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DOE OSTI2023

Techno-Economic Assessment of Residential Heat Pump Integrated with Thermal Energy Storage

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.

25 ENERGY STORAGE↗

DOE OSTI2020

A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting

Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

42 ENGINEERING↗

DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis: Preprint

The high energy consumption from residential buildings provides them large potential to participate in demand response programs. To design appropriate demand response programs for residential buildings, it is important for electric utilities to know the energy consumption characteristics for different types of households so that utilities can send requests to the groups with a higher possibility to successfully respond. In this paper, we develop a load model to generate synthetic load profiles for different types of households incorporating demographical information including Current Population Survey data set and American Time Use Survey data set. The details of each data set and the details of the load models are presented. The synthetic household load profiles are generated by the load model and clustered into different groups based on state, age, number of occupants, income level, and city of the household. The average energy consumption characteristics for different groups of households are analyzed and compared, which will help electric utilities issue demand response signals to appropriate households.

building loads↗

DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis

building loads↗

DOE OSTI2024

Electrification Analysis: Container Ports' Cargo Handling Equipment

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, the Electrification Analysis of Container Ports' Cargo Handling Equipment project. This project created a scalable solution to model energy demand per shipping container moved (kWh/TEU) for an all-electric cargo handling equipment fleet located at a maritime port. The model allows stakeholders to understand energy demand at each electric vehicle (EV) equipment level and is easily scalable to container demand and EV adoption rate projections.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Impact of Geothermal District Heating System on Flexibility of Microgrid in Tuttle, Oklahoma: Preprint

Flexibility is the capability of the power grid to maintain a balance between electricity generation and variable demand. This study presents preliminary results evaluating the impact of geothermal district heating systems on the flexibility of a conceptual microgrid in Tuttle, Oklahoma. Heating demand profiles were modeled using EnergyPlus for the district that includes two schools and 250 single-family houses. Then, geothermal energy production was modeled using GEOPHIRES to estimate how much heating demand in the district can be supplied by five different geothermal system scenarios. The results indicated that geothermal energy production varied depending on the resource temperature at different depths, system configurations, and flow rates. For the grid flexibility analysis, electricity consumptions in the five geothermal systems were estimated for pump operations to circulate water from the wells to radiators, while electricity consumption by air-source heat pump in the base case was estimated to supply the same heating load. Electricity consumption in the geothermal systems was significantly lower than those in base cases. The electricity saved by the geothermal system was then incorporated into the microgrid electrical load profiles where variable renewable electricity generation is significantly high. The results visually showed that geothermal district heating system can improve grid flexibility as a baseload during the winter season. The results also highlighted potential opportunities to save energy costs that will be further analyzed in future study.

Cambium↗

DOE OSTI2025

Impact of Geothermal District Heating System on Flexibility of Microgrid in Tuttle, Oklahoma

Flexibility is the capability of the power grid to maintain a balance between electricity generation and variable demand. This study presents preliminary results evaluating the impact of geothermal district heating systems on the flexibility of microgrid in Tuttle, Oklahoma. Heating demand profiles were modeled using EnergyPlus for the district that includes two schools and 250 single-family houses. Then, geothermal energy production was modeled using GEOPHIRES to estimate how much heating demand in the district can be supplied by five different geothermal system scenarios. The results indicated that geothermal energy production varied depending on the resource temperature at different depths, system configurations, and flow rates. For the grid flexibility analysis, electricity consumptions in the five geothermal systems were estimated for pump operations to circulate water from the wells to radiators, while electricity consumption by air-source heat pump in the base case was estimated to supply the same heating load. Electricity consumption in the geothermal systems was significantly lower than those in base cases. The electricity saved by the geothermal system was then incorporated into the microgrid electrical load profiles where variable renewable electricity generation is significantly high. The results visually showed that geothermal district heating system can improve grid flexibility as a baseload during the winter season. The results also highlighted potential opportunities to save energy costs that will be further analyzed in future study.

Cambium↗

DOE OSTI2024

Material selection and manufacturing for high‐temperature heat exchangers: Review of state‐of‐the‐art development, opportunities, and challenges

Abstract Many energy systems demand heat transfer at high temperatures to keep up with high demand for power, so high‐temperature material that can perform and last under these harsh conditions is needed for heat exchangers. The engineering requirements for these high‐temperature heat exchanger material call for high thermal conductivity, high resistance to fracture, high resistance to creep deformation, environmental stability in environments associated with the application, and high modulus of elasticity while maintaining low cost to make and maintain. Naturally, ceramics are a good solution for this endeavor. In the past, high‐temperature heat exchangers made from ceramics have been used. We provide examples of ceramics in relevant heat exchange applications and provide motivation where additive manufacturing (AM) can improve efficiency. AM for the relevant material is under development, and we provide insight on the AM of ceramic materials and examples of AM heat exchangers keeping cost in mind. The motivation of the review paper is to provide a framework for material and manufacturing selection for high‐temperature heat exchangers for AM to keep up with the demand for better efficiency, better material, better manufacturing, and cost moving forward with AM technology in high‐temperature ceramic heat exchangers.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Reviewing clinical considerations and guideline recommendations of C1 inhibitor prophylaxis for hereditary angioedema

Abstract Background Hereditary angioedema (HAE), a rare disease that is characterized by painful and recurring non‐allergic swelling episodes, is caused by the deficiency or dysfunction of C1 inhibitor (C1INH) protein. A comprehensive HAE management plan may require long‐term prophylaxis (LTP) in addition to on‐demand treatment to help “normalize” patients' lives so that they may fully engage in work, school, family, and leisure activities. Aim The main objective of this narrative review is to provide an overview of updated guideline recommendations specific to LTP of HAE and discuss clinical considerations and pharmacologic management options, with a focus on C1INH. Materials and Methods The authors reviewed relevant HAE literature for current recommendations regarding LTP and the role of C1NH. Results Acute HAE attacks are treated with on‐demand medication; however, there is a consensus that LTP should routinely be considered for risk reduction and prevention of future episodes. The 2017 World Allergy Organization/European Academy of Allergy and Clinical Immunology guidelines recommend that all patients with HAE be evaluated for LTP routinely and the 2020 HAE Association (HAEA) guidelines emphasize that the decision to use LTP should not be based on rigid criteria, but rather should be based on individual patient needs. Both guidelines recommend C1INH as first‐line/preferred therapy for LTP in a range of patient types including adults, children/adolescents, and pregnant/lactating patients. The HAEA also recommends the kallikrein inhibitor, lanadelumab, as a first‐line option for LTP. HAE pathway‐specific agents for LTP have not been associated with notable safety concerns. Discussion Plasma‐derived C1INH has been available for 40+ years in Europe and impacts multiple targets within the HAE pathway. C1INH has been used for on‐demand treatment and LTP. A subcutaneous formulation of plasma‐derived C1INH is approved for LTP and produces functional C1INH activity levels consistently above the threshold needed for protection from HAE attacks. Other pathway‐specific options for LTP include the plasma kallikrein inhibitors, lanadelumab‐flyo and berotralstat, approved for adults and pediatric patients aged ≥12 years. C1INH is approved for adults and pediatric patients aged ≥6 years. Conclusion Assessing the need for LTP is vital in the ongoing dialogue between clinicians and patients, as both disease‐related factors and patient preferences may change over time. Among available options for LTP, plasma‐derived C1INH is the broadly recommended first‐line option for LTP in patients with HAE, including pregnant/lactating women and pediatric patients (≥6 years).

Anderson, John↗

DOE OSTI2021

Long‐term continuous cropping affects ecoenzymatic stoichiometry of microbial nutrient acquisition: a case study from a Chinese Mollisol

Abstract BACKGROUND Soil‐ and plant‐produced extracellular enzymes drive nutrient cycling in soils and are assumed to regulate supply and demand for carbon (C) and nutrients within the soil. Thus, agriculture management decisions that alter the balance of plant and supplemental nutrients should directly alter extracellular enzyme activities (EEAs), and EEA stoichiometry in predictable ways. We used a 12‐year experiment that varyied three major continuous grain crops (wheat, soybean, and maize), each crossed with mineral fertilizer (WCF, SCF and MCF, respectively) or not fertilized (WC, SC and MC, respectively, as controls). In response, we measured the phospholipid fatty acids (PLFAs), EEAs and their stoichiometry to examine the changes to soil microbial nutrient demand under the continuous cropping of crops, which differed with respect to the input of plant litter and fertilizer. RESULTS Fertilizer generally decreased soil microbial biomass and enzyme activity compared to non‐fertilized soil. According to enzyme stoichiometry, microbial nutrient demand was generally C‐ and phosphorus (P)‐limited, but not nitrogen (N)‐limited. However, the degree of microbial resource limitation differed among the three crops. The enzymatic C:N ratio was significantly lower by 13.3% and 26.8%, whereas the enzymatic N:P ratio was significantly higher by 9.9% and 42.4%, in MCF than in WCF and SCF, respectively. The abundances of arbuscular mycorrhizal fungi and aerobic PLFAs were significantly higher in MCF than in WCF and SCF. CONCLUSION These findings are crucial for characterizing enzymatic activities and their stoichiometries that drive microbial metabolism with respect to understanding soil nutrient cycles and environmental conditions and optimizing practices of agricultural management. © 2021 Society of Chemical Industry.

Chen, Xu↗

DOE OSTI2026

Key Drivers for Offshore Wind Deployment in the Western United States

The western coast of the United States has abundant natural resources, including wind, sun, and water. Despite strong ocean winds, offshore wind energy (OSW) in the Western United States is in the early stages of technology development due largely to the deep water off the coast, which requires floating platforms rather than fixed‐bottom turbines. Floating OSW increases the technical difficulty of installation and thus the cost relative to both fixed‐bottom OSW and land‐based turbines. OSW is a potential generation option to help meet increasing demand on the west coast of the United States because it likely has fewer land‐use conflicts than other technologies and complements sources in the existing electricity supply by providing energy during times of high system stress. This paper examines the possible drivers and barriers to OSW deployment on the West Coast using the National Laboratory of the Rockies' state‐of‐the‐art capacity expansion model and the Regional Energy Deployment System (ReEDS) model. We use ReEDS to explore a multitude of future scenarios looking at key drivers for OSW deployment, including variations on the cost of OSW, electricity demand growth, and the availability of competing technologies to examine the factors that may play a role in OSW growth. Assuming coastal state policies such as renewable portfolio and clean energy standards remain in place, we find that OSW can play a role in meeting electricity demand and provide energy during stressful grid conditions and find that deployment from the least‐cost investment model ranges from 7.6 to 38 GW by 2045.

17 WIND ENERGY↗

DOE OSTI2023

Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation

The rapid growth of Cloud Computing (CC) has increased the variety of computing resources, storage, and communication services that pose significant new challenges for the efficient use of cloud resources. The cost-efficient allocation of cloud resources has become a decisive premise for the adoption of CC services. The cost-efficient selection and scheduling of these resources to meet the demands of a scientific workflow is a challenging problem that is exacerbated by the inclusion of multiple CC providers. In this paper, we present a novel strategy for the cost-efficient selection of CC resources using Lagrange relaxation. Our approach is based on preselection of resources and demand decomposition to create a series of smaller sub-problems, which allow the estimation of the best cost-structures and selection of CC service providers for a subset of the time period of the planning horizon. Decomposition of the demand is achieved through the boundary analysis of a continuous relaxation of the problem. Using the metrics defined in terms of the cost and time of completion, we demonstrate excellent performance in relation to optimal solutions. Our method reduced the computational time from hours to seconds for a representative 36-month problem and provided high-quality solutions (< 0.05% relative error). Given the importance of selecting resources and scheduling complex scientific workflows, we believe that this novel strategy will be beneficial for many researchers and users of cloud computing resources.

De La Torre, Luis↗

DOE OSTI2019

Network coding for improving throughput in WDM optical networks with dedicated protection

Network coding (NC) has been recently emerging as an excellent technique for leveraging network efficiency. In optical networks, NC and protection appear to be a good match and indeed, NC could be effectively exploited for greater protection efficiency thanks to the opportunity of combining signals rather than duplication in the traditional approach. Motivated by this observation, we investigate the application of a simple and practical NC technique based on XOR operation to improve the network throughput in survivable optical networks. To this end, we propose the NC-based protection whose speed recovery is on a par with the conventional 1+1 dedicated protection while the capacity usage could be more efficient, paving the way for improving throughput in constrained capacity conditions. A novel mixed integer linear programming model is developed for survivable optical networks design employing the proposed NC-based protection. The model attempts to optimally determine the demands to serve, the routing for served demands and NC assignment among demands aiming at maximizing the network throughput in the constraint of bandwidth limitation. Extensive numerical evaluations on realistic topologies are provided to highlight the advantages of our NC-based protection proposal compared with the conventional dedicated protection in improving the network throughput. Encouraging results are observed when the new protection proposal based on NC could accommodate up to 30% more traffic than the conventional case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions

Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H 2 ) storage likewise holds strong potential, though with distinct challenges and mechanisms. H 2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H 2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H 2 storage systems by optimizing H 2 adsorption at the nanoscale and improving the kinetics of H 2 uptake and release. We discuss various H 2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H 2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H 2 storage, particularly in prototype vehicles, from a techno-economic perspective. In conclusion, this forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand.

25 ENERGY STORAGE↗

DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗

DOE OSTI2021

Plentiful electricity turns wholesale prices negative

In 2020, average wholesale electricity prices in the United States fell to $21/MWh, their lowest level since the beginning of the 21st century. Low natural gas prices and the proliferation of low marginal cost resources like wind and solar had already established a trend toward lower wholesale prices, and this trend was exacerbated by declining electricity demand due to the Covid-19 pandemic in 2020. Negative real-time hourly wholesale prices occurred in about 4% of all hours and wholesale market nodes across the United States, but these were not distributed evenly. Regional clusters emerged, for example, in the Permian Basin in western Texas, and in Kansas and western Oklahoma in the Southwest Power Pool (SPP), negative prices accounted for more than 25% of all hours. Negative electricity prices result either from local congestion of the transmission system leading supply to exceed demand locally or due to system-wide oversupply. Looking at the latter condition in SPP, we find that all major generator types contribute to this excess supply, because of limited ramping flexibility or self-scheduled out-of-market unit commitments. Additional monetary production incentives such as renewable energy credits or tax credits also enable negative bids; indeed, negative prices predominantly occur when demand levels are low and wind production levels are high. Frequent negative prices can inform the value of additional renewable energy investments at specific locations, the need for transmission and storage development, and opportunities load growth or adaptation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Optimal Gas-Electric Energy System Decarbonization Planning

As energy utilities implement climate change mitigation policies, system planners require strategies for achieving affordable emissions reductions. Coordinated planning of electric power and natural gas systems will allow synergistic investments to address cross-sector operational constraints, competing uses for net-zero emissions fuels, and shifts in energy demands across energy carriers. In this study, we develop a novel optimization program that finds the cost-minimizing mix of infrastructure expansion or reduction across gas and electric systems to satisfy sector-specific emissions constraints. Alongside energy supply resources, our framework allows for central-planning of end-use equipment stocks to allow switching between gas and electric appliances upon failure or premature replacement. The proposed model is used to simulate case study scenarios for a benchmark 24-pipe gas network coupled to a 24-node power system test network. We find that electrification of greater than 80% of core gas demands is a component of the least-cost solution for modeled energy systems. Despite this substitution, the gas system is maintained to service difficult-to-electrify customers and to deliver net-zero emissions gas to electricity generators in times of peak electricity demand. Restricting electrification of gas appliances increases reliance on power- to-gas technologies and increases annual costs by 15% in 2040. Neglecting constraints on pipeline blending of hydrogen can produce a misleading result that relies on hydrogen blend fractions of greater than 50%. In all cases, we find the average costs of delivered gas increase nearly 5-fold across the decarbonization transition, highlighting the importance of future work investigating cost-allocation strategies for ensuring an equitable energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Electrifying High-Efficiency Future Communities: Impact on Energy, Emissions, and Grid

To combat climate change and meet decarbonization goals, the building sector is improving energy efficiency and electrifying end uses to reduce carbon emissions from fossil fuels. All-electric buildings are becoming a trend among new constructions, introducing opportunities for decarbonization but also technical challenges and research gaps. For instance, further investigation is needed to understand how the adoption of energy efficiency measures (EEMs) and distributed energy resources (DERs) in all-electric communities would affect energy consumption, carbon emissions, and grid planning. This paper presents a case study of a mixed-use, all-electric community located in Denver, Colorado. We use URBANopt TM , a physics-based urban energy modeling platform to model the community and then evaluate the impact of EEMs and DERs (i.e., photovoltaics [PV], electric vehicles [EVs], and batteries) on the community's energy usage, carbon emissions, and peak demand. The results show that adding EEMs and PV led to both energy consumption and carbon emissions reductions across all building types. However, we saw fairly limited impact of EEMs and PV on buildings' peak demand in our case. Additionally, due to overnight EV charging activities and higher grid carbon intensity at night, the carbon emissions in multifamily buildings have a noticeable increase compared to scenarios without vehicles. Finally, the addition of batteries helped reduce peak demand by 11%-29%. The modeling workflow and evaluation methods can be applied to similar communities to evaluate their performance and the effect of integrating EEMs and DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗