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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 OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Simple Building Calculator

Whole building energy modeling is a powerful tool for analyzing energy use in buildings. For large buildings benefits of modeling services can easily be quantified due to the short payback period of the implemented measures. For small buildings, the upfront costs of developing robust energy models often deter building owners from investing in energy modeling services. To address this shortcoming, a ”Simple Building Calculator” (SBC) was developed. SBC uses pre-simulated results of a range of common measures over a wide range of efficiency inputs. It combines whole building simulation results with statistical modeling techniques to predict energy impact of measures very quickly. In this paper we present the modeling methodology used to develop the data supporting SBC.

Building Energy Simulation, commercial building, E↗
DOE OSTI2022

Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review

The modern power grid faces multiple challenges due to an increase in the adoption of renewable generation, such as dynamically balancing supply and demand at different time scales. Demand side management in buildings plays a vital role in achieving this balance because buildings can provide grid services through a variety of building assets. However, the development of grid-interactive, efficient buildings is still in its infancy, and a systematic and holistic understanding of grid service delivery strategies in terms of energy efficiency, load shifting, load shedding and load modulating is still limited. This paper is a comprehensive review of the development and application of building-level control strategies for utilizing heating, ventilating, and air conditioning systems to provide grid services. These strategies have been investigated through numerical and experimental studies. Control algorithms, such as heuristic rule-based control and model-based control, have been used to enable the automatic control delivery of grid services. The advantages and disadvantages of the strategies are summarized and discussed. Finally, research trends are also identified, which include considering predicted mean vote-based and occupant-based thermal comfort, modeling of occupant behavior, integrating power grid operations with building control, and combining different demand flexibility modes in the control design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

DE-EE0007069 – Location, Location, Efficiency! (L.L.E!) (Final Technical Report)

As a participant in Department of Energy's national Better Buildings Challenge, the City of Milwaukee's Environmental Collaboration Office and local partners developed a program to bundle and deliver energy efficiency services to commercial buildings and help bring energy efficiency technologies developed by DOE's Building Technology Office to market. This report summarizes the programs and its results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗
DOE OSTI2022

MPC solution for optimal load shifting for buildings with ON/OFF staged packaged units: Experimental demonstration, and lessons learned

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. In this work, two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Maximizing the Benefits of On-Site Renewable Energy Generation Using On-Site Energy Storage

The commercial and industrial building sectors have a major role to play in a U.S. low-carbon future. Building owners can make a difference by deploying on-site renewable generation and storage technologies, which facilitates future-proofing their buildings as well as more effectively meeting the service requirements of the building. This resource provides an overview of common generation, storage, and load management technologies in behind-the-meter systems, and serves as a primer to demonstrate the impact on net building loads required from the grid for different sizes of renewable generation along with on-site electrical and thermal storage. These discussions can help commercial building owners communicate the value of these systems to project teams and develop innovative behind-the-meter solutions to achieve their energy goals.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration

Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems

With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Reduced Contracting Time To Accelerate Construction: A Streamlined Approach for Speed and Scale

In the fast-paced construction world, efficiency isn't just an advantage - it's a necessity. By implementing the Cooperative Construction Contracting Agreement (CCCA), we are transforming how we approach major projects, replacing traditional multistep contracting with a streamlined, one-step process. This innovative contracting approach saves valuable time and resources, enabling us to accelerate delivery across our portfolio of projects. Under the old model, each construction phase required separate competitive solicitations for design, build, and other services. With the CCCA, we've consolidated these steps, working with a trusted partner from start to finish. This results in significant time savings that translate into faster project execution without compromising quality or safety.

collaborative partnerships↗
DOE OSTI2025

Energy innovation in the US buildings sector: Setting the stage and mapping the future

Jared Langevin is a staff scientist at Lawrence Berkeley National Laboratory, where he leads modeling of US buildings sector innovation and its implications for energy demand, consumer costs, and the power grid. Eric Wilson is a senior research engineer in the Building Technologies and Sciences Center at the National Renewable Energy Laboratory (NREL). Much of his 15-year career at NREL has revolved around modeling and analysis of the US building stock. Jared and Eric co-led the development of a National Blueprint for buildings sector innovation while serving as advisors to the US Department of Energy’s Deputy Assistant Secretary for Buildings and Industry.

Langevin, Jared↗
DOE OSTI2022

Building Blocks for Secure and Prosperous Defense Critical Supply Chains: A Case Study from Microelectronics

Securing defense-critical supply chains, built on resilient and sustainable microelectronics fabrication and deployment, is a national imperative and one that requires an investment in basic and applied research, development, and deployment into industries in (and out of) the Defense Industrial Base (DIB). Critical next steps include the development of pilots for revolutionary concepts in a new formal verification model and the Cyber-Physical Passport (CPP) concept for chain of custody. Critical infrastructure leadership should take action with a sense of urgency. Working in conjunction with the Under Secretary for Acquisition and Sustainment, the Assistant Secretary of Defense for Research and Engineering should establish pilot programs with the Defense Advanced Research Projects Agency and the service labs to build and test both concepts in legacy and new system development.. Additionally, the Pentagon may be aware that an existing Manufacturing Innovation Institute is piloting, with the semiconductor industry, cyber innovations to provide verifiable security properties and guarantees of physical functions to build a more cyber secure, resilient and efficient micro-electronics supply chain.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Assessment of Low-Cost Minisplit Heat Pump Connection System

This project was funded by SCE's Emerging Technologies Program, and evaluates the feasibility of a connection system that can pave the path to accelerated, cost-effective adoption of high-efficiency Minisplit Heat Pump (MSHP) systems. This report details the characterization of this connection system and a heat pump enabled with this technology in a laboratory setting, and assesses the energy and cost impact applicable to residential buildings in SCE service territory. The energy and economic analysis leverages NREL's ResStock TM modeling approach based on the EnergyPlus ® hourly simulation platform. The connection system can be incorporated into existing MSHP architectures. This reduces installation time from 10 to 20 man hours to approximately one hour or less, dramatically lowering total installation costs without adversely impacting heat pump performance. This report characterizes the connection system's leakage performance while connected and disconnected, as well as leakage over several connection/disconnection cycles, commensurate with manufacturer specifications for the individual components. The connector was incorporated into an off-the-shelf MSHP. Performance was compared to an identical unmodified heat pump. The connector had no impact on performance. Large-scale hourly energy simulations were performed for 22,574 homes across 15 counties in SCE service territory. The analysis was performed using baseline assumptions about the penetrations of window air conditioners (~18%) and six upgrade scenarios for these air conditioners. All scenarios assumed 100% adoption of the Connector-Supported Heat Pump (CSHP) in place of window air conditioners: MSHPs at Seasonal Energy Efficiency Ratio (SEER) 17, 25, and 33, and CSHPs at SEER 17, 25, and 33. Results show the increased adoption of high-efficiency heat pumps can result in up to 29% air conditioning energy savings for homes with window air conditioners.

42 ENGINEERING↗
DOE OSTI2020

Connected Communities: A Multi-Building Energy Management Approach

Connected Communities (CCs) are collections of buildings and distributed energy resources (DERs) that incorporate integrated energy management strategies at the multi-building scale. Based on the research conducted for this report, RMI and NREL have identified examples of CCs that are enabling increasingly integrated solutions, sharing resources among buildings, and providing services back to the grid. As demonstrated by these examples, CCs can provide real and scalable solutions to mitigate energy costs, enable more flexibility in grid operations, and offer choices in meeting greenhouse gas (GHG) emissions goals. Nationwide examples of these solutions are included in the appendix of this report. In order to understand the most promising practices for the design, operation, and business models associated with CCs, a search was performed to create a nationwide inventory of CCs and multiple stakeholder interviews were conducted. Based on that research, this paper explores the many factors that influence the development and operation of CCs and evaluates their potential value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER

Tapping into the flexibility of aggregations of Grid-interactive and Efficiency Buildings (GEBs) represent a large opportunity to cost-effectively improve operations in future low-carbon power grids. The US Department of Energy's Connected Communities program seeks to demonstrate collections of these GEBs at 10 different sites across the country. However, consistent metrics for evaluation across all projects are needed to build confidence in the approach. This paper presents the metrics relating to Grid Service Provision that will be computed at the 10 demonstrations. The metrics proposed quantity the magnitude of service offers, the consistency and quality of services provided by the community, and the individual DER contributions to the community-level service. The goal of this work is to present these metrics and describe some of their intended insights. The hope is that these insights will be useful and build confidence for grid operators, regulators, and aggregators and practitioners as they look to deploy these resources in grids of the future.

MacDonald, Jason S↗
DOE OSTI2021

Energy Sector Position, Navigation, and Time Cybersecurity Profile

The Pacific Northwest National Laboratory has developed this positioning, navigation, and timing (PNT) profile for the Energy Sector at the request of the U.S. Department of Energy. The Department of Energy is the sector-specific agency for the Energy Sector and is responsible for providing guidance in the form of this PNT Profile, per Executive Order 13905, Strengthening National Resilience Through Responsible Use of Positioning, Navigation, and Timing Services (Executive Office of the President 2020) issued February 18, 2020. Executive Order 13905 directed the National Institute of Standards & Technology to develop a PNT Profile that is broadly applicable to all sectors and to serve as a foundation for sector-neutral guidance, NISTIR 8323 Foundational PNT Profile: Applying the Cybersecurity Framework for the Responsible Use of Positioning, Navigation, and Timing (PNT) Services. This document builds on NISTIR 8323 to identify and serve as the required Energy Sector specific guidance. For purposes of this PNT Profile, Energy Sector precise time is 0.5 µs (microseconds) to 1 µs accuracy. Currently, the electricity subsector has systems that use this level of precision time, but the oil and natural gas subsector currently does not. While the electricity subsector uses precision timing, it does not use positioning and/or navigation at this level of accuracy. For this reason, this PNT Profile will focus on only precise time for the electricity subsector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Technical Assistance for Rural Alaska Tribal Energy Solutions (Final Technical Report)

The original intent of this project was to provide technical assistance (TA) to rural Tribal communities across all regions of Alaska with a focus on public infrastructure. The Alaska Native Tribal Health Consortium (ANTHC) met this goal by providing more than 48 instances of energy-related technical assistance to rural Alaska Native communities, many of which led to them securing funding and implementing new projects that contributed to reducing the high cost of energy. Additionally, the Department of Energy (DOE) Office of Indian Energy staff saw the benefits of the energy technical assistance services being provided by ANTHC and, recognizing the value of having a trusted tribal non-profit entity providing technical support to its membership, amended the project to include standardization of TA services, internal capacity building, and regional outreach. This amended scope allowed ANTHC to streamline and standardize its technical assistance process, provide outreach to rural Alaska Native communities about DOE and ANTHC’s technical assistance program, and improve the ability of staff to provide even higher quality technical deliverables in support of energy projects. Overall, this project was successful in using technical assistance to empower both rural Alaska Native communities and the Alaska Native Tribal Health Consortium to develop and deploy energy efficiency and renewable energy solutions that will reduce the extremely high energy cost burdens facing Tribal communities in Alaska.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗