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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Solar Schematic

The home shown at right is specially designed to accommodate solar heating units; it has roof planes in four directions, allowing placement of solar collectors for best exposure to the sun. Plans (bottom) and complete working blueprints for the solar-heated house are being marketed by Home Building Plan Service, Portland, Oregon. The company also offers an inexpensive schematic (center) showing how a homeowner only moderately skilled in the use of tools can build his own solar energy system, applicable to new or existing structures. The schematic is based upon the design of a low-cost solar home heating system built and tested by NASA's Langley Research Center; used to supplement a warm-air heating system, it can save the homeowner about 40 percent of his annual heating bill for a modest investment in materials and components. Home Building Plan Service saved considerable research time by obtaining a NASA technical report which details the Langley work. The resulting schematic includes construction plans and simplified explanations of solar heat collection, collectors and other components, passive heat factors, domestic hot water supply and how to work with local heating engineers.

Source record↗
DOE OSTI2020

Scheduling and Control of Flexible Building Loads for Grid Services Based on a Virtual Battery Model

This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize the aggregate flexibility from heterogeneous TCLs using a battery-equivalent model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual TCLs to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.

Wu, Di↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km 2 service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Resilient Information Architecture Platform for Smart Grid (RIAPS)

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1996

Implementation of a Campuswide Distributed Mass Storage Service: the Dream Versus Reality

In 1990, a technical team at NASA Lewis Research Center, Cleveland, Ohio, began defining a Mass Storage Service to pro- wide long-term archival storage, short-term storage for very large files, distributed Network File System access, and backup services for critical data dw resides on workstations and personal computers. Because of software availability and budgets, the total service was phased in over dm years. During the process of building the service from the commercial technologies available, our Mass Storage Team refined the original vision and learned from the problems and mistakes that occurred. We also enhanced some technologies to better meet the needs of users and system administrators. This report describes our team's journey from dream to reality, outlines some of the problem areas that still exist, and suggests some solutions.

Prahst, Stephen↗
DOE OSTI2022

Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

A transition from generation on demand to consumption on demand is one of the solutions to overcome the many limitations associated with the higher penetration of renewable energy sources. Such a transition, however, requires a considerable amount of load flexibility in the demand side. Demand response (DR) programs can reveal and utilize this demand flexibility by enabling the participation of a large number of grid-interactive efficient buildings (GEB). Existing approaches on DR require significant modelling or training efforts, are computationally expensive, and do not guarantee the satisfaction of end users. To address the aforementioned limitations, this software code considers a scalable hierarchical model-free transactional control approach that incorporates elements of virtual battery, game theory, and model-free control (MFC) mechanisms. The developed approach separates the control mechanism into upper and lower levels. The MFC modulates the flexible GEB in the lower level with guaranteed thermal comfort of end users in response to the optimal pricing and power signals determined in the upper layer using a Stackelberg game integrated with aggregate virtual battery constraints. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable even on small, embedded devices. This software code enables the use of residential and small-size commercial buildings to offer a potentially substantial source of ancillary grid services that are currently underutilized. A hierarchical, model-free transactive building control provides a smooth interface between the grid service requests of utilities and the reliable control required by participating buildings. Model-free control, which supports distributed control architecture, will be a more scalable solution that can be deployed in neighborhood-size systems as well as individual buildings. The proposed approach contributes to the body of knowledge on two main aspects. First, it couples the MFC with the game-theoretic control and proposes a scalable model-free transactive control approach. MFC does not require any modeling effort or model training for the various building loads. This is very beneficial since deriving an accurate model for every single unit participating in DR programs and obtaining all the parameters about the units (e.g., thermal coefficients, standby losses) are infeasible. Also, MFC is computationally efficient, easily deployable even on small, embedded devices, and can be implemented in real time. Second, it integrates the concept of virtual battery into DR via the Stackelberg game. The concept of virtual battery enables efficient coordination and aggregation of a large number of flexible GEB with guaranteed thermal comfort of end users.

Olama, Mohammed [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2023

The Integration of Wi-Fi Location-Based Services to Optimize Energy Efficient Commercial Building Operations

This project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at commercial buildings located in Saint Paul, MN; Madison, WI; New York City; and Fort Worth, TX.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

helios: An R package to process heating and cooling degrees for GCAM

helios is an open-source R package that estimates population-weighted heating and cooling degree-hours (HDH and CDH) and degree-days (HDD and CDD) at various temporal (e.g., energy dispatch segments, monthly, yearly) and spatial scales (e.g., U.S. states, global political regions, countries). The degree hour and degree day outputs from helios are used to inform electricity demand load in the Global Change Analysis Model (GCAM) as well as in GCAM-USA (which is the version of GCAM with U.S. state-level details). helios uses a workflow with four steps: processing raw data; calculating heating and cooling degrees; visualizing performance diagnostics; and outputing results in various formats. There are two sources of widely-used climate data compatible with helios: (1) hourly climate data with 12-km resolution that are dynamically downscaled with the Weather Research and Forecasting (WRF) model and projected using a thermal global warming (TGW) approach; and (2) daily climate data with 0.5-degree resolution from the Coupled Model Intercomparison Project (CMIP) that is bias-adjusted and statistical downscaled by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In summary, helios is a model that standardizes methodology of heating and cooling degrees-hours and degree-days using publicly available data and advance the understanding of the impact of spatial and temporal temperature variability on building energy services.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2011

Using Open and Interoperable Ways to Publish and Access LANCE AIRS Near-Real Time Data

The Atmospheric Infrared Sounder (AIRS) Near-Real Time (NRT) data from the Land Atmosphere Near real-time Capability for EOS (LANCE) element at the Goddard Earth Sciences Data and Information Services Center (GES DISC) provides information on the global and regional atmospheric state, with very low temporal latency, to support climate research and improve weather forecasting. An open and interoperable platform is useful to facilitate access to, and integration of, LANCE AIRS NRT data. As Web services technology has matured in recent years, a new scalable Service-Oriented Architecture (SOA) is emerging as the basic platform for distributed computing and large networks of interoperable applications. Following the provide-register-discover-consume SOA paradigm, this presentation discusses how to use open-source geospatial software components to build Web services for publishing and accessing AIRS NRT data, explore the metadata relevant to registering and discovering data and services in the catalogue systems, and implement a Web portal to facilitate users' consumption of the data and services.

Zhao, Peisheng↗
DOE OSTI2021

Blueprint for Integrating Grid-Interactive Efficient Building (GEB) Technologies into U.S. General Services Administration Performance Contracts

This document provides recommended strategies for increasing implementation of grid-interactive efficient building (GEB) measures in federal performance contracts. This document outlines distributed energy resource screening processes, contractual considerations for GEB technologies within performance contracts, and guidance for integration of GEB analysis procedures and technologies into each phase of a performance contract.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
NASA NTRS2014

Integrating Space Communication Network Capabilities via Web Portal Technologies

We have developed a service portal prototype as part of an investigation into the feasibility of using Java portlet technology as a means of providing integrated access to NASA communications network services. Portal servers provide an attractive platform for this role due to the various built-in collaboration applications they can provide, combined with the possibility to develop custom inter-operating portlets to extent their functionality while preserving common presentation and behavior. This paper describes various options for integration of network services related to planning and scheduling, and results based on use of a popular open-source portal framework. Plans are underway to develop an operational SCaN Service Portal, building on the experiences reported here.

web services↗
NASA NTRSDate not supplied

Small Spacecraft Sample Return to Support Gateway and Lunar Science

The Lunar Gateway is a planned orbital outpost to support Lunar surface, Cislunar, and deep space exploration activities. NASA, together with international and commercial partners, are providing various capabilities, infrastructure, and services to build the Lunar economy. As Gateway capabilities and transportation logistics evolve, utilization is expected to increase, providing ample science, technology demonstration, and commercial development opportunities. Elements of the transportation network supporting Lunar activities are primarily focused on the outbound segment, which include Commercial Lunar Payload Services (CLPS), Human Landing System (HLS), Deep Space Logistics (DSL), and the SLS/Orion crew transportation system. Initially, the only Earth return segment will be provided via Orion. However, infrequent mission cadence (once every 12 months), limited payload return mass (100 kg), and operational constraints suggest that additional sample return logistics capability will be needed. Sustaining long-term presence at the Moon will likely require innovative approaches for frequent and affordable payload return. NASA Ames Research Center and the DSL team at Kennedy Space Center (which provides the Gateway Logistics Services missions) have investigated the development of a small spacecraft-based sample return capability to complement Orion. The goal of the first mission is to demonstrate the capability as a part of an early DSL mission, and provide up to 10 kg of scientific sample return from the Gateway. The mission concept envisions the progressive addition of sample return capabilities, including returning temperature- and acceleration-sensitive payloads, and evolution into a commercially provided service, similar to existing ISS payload return logistics. An overview of payload science and technology use cases and small spacecraft mission concepts will be presented to engage scientists, payload developers and mission planners who are considering Lunar exploration activities that will require the return of high-value samples from the Gateway and/or the lunar surface.

Alan Cassell↗
NASA NTRSDate not supplied

Small Spacecraft Sample Return Mission Concept to Support Gateway and Lunar Science

The Lunar Gateway is a planned orbital outpost to support Lunar surface, Cislunar, and deep space exploration activities. NASA, together with international and commercial partners, are providing various capabilities, infrastructure, and services to build the Lunar economy. As Gateway capabilities and transportation logistics evolve, utilization is expected to increase, providing ample science, technology demonstration, and commercial development opportunities. Elements of the transportation network supporting Lunar activities are primarily focused on the outbound segment, which include Commercial Lunar Payload Services (CLPS), Human Landing System (HLS), Deep Space Logistics (DSL), and the SLS/Orion crew transportation system. Initially, the only Earth return segment will be provided via Orion. However, infrequent mission cadence (once every 12 months), limited payload return mass (100 kg), and operational constraints suggest that additional sample return logistics capability will be needed. Sustaining long-term presence at the Moon will likely require innovative approaches for frequent and affordable payload return. NASA Ames Research Center and the DSL team at Kennedy Space Center (which provides the Gateway Logistics Services missions) have investigated the development of a small spacecraft-based sample return capability to complement Orion. The goal of the first mission is to demonstrate the capability as a part of an early DSL mission, and provide up to 10 kg of scientific sample return from the Gateway. The mission concept envisions the progressive addition of sample return capabilities, including returning temperature- and acceleration-sensitive payloads, and evolution into a commercially provided service, similar to existing ISS payload return logistics. An overview of payload science and technology use cases and small spacecraft mission concepts will be presented to engage scientists, payload developers and mission planners who are considering Lunar exploration activities that will require the return of high-value samples from the Gateway and/or the lunar surface.

Small Spacecraft↗