Building Technologies Office

These reports evaluate state-of-the-art and emerging building technologies that have significant potential to provide grid services. The reports also identify major research challenges and gaps facing the technologies as well as opportunities for technology-specific R&D. The GEB Technical Report Series will help inform and guide BTO's portfolio and serve as a foundational resource for the larger building research community. On-site behind-the-meter generation, battery storage, and electric vehicles are also an important part of the distributed energy resource (DER) optimization strategy for buildings. In general, the component technology reports do not focus on distributed generation or battery storage, but the Whole-Building Controls, Sensors, Modeling, and Analytics report discusses how a building can optimize across all DERs.

Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.

Buildings consume more than 30% of the total primary energy expended worldwide and contribute to a third of the world?s greenhouse gas (GHG) emissions. In the United States, buildings consume more than 40% of the total energy and contribute almost 38% of GHG emissions. In addition, the buildings in the United States consume more than 75% of the electricity generated. The need to mitigate climate change is driving efforts to make U.S. electric power generation cleaner, and this provides new impetus for improving the operating efficiency of buildings at scale and increasing the hosting capacity of distributed renewable generation. Because these renewable generation technologies are variable in nature, they create significant short- and long-term imbalances between supply and demand.

The dynamics of the turbulent atmospheric boundary layer play a fundamental role in wind farm energy production, governing the velocity field that enters the farm as well as the turbulent mixing that regenerates energy for extraction at downstream rows. Understanding the dynamic interactions among turbines, wind farms, and the atmospheric boundary layer can therefore be beneficial in improving the efficiency of wind farm control approaches. Anticipated increases in the sizes of new wind farms to meet renewable energy targets will increase the importance of exploiting this understanding to advance wind farm control capabilities. This review discusses approaches for modeling and estimation of the wind farm flow field that have exploited such knowledge in closed-loop control, to varying degrees. We focus on power tracking as an example application that will be of critical importance as wind farms transition into their anticipated role as major suppliers of electricity. The discussion highlights the benefits of including the dynamics of the flow field in control and points to critical shortcomings of the current approaches.

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

The introduction of smart sensing, metering, and control technology is transforming the nature of our power system from end to end.

• Assessment of thermal resilience in residential buildings is essential in cold climates • High-performance windows can extend thermal survivability time by up to 3.8 days • High-performance windows can greatly lower the risk of frozen water pipes bursting • Upgrading windows in older homes can lower HVAC energy use by up to 18% • Thermal resilience requirements should be included in building codes and regulations Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

The timely restoration of electricity services following extreme weather events is crucial to meet customer energy resilience as well as for the economic and national security of the United States. Electricity restoration plans are needed to monitor multi-state power restoration operations, undertake resource planning, and analyze system vulnerabilities. However, these plans are proprietary to utility companies and not readily available to first responders and decision-makers. The purpose of the Restoration of Power Outage from Wide-area Severe Weather Disruptions (RePOWERD) project was to (i) determine which type of model – empirical, statistical, or probabilistic-most accurately predicts restoration times for distribution-level power outages caused by Category 2 or higher hurricanes, and (ii) identify the impact on restoration times of various predictor variables, such as power outage impact (i.e., customers impacted), storm characteristics, land-use patterns, and baseline customer density at county-service-area resolution. Seven models were developed for hurricanes that made landfalls from 2017 - 2022 along the Southeast region of the United States (Irma, Michael, Harvey, Laura, and Zeta). Comparing methods for predicting the time to restore power to 95 % of impacted customers for these hurricanes revealed that: 1) outage magnitude (i.e., initial number of customers experiencing outages and their spatial distributions) is the strongest predictor of recovery time; 2) the performance of the log-linear regression model was similar to more complex, less interpretable models (e.g., accelerated failure time); and 3) the final log-linear regression model achieved strong overall performance, but it struggled with certain hurricanes (overall adjusted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.6730, with a minimum of 0.4006 for Harvey and maximum of 0.8636 for Zeta). Using the log-linear regression model to forecast restoration time is viable, as all input data are publicly available prior to or at storm onset; however, the model reliability would benefit from expanding the scope of predictors and training data.

The growing frequency of power outages has prompted increased interest in developing a more resilient power grid that can quickly recover from weather-related damage. At the distribution level, power restoration is a complex, multi-stage process involving multiple response entities. Providing utility stakeholders, government regulators, and the public with information about outage duration and estimated time to restoration is crucial. The research employs a multi-agent simulation approach, which allows for the simulation of decision-making behaviors among different entities and the incorporation of various uncertainties. Specifically, the study uses the open-source simulation package Mesa-Geo in conjunction with the Python language and constructs a road network using the open-source network extension pgRouting for routing queries. The research design includes several experiments focused on Florida as a case study, comparing repair crew sizes, power outage numbers, and road damage scenarios. The findings could offer valuable managerial guidance on resource allocation in the restoration process.

Owners and renters all over the world are being encouraged to stay home as much as possible to mitigate the spread of the SARS CoV-2 virus and the COVID-19 disease. It is prudent to take reasonable precautions at home to decrease risks during the pandemic because most COVID-19 transmission happens in the home (see “learn more”). This is particularly true for households with infected or sensitive members. Furthermore, because people infected with COVID-19 can be asymptomatic you may have an infected person in your home for several days before you know it.

Moving to the ANSI/ASHRAE/IES Standard 90.1-2010 version from the Base Code (90.1-2007) is cost-effective for all building types and climate zones in the State of New Jersey.

Moving to the ANSI/ASHRAE/IES Standard 90.1-2010 version from the Base Code (90.1-2007) is cost-effective for all building types and climate zones in the State of Iowa.

Moving to the ANSI/ASHRAE/IES Standard 90.1-2010 version from the Base Code (90.1-2007) is cost-effective for all building types and climate zones in the State of Alabama.