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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Using numerical methods to screen magnetocaloric materials in an active magnetic regenerative cycle

A 2-dimensional numerical model of a room temperature operating Active Magnetic Regenerator (AMR) that comprises of a regenerator, hot and cold heat exchangers, heat transfer fluid is developed. The regenerator is made of a magnetocaloric material (MCM) which heats up upon applying a magnetic field, H, and cools down when the field is removed; thus, making it the most essential part of an AMR. The model takes experimentally measured ΔT ad (H,T) and the C p (H,T) data as input and provides quantitative performance metrics of the magnetic cooling system, such as ΔT span and the cooling load, as output. With this model, it is possible to assess a wide range of MCMs in an AMR. 4 different MCMs were investigated using this model in terms of their ΔT span and the cooling loads—LaFe 10.96 Co 0.97 Si 1.07 , MnFeP (1-x) As (x) and AlFe 2 B 2 and Gd. During the screening of MCMs, all the important operating conditions of the device were fixed such as the fluid flow rate, ambient temperature, cycle duration, magnetic field strength. Our results indicate that Gd exhibits the maximum ΔT span with respectable cooling load, while AlFe 2 B 2 generates the lowest ΔT span . Even though LaFeCoSi compound did not perform as well as Gd, it could be the MCM choice for the realization of magnetic refrigeration on a global scale due to its much lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2020

Analysis of Space-Conditioning Loads in Commercial Buildings

Space conditioning end-uses, which include heating, cooling, and ventilation, represent a significant fraction of commercial building energy use, with a wide variety of heating and cooling technology options available in the market. In the interest of improving the overall efficiency of heating, ventilation and air-conditioning (HVAC) technologies, governments, utilities and private sector entities have implemented a variety of market transformation policies that aim to influence consumer purchase decisions. To evaluate the costs and benefits of such programs, analysts typically postulate a hypothetical default equipment choice, and compare it to one that provides comparable service with lower energy and/or power use. The corresponding reduced operating cost provides a benefit that offsets the potential higher cost of improved efficiency. Typically, life-cycle cost or cash-flow analyses are used to quantify the net economic benefit. These analyses require the capability to assess how a given equipment design would perform across a broad range of characteristics, both of the building and of the local weather. While these assessments can be performed using customized building simulations, it is generally not practical to develop and validate detailed building simulation code to cover all the potential variations of equipment design and installation. An alternative, and somewhat simpler, approach is to solely use detailed building simulations to generate time series of heating and cooling loads in commercial buildings. These loads can then be used as input to more detailed, stand-alone engineering models that simulate HVAC system performance under different equipment designs. This approach was used to evaluate a range of high-efficiency commercial packaged air conditioner design options for the Department of Energy’s Appliance and Equipment Standards Program (DOE-EERE 2015). While there may be some loss of precision relative to full simulation, the accuracy of this approach is sufficient for practical applications of cost-benefit analysis. This report describes the development of a database of commercial building heating and cooling loads, generated using the EnergyPlus software package, a whole building energy use model supported by the Department of Energy (DOE-EERE 2020a). EnergyPlus takes as input a set of configuration files that describe the building itself (size, zoning, envelope characteristics, etc.) and the various systems within it (HVAC, lighting, water heating, etc.). This analysis uses a publicly available collection of commercial reference buildings (CRB), comprised of sixteen building types and three vintages (DOE-EERE 2020b; Deru et al 2011). Each building is simulated in eighteen different locations, covering a wide range of climatic conditions. The prototype building description files assign the type of HVAC equipment used, and capacities across climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Experiments on a vapor compression air conditioner with liquid desiccants for efficient dehumidification

Buildings require air conditioning systems that not only cool and dehumidify supply air but also provide sufficient ventilation to ensure indoor air quality and occupant comfort. However, standard recirculation systems-which introduce about a 10 % to 20 % fraction of outdoor air-often fail to deliver air that is precisely cooled and dry, particularly because 80-90 % of the ventilation cooling load is latent. Mixing humid ventilation air with recirculated indoor air increases the energy and costs required to condition the air to comfortable levels. Dedicated outdoor air systems (DOASs) are designed to handle this latent dominated ventilation load and thus need to have efficient humidity removal. Many cooling cycles can perform this task. Here we describe a liquid desiccant DOAS, which combines a vapor compression cycle and a liquid desiccant absorber and desorber pair. We present its performance at 26 operating conditions and a thermodynamic model which can accurately predict the moisture removal efficiency. The model's performance predictions have a mean percentage error of 2.5 % and a coefficient of variation of the root mean square error of 7.5 %. We also compare the performance of this vapor-compression-coupled liquid desiccant system with a standard vapor compression system with the same components but no liquid desiccant. For the 26 conditions tested in this study, this comparison shows that adding liquid desiccants lowers the required evaporator cooling load by 21 %, allows for 25 % lower compressor volumetric capacity, and 25 % lower electricity use. Future work will leverage this model to quantify the reduction in annual electricity use across different climates, including the need for a standard vapor compression system to reheat the air during some of the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Overall chilled water system energy consumption modeling and optimization

The emergence of increasingly affordable variable-speed drive technology has changed the approach used to control chilled water systems equipped with these drives. The purpose of this research was to develop an integrated chilled water modeling technique that can determine the optimal system setpoints and estimate the energy saving potential of chiller system. The chiller system equipped with Variable Frequency Drives (VFDs) on cooling tower fans and condenser water pumps. To accomplish the objective, physical component models of the centrifugal chiller, cooling tower and condenser water pump were established with the goal of incorporating the system’s condenser water flow rate and cooling tower fan speeds as optimization variables. Furthermore, a cooling load prediction algorithm was developed using a multiple non-linear regression model to approximate the building’s cooling load subject to a range of environmental conditions. The inputs and outputs of the individual component models were linked to estimate how adjusting the cooling tower fan and condenser water pump speed would influence the system’s comprehensive performance. Here, the overall system model was then optimized using a generalized reduced gradient optimization algorithm to determine the potential energy savings through speed control with VFDs and to ascertain a control logic strategy for the building automation system to operate the heating and cooling system. A case-study was performed on a single chiller system at a museum and the model was calibrated according to logged data collected over four months. Results showed that for the system analyzed, the energy saving of optimizing the cooling tower fan system was found to be 12–15%, while the energy saving potential of optimizing the condenser water pump with the cooling tower fan was negligible. Additionally, comparing different cooling tower fan control strategies showed that a wet-bulb approach-based cooling tower control strategy was shown to have the highest correlation to the optimized fan speed with an R 2 of 0.924.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Variable Refrigerant Flow With 25% Upsizing Allowance for Heating

This documentation focuses on a single end-use savings shape upgrade - a variable refrigerant flow with heat recovery (VRF HR) heating and cooling system coupled with a dedicated outdoor air system (DOAS) for ventilation and where 25% upsizing (or 125% of the original size) is allowed for heating in colder climates (i.e., heating dominant regions). This document will primarily discuss the additional changes to the sizing algorithm and modeling approach, while a comprehensive overview of the fundamental modeling methodology and background of the VRF HR DOAS upgrade, including applicability and other key assumptions can be found in the original documentation: Variable Refrigerant Flow with Heat Recovery and Dedicated Outdoor Air System. To provide more context on the 25% upsizing algorithm, if the building is a cooling dominant (e.g., design cooling load higher than design heating load ), then the outdoor unit capacity of the VRF heat pump is sized based on the design cooling load. However, if the building is heating dominant, then this building is a candidate for the 25% upsizing allowance. Once the 25% upsizing is allowed, if the 25% upsized capacity (or 125% from the original size) represented with design condition exceeds the design heating load, then the design heating load is used for calculating the rated capacity of the outdoor unit. And if the 25% upsized capacity represented with the design condition does not exceed the design heating load, then the 25% upsized capacity represented with rated condition is used for the capacity of the outdoor unit while the remaining heating load is handled with the supplemental/backup electric resistance coil.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Advanced absorber heat integration via heat exchange packings

A rate-based model of an absorption column was developed and used to analyze several intercooling strategies utilizing “heat exchange packings.” These packings are capable of removing heat from the column and transferring it to a cooling fluid within the packing. For absorption of CO 2 into aqueous monoethanolamine under industrial conditions, intercooling via heat exchange packings placed along 10–20% of the column could reduce the column height by ~15%. The height of these columns was close to the minimum theoretical value, calculated by numerically optimizing the temperature profile. Effective intercooling could also be achieved by using the cool, rich solvent as the cooling fluid. This reduces the cooling load and facilitates recovery of waste heat. Heat exchange packings could also be used to redistribute heat within the column, reducing the column height with no net cooling load. However, this approach requires larger heat transfer coefficients than have been experimentally observed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

The power reliability event simulator tool (PRESTO): A novel approach to distribution system reliability analysis and applications

The growing interest in onsite solar photovoltaic and energy storage systems is partially motivated by customer concerns regarding grid reliability. However, accurately assessing the effectiveness of PVESS in mitigating these interruptions requires a comprehensive understanding of location-specific outage patterns and the ability to simulate realistic scenarios. To address the gap, we introduce the Power Reliability Event Simulation TOol (PRESTO), the first publicly available tool that simulates location-specific power interruptions at the county level. PRESTO allows for a more realistic assessment of system reliability by considering the unpredictability and location-specific patterns of power interruptions. We applied PRESTO in a case study of a single-family home across three U.S. counties, examining the performance of a solar photovoltaic system with 10kWh of battery storage during short-duration power interruptions. Our findings show that this system reliably met 93% of energy demand for essential non-heating and cooling loads, fully serving these loads in 84% of events, despite the constraints of daily time-of-use bill management which limits the battery's state-of-charge reserve. However, when heating and cooling loads were included, system performance decreased significantly, with only 70% of demand met and full service in 43% of events. These results highlight the challenges of using solar photovoltaic and energy storage systems for short-duration outages, emphasizing the need to consider factors like battery size and grid charging strategies to improve reliability. Our study demonstrates the practical applications of PRESTO, providing valuable insights into potential mitigation strategies including grid charging and optimizing battery size.

14 SOLAR ENERGY↗
DOE OSTI2024

An Efficient Annual-Performance Model of a Geothermal Network for Improved System Design, Operation, and Control: Preprint

Geothermal district energy systems (DES), and specifically geothermal networks, provide a viable solution for decarbonizing residential and commercial heating and cooling loads. District energy systems of all kinds enable a thermal resource with a relatively high capital cost (such as a geothermal borehole field) to be shared among a large number of users. While district heating and cooling has been studied for many years, geothermal networks, fifth generation DES that utilize water-source heat pumps and an ambient temperature loop to meet heating and cooling loads, have not been implemented extensively and thus require additional technical and economic optimization to obtain maximum benefits. This paper presents a newly developed reduced-order model that captures the flow of energy within the network, including the commercial and residential users' electrical usage, at an hourly rate over a year. The model includes building loads, heat pumps, borehole fields, and auxiliary heat/cool input, all connected with an ambient-temperature thermal loop model. In the model, operational control is possible for the borehole fields, circulation pump, and auxiliary system. For a given system, the model can output the complete state parameters for each component, the thermal loop, and the collective system, such as flow rate over time, average thermal loop temperature over time, and total electricity usage. The model can also be used to optimize the system control for maximizing system efficiency or minimizing system operational cost. For example, one initial assessment of the borehole controller for an example system showed that a controller with on/off operation of the borehole field reduces annual electrical usage by 33%, compared with continuous operation mode. Hence, the model can assist in optimizing a given system's operation to get the most value out of a geothermal network installation. Future work will consider the model's application to a demonstration project, including the model validation against operational data and system operation optimization.

ambient-temperature loop↗
DOE OSTI2024

Investigation of precooling unit design options in hydrogen refueling station for heavy-duty fuel-cell electric vehicles

Precooling gaseous hydrogen fuel to a cold temperature before refueling a heavy-duty (HD) hydrogen fuel cell electric vehicle (HFCEV) is essential to avoid overheating the vehicle tank, as well as achieving a high state of charge (SOC). Because a large volume of hydrogen is dispensed during each fill, the need for a shorter fill time amplifies the need to precool each load for refueling a HFCEV. Thus, the design and operation of a precooling unit (PCU), as well as the associated capital and operating costs, plays a pivotal role in any plans for heavy-duty hydrogen refueling stations. Here, in this paper, we present a thermodynamic and technoeconomic analysis of a PCU in a gaseous hydrogen refueling station (HRS) for HD HFCEVs. By employing Argonne National Laboratory's hydrogen station cost optimization and performance evaluation (H2SCOPE) model, the refueling of 50 kg of hydrogen on-board type IV tank at ambient temperatures of 15–45 °C and varying fill rates is simulated. The required degree of precooling temperature to obtain either 100% or the maximum possible SOC is obtained from the simulation. Additionally, the simulation results demonstrate that the average flow rate of hydrogen is approximately 40% lower than the maximum flow rate during a typical fill; which motivates further evaluation of the instantaneous hydrogen mass flow rate profile and suggests the scope of improving precooling unit design. Accordingly, a hybrid strategy of precooling hydrogen has been proposed to address the cooling load by sizing the refrigeration unit for the average flow rate of hydrogen, while supplementing the above average peak hydrogen flow cooling load through thermal buffering. The combined technique enables the downsizing of the original PCU capacity by 25–40% and demonstrates a potential cost reduction of the PCU by approximately 30%, which translates to an installed cost reduction of ∼$125,000 per dispenser.

25 ENERGY STORAGE↗
DOE OSTI2023

ResStock: Annual Baseline Results with Component Loads

The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities. The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel. A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.

Array↗
DOE OSTI2020

Numerical and experimental analysis of precooled desiccant system

This paper presents a numerical and experimental study of four novel, desiccant air-conditioning systems based on pre-cooling of the ambient air. The purpose of the study is to: 1) establish which configuration is most profitable for the future studies through numerical and experimental methods; 2) verify the concept of precooled system and confirm that it is working in experimental conditions (for this purpose two prototypes of evaporative cooling units were developed and compared for different operation arrangements with desiccant wheel); 3) Analyze practical aspects of the systems, which includes water consumption analysis, economic analysis and establishing operational modes during a cooling season in temperate climate conditions (the proposed solution has a significant advantage during operation in a temperate climate, since it can utilize its first evaporative cooler the only source of cooling energy when the humidity of outdoor air allows to to meet the latent cooling load). Four arrangements of the system with different dew point evaporative air cooler prototypes were selected and developed for comparative analysis. The system was simulated as a whole, also, each component was individually tested on measurement station. develop the optimal arrangement of the system through numerical and experimental analysis. Four different performance indicators were considered: thermal COP (Coefficient Of Performance), the ERR (Energy Efficiency Ratio), coefficient a describing the potential in compensating indoor cooling loads, water consumption ratio. Also costs of the systems were compared through economic analysis. It was established that arrangement which is characterized by best-averaged performance in terms of all considered factors is a unit combined with two different types of evaporative coolers (marked as System B)- this is caused by the favorable arrangement of two units, where one is better suited to the pre-cooler role, while the other is better suited as the post-cooler.

42 ENGINEERING↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Thermostat Control for Load Shedding in Large Offices

This documentation focuses on a single End-Use Savings Shape measure - thermostat control for load shedding. The thermostat control for load shedding measure applies heating and cooling temperature setpoint offsets for reducing the heating and cooling load during peak window. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the peak window, and then adjusts the thermostat cooling and/or heating setpoints by an offset value from original schedules during the peak window to reduce the HVAC or whole building daily peak load. The measure is flexible and allows users to adjust the heating and cooling offset values respectively, but for this study, the adjustment for heating and cooling setpoints are set to -2 degrees Celsius and +2 degrees Celsius by default. The measure provides options of adding rebound control period (default 2 hours) after peak windows for the setpoints to be ramped back to default values, to prevent the system from generating higher peak demand with step changes of setpoints in post-peak periods . This measure is applicable t o large offices equipped with electric HVAC system (either electric cooling only or both electric heating and cooling), which account for approximately 8.72% of the ComStock floor area. The thermostat control for load shedding measure demonstrates 2-5% daily peak demand reduction and 0.068% total site energy savings (3 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Simulations of Grid-Responsive HVAC Cooling Measures via Ice/PCM storage

This study uses EnergyPlus building energy simulations to assess grid-responsive HVAC cooling measures during peak hours having high electricity price, including: lock the high capacity of a two-speed cooling coil during grid-response hours; and use ice/PCM energy storage to drive a chilled water coil and meet the cooling load, when shut off the main electric cooling coil. Here, the impacts on peak power reductions, energy and utility cost savings, and comfort levels in residential buildings of two U.S. cities were revealed.

Shen, Bo↗
DOE OSTI2024

Community Geothermal: Energy, Cost, and Carbon Modeling for District Design - Ann Arbor, MI

This data includes results on an analysis of existing and projected energy, cost, and carbon for the City of Ann Arbor - District Geothermal Design and Deployment to Equitably Decarbonize Low Income Neighborhoods in Ann Arbor project. The scope of the project includes designing and implementing a geothermal district heating and cooling system that reduces thermal heating and cooling load by 75% and greenhouse gas emissions by 40% in the project area (262 households, 6 commercial buildings). The existing neighborhood was modeled using Design Builder, an EnergyPlus software, to understand the current energy load. The energy model was then flipped to reflect the designed district geothermal heating and cooling system to project the effect on energy, carbon, and cost. This dataset includes the analysis files utilized and created for this study. There are 3 categories of data: 1) existing/benchmarking, 2) energy modeling, and 3) post processed calculations. This follows the methodology and process of the project team, which is fully explained in file 00_Technical Economic Environmental Assessment. All uses of data are referenced throughout this assessment to their respective files included below.

15 GEOTHERMAL ENERGY↗