide economically viable results when heat pumps were strategically integrated, a design that has been proven in at least one European installation already. In this context, “economically viable” means that the LCOH for the project is less than the regional commercial price of heat that would be generated using natural gas (the most common and cheapest fossil alternative in our area). Based on a single-bottom-line (LCOH) valuation, our results show that economic success is likely for any reservoir deeper than about 2.25 km where a sufficient permeability exists (or can be created with stimulation or similar engineering enhancement) and reasonable production temperatures sustained over time. The LCOH values of $\$ 4.77$ to $\$ 6.46$ per MMBtu are all lower than the comparable cost of providing heat energy with a natural gas boiler for a facility in the Northeast U.S. that obtains natural gas at commercial rates (Commercial heat costs would include at least $\$ 9.41$ in fuel costs, representing total boiler/transfer efficiency of 85% using gas at $\$ 8.00$ per MMBtu; LCOH would be higher if capital equipment and other operating costs were included). In addition to LCOH, Cornell’s scope also included evaluation of the Environmental value and Regional Economic value of each scenario. When considering the environmental and regional economic value of such a project, the overall “triple bottom line” cost (i.e., the LCOH less the LCOH ENV and LCOH REG values) is even lower and in some cases negative; i.e., in some cases a project achieving the listed performance would have higher value to the environment and to the regional economy than the total project costs over the 30-year project timeframe.

15 GEOTHERMAL ENERGY↗
DOE OSTI2010

Adaptive and Efficient Computing for Subsurface Simulation within ParFlow

This project is concerned with the PF.WRF model as a means to enable more accurate predictions of wind fluctuations and subsurface storage. As developed at LLNL, PF.WRF couples a groundwater (subsurface) and surface water flow model (ParFlow) to a mesoscale atmospheric model (WRF, Weather Research and Forecasting Model). It was developed as a unique tool to address coupled water balance and wind energy questions that occur across traditionally separated research regimes of the atmosphere, land surface, and subsurface. PF.WRF is capable of simulating fluid, mass, and energy transport processes in groundwater, vadose zone, root zone, and land surface systems, including overland flow, and allows for the WRF model to both directly drive and respond to surface and subsurface hydrologic processes and conditions. The current PF.WRF model is constrained to have uniform spatial gridding below the land surface and matching areal grids with the WRF model at the land surface. There are often cases where it is advantageous for land surface, overland flow and subsurface models to have finer gridding than their atmospheric counterparts. Finer vertical discretization is also advantageous near the land surface (to properly capture feedbacks) yet many applications have a large vertical extent. However, the surface flow is strongly dependent on topography leading to a need for greater lateral resolution in some regions and the subsurface flow is tightly coupled to the atmospheric model near the surface leading to a need for finer vertical resolution. In addition, the interactions (e.g. rain) will be highly variable in space and time across the problem domain so an adaptive scheme is preferred to a static strategy to efficiently use computing and memory resources. As a result, this project focussed on algorithmic research required for development of an adaptive simulation capability in the PF.WRF system and its subsequent use in an application problem in the Central Valley of California. This report documents schemes of use for a future implementation of an adaptive grid capability within the ParFlow subsurface flow simulator in PF.WRF. The methods describe specific handling of the coarse/fine boundaries within a cell-centered discretization of the nonlinear parabolic Richards equation model for variable saturated flow. In addition, we describe development of a spline fit and table lookup method implemented within ParFlow to enhance computational efficiency of variably saturated flow calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗
DOE OSTI2021

Modeling Variable Renewable Energy and Storage in the Power Sector

The emergence of variable renewable energy and battery storage technologies have fundamentally transformed the electric power sector and generated demand for analysis to understand their roles in future energy systems. Although unique characteristics of these resources are well-recognized and require more sophisticated methodologies to capture effectively, guidance is limited on best practices and research gaps. This paper selectively reviews recent literature and draws upon our collective modeling experience to offer recommendations to analysts and consumers of model outputs on approaches for modeling variable renewable energy and storage in long-term electric sector models. We focus on regional- and national-scale models with technological, temporal, and spatial detail given their prevalence in planning and policy analysis, though insights are applicable in other settings. The review highlights how the research frontier has advanced in representing renewables and energy storage over the past decade; however, given the many considerations involved with appropriately capturing salient economic and operational characteristics of renewables, there is a gap between commonly used models and state-of-the-art methods. Model simplifications can materially impact policy analysis associated with power sector decarbonization and high renewables deployment, and improving model representations of variable renewables can enhance insights for policymakers and other stakeholders. This review can point the way for improved methods for established models and designs for emerging ones.

energy and environmental policy↗
DOE OSTI2017

The role of density-dependent and –independent processes in spawning habitat selection by salmon in an Arctic riverscape

Density-dependent (DD) and density-independent (DI) habitat selection is strongly linked to a species’ evolutionary history. Determining the relative importance of each is necessary because declining populations are not always the result of altered DI mechanisms but can often be the result of DD via a reduced carrying capacity. Here, we developed spatially and temporally explicit models throughout the Chena River, Alaska to predict important DI mechanisms that influence Chinook salmon spawning success. We used resource-selection functions to predict suitable spawning habitat based on geomorphic characteristics, a semi-distributed water-and-energy balance hydrologic model to generate stream flow metrics, and modeled stream temperature as a function of climatic variables. Spawner counts were predicted throughout the core and periphery spawning sections of the Chena River from escapement estimates (DD) and DI variables. In addition, we used isodar analysis to identify whether spawners actively defend spawning habitat or follow an ideal free distribution along the riverscape. Aerial counts were best explained by escapement and reference to the core or periphery, while no models with DI variables were supported in the candidate set. Moreover, isodar plots indicated habitat selection was best explained by ideal free distributions, although there was strong evidence for active defense of core spawning habitat. These results are surprising, given salmon commonly defend spawning resources, and are likely due to competition occurring at finer spatial scales than addressed in this study.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Geospatial Modeling of Asthma Population in Relation to Air Pollution

Current observations indicate that asthma is growing every year in the United States, specific reasons for this are not well understood. This study stems from an ongoing research effort to investigate the spatio-temporal behavior of asthma and its relatedness to air pollution. The association between environmental variables such as air quality and asthma related health issues over Mississippi State are investigated using Geographic Information Systems (GIS) tools and applications. Health data concerning asthma obtained from Mississippi State Department of Health (MSDH) for 9-year period of 2003-2011, and data of air pollutant concentrations (PM2.5) collected from USEPA web resources, and are analyzed geospatially to establish the impacts of air quality on human health specifically related to asthma. Disease mapping using geospatial techniques provides valuable insights into the spatial nature, variability, and association of asthma to air pollution. Asthma patient hospitalization data of Mississippi has been analyzed and mapped using quantitative Choropleth techniques in ArcGIS. Patients have been geocoded to their respective zip codes. Potential air pollutant sources of Interstate highways, Industries, and other land use data have been integrated in common geospatial platform to understand their adverse contribution on human health. Existing hospitals and emergency clinics are being injected into analysis to further understand their proximity and easy access to patient locations. At the current level of analysis and understanding, spatial distribution of Asthma is observed in the populations of Zip code regions in gulf coast, along the interstates of south, and in counties of Northeast Mississippi. It is also found that asthma is prevalent in most of the urban population. This GIS based project would be useful to make health risk assessment and provide information support to the administrators and decision makers for establishing satellite clinics in future.

Kethireddy, Swatantra R.↗
NASA NTRS1981

Generalized variables for controlling Government Transfer Services with respect to performance measures

The term Government Transfer Services is used in reference to any of the organized streams of public resources that flow into private economic activity. This includes such activities as offshore leasing, Social Security, and NASA technology transfer services. This paper describes a performance measure, empirical results, a theory, and a control model for such services. These are illustrated by a specific example (NASA). An agenda for developing this service control method is also presented.

Johnson, F. D.↗