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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly Appliance TM (GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-D TM simulation environment.

Radhakrishnan, Nikitha↗
DOE OSTI2021

Hybrid Resources: Challenges, Implications, Opportunities, and Innovation

The electric power system has historically been designed to provide reliable energy to loads by using a relatively small number of well-understood generators. The distinction between load, generation, and transmission resources has been quite clear. Most of the responsibility for planning and operating a system - building a highly reliable network from less reliable parts - has been with the system manager, whether that be a utility, a regional market operator, or some similar entity. Given this historical context, many experts were initially perplexed by the rapidly growing popularity of hybrid resources, which combine multiple technologies into a single entity. Rather than depending on a system operator to provide instructions to individual technologies, hybrid resources intentionally take on more operational responsibility by optimizing and scheduling their combined functions. Interconnection queues in many regions reveal a large and growing interest in hybrids, suggesting that project developers and investors see them as providing advantages.

generators↗
DOE OSTI2024

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Experimental test of model predictive control in a variable air volume system

Model predictive control (MPC) has been widely studied as a promising approach for improving energy efficiency and operational flexibility in buildings, yet its real-world performance for commercial variable air volume (VAV) systems remains insufficiently characterized. In particular, the impacts of model mismatch on control robustness, real-time computational burden, and device-level operation are rarely evaluated using long-term field data. Here, this study presents a comprehensive experimental evaluation of MPC applied to a full-scale VAV system in Oak Ridge National Laboratory’s Flexible Research Platform-2 building with constant cooling/heating temperature setpoints and no occupancy. The study offers three key advantages over existing work: (1) it uses a representative building in a full-scale experimental test, capturing realistic system dynamics and complexity; (2) it evaluates a relatively sophisticated MPC formulation using two different optimization solvers (Gurobi and PSO), fully accounting for computational complexity and methodological diversity; and (3) it systematically assesses potential negative impacts on various building devices, benchmark against a well-established baseline, ASHRAE Guideline 36 (G36). To isolate zone- and air-handling-unit–level supervisory control effects, the supply fan was operated with a fixed static pressure setpoint under all strategies, and the trim-and-response static pressure reset in G36 was not enabled. Results show that MPC maintained thermal comfort while improving energy efficiency. Abrupt solar radiation variations degraded performance. Computation times ranged from ∼1 s (Gurobi) to ∼ 70 s (PSO). Compared with G36, MPC achieves 33% energy savings and reduces median reheat coil output by approximately a factor of 5–10 for a representative cooling day under matched weather conditions. However, it increases the maximum discomfort deviation from 0.5 to 1°C and results in a 32% increase in staging frequency. In addition, PSO-based MPC introduced damper oscillations, also affecting actuator longevity.

ASHRAE guideline 36↗
DOE OSTI2021

Integration of Simulated and Real Distributed Acoustic Sensor Measurements to Develop AI Enhanced Intelligent Sensing Operations

Distributed acoustic sensors (DAS) have shown remarkable success in monitoring critical civil, energy and transportation assets over long distances and under harsh environmental conditions. Integration of artificial intelligence (AI) technologies with DAS can extend their operational capabilities from conventional tasks (e.g., vibration frequency detection) to more advanced intelligent tasks like anomaly detection. However, these AI technologies often require large labeled/ annotated DAS measurements datasets of the assets in both normal and anomalous operating conditions. This data acquisition task can be both time consuming and costly. This work develops a generative adversarial network based unsupervised domain adaptation framework to build intelligent DAS operational capabilities.

Venketeswaran, Abhishek↗
DOE OSTI2021

Facility Safety Plan – Building 225

B225 is operated by the Weapons and Complex Integration (WCI) Directorate and is classified as a Low Hazard facility for radiological hazards and Light Science Industry (LSI) for chemical, radiation generating devices (RGDs) and industrial hazards. Explosives and biological hazards are not authorized for B225. B225 is located in the southwest quadrant of Lawrence Livermore National Laboratory (LLNL) less than 600 meters from the nearest site boundary. B225 is a single-story concrete tilt-up building with a steel deck roof. The primary mission of this facility is to apply nondestructive evaluation (NDE) methods to materials, components, and assemblies. B225 also contains the polymer additive manufacturing laboratories. The floor plans for B225 are provided in Appendix A.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Optimizing Price-Informed Operation of a Battery Storage System in an Office Building

New prescriptive efficiency requirements amended to US model energy codes historically have been evaluated using an average, blended electricity rate, which obscures demand charges. Post 2019, new measures can be evaluated using a representative time-of-use (TOU) tariff yet more sophisticated analysis methods are needed to consider a variety of TOU tariffs and assess price-informed control. To address these needs, this study couples building prototype simulation model with varying-in-sophistication battery storage operating strategies for different electricity TOU tariffs. The analysis compares the impact of operating a battery storage system following simpler rule-of-thumb methods versus a semi-optimized priced-informed heuristic approach. The investigation demonstrates that the heuristic approach results in greater electricity cost savings and is practical to implement.

Lei, Xuechen↗
DOE OSTI2024

Strategies for connecting whole-building LCA to the low-carbon design process

Abstract Decarbonization is essential to meeting urgent climate goals. With the building sector in the United States accounting for 35% of total U.S. carbon emissions, reducing environmental impacts within the built environment is critical. Whole-building life cycle analysis (WBLCA) quantifies the impacts of a building throughout its life cycle. Despite being a powerful tool, WBLCA is not standard practice in the integrated design process. When WBLCA is used, it is typically either speculative and based on early design information or conducted only after design completion as an accounting measure, with virtually no opportunity to impact the actual design. This work proposes a workflow for fully incorporating WBLCA into the building design process in an iterative, recursive manner, where design decisions impact the WBLCA, which in turn informs future design decisions. We use the example of a negative-operational carbon modular building seeking negative upfront embodied carbon using bio-based materials for carbon sequestration as a case study for demonstrating the utility of the framework. Key contributions of this work include a framework of computational processes for conducting iterative WBLCA, using a combination of an existing building WBLCA tool (Tally) within the building information modeling superstructure (Revit) and a custom script (in R) for materials, life cycle stages, and workflows not available in the WBLCA tool. Additionally, we provide strategies for harmonizing the environmental impacts of novel materials or processes from various life cycle inventory sources with materials or processes in existing building WBLCA tool repositories. These strategies are useful for those involved in building design with an interest in reducing their environmental impact. For example, this framework would be useful for researchers who are conducting WBLCAs on projects that include new or unusual materials and for design teams who want to integrate WBLCA more fully into their design process in order to ensure the building materials are consciously chosen to advance climate goals, while still ensuring best performance by traditional measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Low Carbon Technology Strategies: Commercial Kitchen

This document is a supplement to Low Carbon Technology Strategies for building types that typically include commercial kitchens. It includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon commercial kitchens.

carbon reduction, carbon emissions, low carbon, de↗
DOE OSTI2024

Long-Term Assessment of Commercial Building Energy and Carbon Emissions in the Northwestern Region Under Future Weather Trend

The future climate significantly impacts building performance and increases uncertainties in energy simulations. A rising temperature trend is expected to heighten cooling loads during summer and result in more carbon emissions. Understanding the impact of future climate on building performance is significant for policymakers to make informed decisions. Building retrofit measures can improve building energy efficiency and reduce operational carbon emissions, yet their effects under future climate conditions have not been fully investigated so far. Thus, we proposed an assessment methodology for evaluating long-term energy consumption and operational carbon reduction potential using a building stock dataset. For this study, commercial buildings in the northwestern (NW) region were utilized to assess the impacts of future climate and building retrofit. In addition, we selected Montana with a cold and dry climate as an example to analyze and discuss the carbon emission reduction potential in buildings. The main findings are: (1) Under future climate trends, changes in energy use intensity (EUI) will fluctuate due to variations in heating and cooling degree-days (HDDs and CDDs) and increasing HDDs will lead to increasing EUI. (2) After applying annual building retrofitting, the long-term EUI reduction potential of buildings in the NW region will decrease with the increasing retrofitting degree, and the short-term EUI reduction potential will be impacted by the change of heating and cooling degree days. (3) In Montana, the long-term carbon intensity reduction potential of retrofitted buildings will decrease under future climate trends with the increasing renewable energy penetration.

building energy modeling↗
DOE OSTI2021

GT Flex: A Coordinated Multi-Building Pilot Study

Buildings are a significant and untapped resource for providing utility electric grid services. Recent studies have estimated that buildings could reduce the peak demand on the electric grid in the U.S. by almost 25% through effective combinations of energy efficiency (EE) measures and load flexibility strategies (Langevin et al. 2021). The U.S. Department of Energy (DOE) has established a goal to triple energy efficiency and demand flexibility in both residential and commercial sections by 2030 compared to 2020 levels (Satchwell et al. 2021). Such findings place buildings alongside electric vehicles, photovoltaics, electric batteries, and other distributed energy resources (DERs) as primary technologies needed for supporting high renewable energy generation grids. Coordinating and optimizing multiple buildings and other DERs is more beneficial and valuable when compared with individual buildings and DERs operating as siloed resources, uncoordinated with others (Olgyay et al. 2020). A pilot study at the Georgia Institute of Technology (GIT) was conducted to evaluate value propositions of a multi-building scale project seeking carbon reduction, energy efficiency and grid-interactive capabilities, by demonstrating the means by which stakeholders can determine the technical and financial merits of grid-interactivity and energy efficiency technologies coordinated across multiple assets. The study focused on analyzing technical feasibility of deploying thermal load flexibility strategies at the multi-building scale, coordinated to not exceed existing infrastructure constraints at the pilot site. Results show that campus can provide 3-3.5 MW of potential load shed over a 4-hour event window through coordinated dispatch of thermal cooling load flexibility without exceeding existing infrastructure capacities. Under future high renewable scenarios, this thermal flexibility resource is also valuable when coordinated to reduce curtailment of intermittent renewables. Economic analyses were performed to effectively communicate various value propositions of grid-interactive and efficient building (GEB) thermal flexibility strategies. Load flexibility presents a financial value proposition to the campus today. By conducting rationalized, coordinated dispatch in response to real time price (RTP) fluctuations, the campus can benefit materially from daily price arbitrage. The RTP signal acts as an aggregating mechanism between the utility and customer to call on demand flexibility resources, with a large portion of the benefit deriving from a relatively small number of days. Realizing and maximizing this benefit with thermal load flexibility requires careful attention to the timing of pricing signals and parameterization of dispatch to overcome efficiency penalties. Grid value and signals are expected to evolve over time, and thermal load flexibility shows potential to adapt dispatch logic to support intermittent renewable generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Comparison of effectiveness and energy use of airborne pathogen mitigation measures to meet clean air targets in a prototypical office building

Organizations such as ASHRAE and the Centers for Disease Control and Prevention (CDC) have proposed guidelines for controlling infectious aerosols in buildings, which can be met through measures such as modified operation of the heating, ventilation, and air-conditioning (HVAC) system or incorporating air-cleaning technologies. However, more research is needed to understand the trade-offs between health, energy, and comfort aspects when designing measures for these guidelines. To address this gap, this paper presents an analysis using new models for air-cleaning technologies, including in-duct and in-room germicidal ultraviolet (GUV) systems and portable air cleaners (PACs). These models are incorporated into an existing prototypical office building model and six measures are designed to meet ASHRAE Standard 241 and CDC clean air targets: MERV 13 HVAC filtration, maximum outdoor air supplied to the building, PACs, and in-duct, upper-room, and whole-room GUV. The measures are simulated for an office building in a cool and humid climate compared against a baseline simulation using MERV 8 filtration. The results show that all measures, except for the maximum outdoor air case, can meet the ASHRAE 241 standard without significant impacts on energy or comfort. The HVAC system measures were not able to meet the CDC target with the default system sizing and lead to significant energy increases, while the in-room measures were able to meet the CDC target with small impacts on energy consumption. Finally, this paper consolidates the simulation findings and provides practical guidance for building operators to meet clean air targets while limiting energy and comfort impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings

We report the complexity of occupant behavior is one of the major contributors to uncertainty in building performance simulation. Agent-based modeling (ABM), a computational simulation technique, has gained attention in the occupant modeling field due to its capability and flexibility to capture the heterogeneity and dynamics of human behavior and the emergent effects. While multiple efforts in the past decade have demonstrated the usefulness of the ABM approach for simulating occupants and their impacts on building performance, several crucial matters in the ABM research still remain unexplored. This paper presents ten questions that highlight the most important issues regarding ABM research and applications for occupant behavior in the context of building performance simulation. The questions and answers aim to provide insights into current and future ABM research, and more importantly to inspire new significant questions from young researchers in the field. This research is part of the IEA EBC Annex 79 project, occupant-centric building design and operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Performance Demonstration of an Occupancy Sensor-enabled Integrated Solution for Commercial Buildings

Traditionally, a single-loop fixed-gain controller is applied to supply fan (SF) and cooling coil (CC) valve controls while a fixed-damper position control is applied to outdoor air (OA) damper control at air handling units (AHUs) in commercial buildings. With the increasing application of occupancy sensors, the information generated by occupancy sensors is applied to not only reduce the electricity loads from lighting and controllable plug loads, but also reset OA intake and minimum supply airflow setpoints. Meanwhile, these intermittent operation actions greatly elevate the dynamics of AHU systems, which may introduce unstable SF and CC valve operations and inaccurate OA flow control at AHUs and consequently degrade maximum energy efficiency gains. With virtual fan and valve flow meter technologies, two advanced controls, including cascade control and gain scheduling control, can be implemented on both the SF and CC valve, and an advanced control using a virtual OA flow meter can be implemented on OA damper integrated with occupancy sensors. The goal of this project is to demonstrate the savings, cost, and performance of an integrated solution that integrates the three advanced HVAC controls with occupancy sensors to allow accurate and stable AHU operations in real buildings. The project objectives are to: 1) develop and validate an advance SF control algorithm; 2) develop and validate an advanced CC valve control algorithm; 3) validate an algorithm to implement a virtual OA flow meter; and 4) demonstrate the savings, cost, and performance of the integrated solution in real buildings. The technical approaches are to: 1) select a test system at the University of Oklahoma; 2) develop and implement the algorithms of advanced SF and CC valve controls and validate the performance; 3) develop and implement the advanced OA control using a virtual OA flow meter and validate the performance; 4) demonstrate the savings, cost, and performance of the proposed integrated solution with and without three advanced HVAC controls; and 5) disseminate the project results through publications and presentations. For the SF control, both the gain scheduling and cascade controls can improve the fan energy performance by reducing the fan power during the transient period and the fan control performance at lower speeds by reducing fan speed variation. Moreover, the gain scheduling control provides a simple and low-cost solution and is recommended. The fan power savings can reach 30% in a transient period. For the CC valve control, the gain scheduling control can considerably reduce the supply air temperature oscillation range and frequency under both higher and lower load conditions and the control valve response is much more stable. As a result, the gain scheduling control is recommended. The projected pump energy consumption can be reduced by 68.5%. With the developed virtual OA flow meter, the OA can be accurately controlled at its setpoint, which is determined based on the actual number of occupants in the building provided by occupancy sensors. The RMSE of the proposed OA control is 15.9 L/s. The energy data shows that the fan power and CC cooling energy were significantly reduced. On the other hand, the energy savings majorly results from the occupancy sensors and the energy savings by the advanced HVAC controls is minimal because that the controllers in the test AHU were tuned with very slow response. An annual technical savings potential is estimated as 0.5 quads in the commercial sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Automatic and rapid calibration of urban building energy models by learning from energy performance database

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. Additionally, this practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m 2 was calibrated to be 7.50 W/m 2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Energy Efficiency Assessment for a Microgrid in the Community of Castañer, Puerto Rico

The Cooperativa Hidroeléctrica de la Montaña (Cooperativa) and the Interstate Renewable Energy Council office in Puerto Rico (IREC) are leading the development of a community solar photovoltaic (PV) microgrid comprised of a 225 kWDC photovoltaic system tied to a 500 kWh storage system in the Community of Castañer, in the town of Lares in Puerto Rico. This microgrid will support the electricity needs of eight essential businesses and four homes. The Cooperativa and IREC requested technical assistance to help conduct a comprehensive energy efficiency assessment for the commercial buildings to maximize the impact of the solar PV microgrid. The goal was to identify energy conservation measures that would have the highest impact in terms of reducing electricity demand in order to; reduce the size and cost of the PV microgrid; and reduce the annual operating costs for building owners.

14 SOLAR ENERGY↗