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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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In order to protect the Solar Dynamics Observatory's instruments from blowing their fuses and being rendered unusable, Goddard Space Flight Center worked with Micropac Industries Inc., based in Garland, Texas, to develop solid-state power controllers, which can depower and then resupply power to an instrument in the event of an electric surge. The company is now selling the technology for use in industrial plants.
Electricity systems are designed to meet peak demand - the maximum load during a specified period, typically in summer - even if that demand occurs only a few hours in a year. Yet most evaluations of electricity efficiency programs focus on reductions in annual energy use. However, these efficiency programs are also delivering peak demand savings at an affordable cost. A new study by the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) explores the program administrator (PA) cost - or the cost to implement an energy efficiency program to a utility or third party administrator - of saving peak demand through efficiency programs for electric utility customers. Berkeley Lab collected data on costs, annual energy savings, and peak demand savings for electricity efficiency programs for 52 utilities and other program administrators in 15 states between 2014 and 2018. The analysis focused on eight program types that represent 68% of the peak demand savings for the utilities and program administrators studied. The findings improve our understanding of which energy efficiency programs produce the most peak demand savings and their cost performance.
Solar energy systems were investigated which complement nuclear and coal technologies as a means of reducing the U.S. dependence on imported petroleum. Solar Thermal Energy Systems (STES) represents an important category of solar energy technologies. STES can be utilized in a broad range of applications servicing a variety of economic sectors, and they can be deployed in both near-term and long-term markets. The net present value of the energy cost savings attributable to electric utility and IPH applications of STES were estimated for a variety of future energy cost scenarios and levels of R&D success. This analysis indicated that the expected net benefits of developing an STES option are significantly greater than the expected costs of completing the required R&D. In addition, transportable fuels and chemical feedstocks represent a substantial future potential market for STES. Due to the basic nature of this R&D activity, however, it is currently impossible to estimate the value of STES in these markets. Despite this fact, private investment in STES R&D is not anticipated due to the high level of uncertainty characterizing the expected payoffs.
Solar energy systems were investigated which complement nuclear and coal technologies as a means of reducing the U.S. dependence on imported petroleum. Solar Thermal Energy Systems (STES) represents an important category of solar energy technologies. STES can be utilized in a broad range of applications servicing a variety of economic sectors, and they can be deployed in both near-term and long-term markets. The net present value of the energy cost savings attributable to electric utility and IPH applications of STES were estimated for a variety of future energy cost scenarios and levels of R&D success. This analysis indicated that the expected net benefits of developing an STES option are significantly greater than the expected costs of completing the required R&D. In addition, transportable fuels and chemical feedstocks represent a substantial future potential market for STES. Due to the basic nature of this R&D activity, however, it is currently impossible to estimate the value of STES in these markets. Despite this fact, private investment in STES R&D is not anticipated due to the high level of uncertainty characterizing the expected payoffs. Previously announced in STAR as N83-10547
In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing clean hybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation.
Miscellaneous Electric Loads (MELs) account for roughly one quarter of building electricity use in most developed countries. A product-specific approach to lowering MELs electricity use in this category takes too long and costs too much because there are so many MELs, each providing unique services. An alternative approach focusing on key functionalities was therefore explored. These functionalities include: (1) power management, (2) power scaling, and (3) power conversion. Cross-cutting efficiency improvements to these functionalities can be incorporated into broad categories of MELs, thus saving electricity and lowering costs. Even though the population of MELs is diverse and rapidly evolving, major technical opportunities exist to improve their efficiency in these functionalities. Research into energy-saving solutions within the cross-cutting technologies will probably have larger savings than focusing on single products.
The Match-E-Be-Nash-She-Wish Band of Pottawatomi Indians, d.b.a. Gun Lake Tribe (Gun Lake Tribe/Tribe), installed a grid connected 68.87 kW solar photovoltaic array at the Tribe’s Jijak Property located in Hopkins, MI. The array generated 95,070 kWh its first year of operation, 7,014 kWh more than projected. The Jijak Gises solar array saved $\$$12,214 during the first year of production including $\$$6,022 in generation credit and $\$$6,192 in offset electrical cost. The array will save the Tribe more than $\$$312,000 with increasing electric rates during the normal life of the system reducing Gun Lake Tribe’s reliance on other energy resources. Competitive pricing allowed the Tribe to install a second smaller array to power the Jijak Garden. The 4.85kW Jijak Garden Array produced 6,643.96 kWh during its first year of production, 475.95 kwh more than projected saving $\$$1,126 during year one of production including invoice credits and offset energy consumption. Over the first 20 years of the array, it is expected to produce 132,879 kWh of electrical energy, saving over $26,000 over the life of the system.
This study investigated the energy and cost savings potential by modeling a dual fuel heat pump system with its control for a residential building in (5A) cold climate in U.S. The simulation analysis showed that the application of the DFHP system and its control to the target building can save the heating energy by 42% over a gas furnace system (baseline system). Although the HP-only system can save more heating energy (i.e., 53%) over the baseline system than DFHP system does, the DFHP with its control demonstrated the higher cost savings when a time of use electricity rate was considered.
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Distributed energy resources are promising solutions for household energy affordability and resilience as weather extremes and aging infrastructure intensify grid reliability risks. This study presents a comprehensive nationwide assessment of over 500,000 U.S. households, evaluating economic and backup viability of solar-battery systems. We find that 60% of households could reduce electricity costs with average savings of 15%, while 63% of households could achieve affordable backup power during power outages covering an average of 51% of their essential energy needs. However, these benefits show limited alignment with areas of greatest need, particularly in regions facing high outage risks. We also identify significant disparities in access to solar and battery, with less-populated and disadvantaged communities showing consistently lower viability. Furthermore, these findings demonstrate the need for targeted policy interventions to ensure equitable access to solar-battery benefits, especially as states transition from net energy metering to other electricity tariff policies.
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.
As buildings account for over 75% of U.S. electricity use, effectively managing their loads can greatly facilitate the transition towards a clean, reliable grid. Grid-interactive efficient buildings (GEBs) combine efficiency and demand flexibility with smart technologies and communication to provide occupant comfort and productivity while serving the grid as a distributed energy resource (DER). In turn, GEBs can play a key role in ensuring access to an affordable, reliable, sustainable, and modern U.S. electric power system. Their national adoption could provide $\$$100-200 billion in U.S. electric power system cost savings over the next two decades. The associated reduction in CO 2 emissions is estimated at 6% per year by 2030 (DOE 2021). Building codes represent standard design practice in the construction industry and continually evolve to include advanced technologies and innovative practices. Historically, national model energy codes establish minimum efficiency requirements for new construction (ICC 2020). Expanding codes to support GEB capabilities is a pivotal step towards realizing demand flexibility in support of a clean grid by addressing capabilities to improve interoperability between smart building systems, the grid, and renewable energy resources. Realizing GEBs requires buildings with automated demand response (DR) capabilities that enable standardized communication with or control of, subject to explicit consumer consent, energy smart appliances or home energy management systems. This is achieved through direct or indirect (i.e., via an aggregator) communication between appliances and the electric grid. Energy codes can also support DR communication standardization and advance the deployment of building-integrated DERs such as energy storage, generation, and electric vehicles (EVs). Incorporating automated DR capabilities in energy codes provides many benefits to the consumers. Specifically, it aligns building electric load demand with intermittent renewable energy source availability, decreases peak load on the electric grid, allows buildings to respond to utility price signals, supports electrical network reliability and market growth of products and processes aligned with clean economic growth. The incorporation of DR into the model residential energy codes was considered for both the 2021 and 2024 International Energy Conservation Code (IECC) code development cycles. The approved DR measures in the 2021 cycle were removed in response to appeals (ICC 2020). Updated language was presented for consideration again for the 2024 IECC, where it was negotiated and again approved, and again removed in response to appeals (ICC 2024). This resulted in many sections, including sections on demand responsive controls, being moved to the credits options or an appendix as a voluntary application. This technical brief updates the proposed DR components such that they can be considered by states and local governments for direct incorporation into their codes, as well as for future IECC energy code development. The proposal refinements are intended to support consistency in approach and provide a degree of certainty for building owners, designers, contractors, manufacturers, and building and fire safety professionals. The scope of this technical brief includes three strategies for DR in residential buildings: 1) smart thermostats with demand-responsive control, 2) electric water heating incorporating demand-responsive controls and communication and 3) grid Integrated solar and energy storage systems.
A performance and operational economics analysis is presented for an integrated-gasifier, combined-cycle (IGCC) system to meet the steam and baseload electrical requirements. The effect of time variations in steam and electrial requirements is included. The amount and timing of electricity purchases from sales to the electric utility are determined. The resulting expenses for purchased electricity and revenues from electricity sales are estimated by using an assumed utility rate structure model. Cogeneration results for a range of potential IGCC cogeneration system sizes are compared with the fuel consumption and costs of natural gas and electricity to meet requirements without cogeneration. The results indicate that an IGCC cogeneration system could save about 10 percent of the total fuel energy presently required to supply steam and electrical requirements without cogeneration. Also for the assumed future fuel and electricity prices, an annual operating cost savings of 21 percent to 26 percent could be achieved with such a cogeneration system. An analysis of the effects of electricity price, fuel price, and system availability indicates that the IGCC cogeneration system has a good potential for economical operation over a wide range in these assumptions.
Model Predictive Control (MPC) applied to buildings is starting to see some commercial adoption by companies. However, it is hard to estimate if relative energy cost savings are enough to justify the cost of MPC implementation with few reported demonstrations. In small commercial and residential buildings, a one size-fits-all solution can help reduce implementation costs, while in very large buildings or districts the potential energy cost savings magnitude can cover more tailored solutions. This estimation becomes harder for medium to large commercial buildings, where a one-size-fits-all solution cannot be adopted and potential energy cost savings might not be sufficient to cover a tailored solution. Therefore, value propositions in addition to energy efficiency alone can make MPC technology more attractive through additional energy cost savings. One such value proposition is load shifting in response to dynamic electricity prices. On this aspect, MPC is a key technology to unlock building thermal mass for energy flexibility in response to electric grid conditions. This study shows the experimental results of MPC control of an office building in Berkeley, where different dynamic electricity price profiles were used in the MPC objective function to shift the building load and to calculate hypothetical electricity costs. Results show potential 50% cost savings with respect to the existing controller with the dynamic price scenario.
The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. Here, this study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.
This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.
Clothes drying is among the most power-intensive household end uses, and vapor compression-based clothes dryers offer substantial energy cost savings compared with conventional electric heater dryers. However, parasitic heat losses along the air path, that is, heat loss at the upstream of the drum, inside the drum, and at downstream of the drum, remain rarely quantified, but can strongly affect overall performance according to experiments. Here, this study enhances a vapor compression simulation model by integrating a validated drum model using a dimensionless heat-and-mass-transfer effectiveness correlation derived from 63 drying tests in the literature to form a quasi-steady dryer simulation model. The model is validated against laboratory measurements of a baseline dryer. The impact of heat losses is simulated at three locations and explored through 16 heat loss cases spanning two loads (2 and 5 kg), two airflow rates, and multiple loss distributions. For a 5 kg wet load, eliminating pre-drum and in-drum losses reduces total energy use by up to 34–35% and shortens drying time by 27–38% compared with the baseline. At a 2 kg wet load, introducing post-drum heat loss lowers energy costs by up to 23% and reduces operation time by up to 57% compared with the baseline. The results demonstrate that the impact of heat loss is location dependent. Vapor compression dryers should minimize pre-drum and in-drum losses while potentially exploiting post-drum heat dissipation to enhance the drying rate and energy cost savings.
Abstract Previous research has recognized that the value of measures that reduce electricity usage depends upon the timing of the savings generated, but the lack of hourly savings shapes has limited the demonstration of this concept. We develop empirical hourly savings shapes for residential space-conditioning measures from nearly 18,000 efficiency projects in California and show how they combine with the diurnal and seasonal variation in electricity system costs. We find that these measures (cooling replacements; windows, doors, and skylights; and other envelope measures) tend to save electricity when system costs are highest and that the hourly savings account for 1.4–1.5 times as much value as non-time-sensitive estimates of efficiency would predict. We present these impact multipliers for each measure to quantify the additional value revealed by the time-sensitive approach. We show that this additional value is similar in an evolving electricity grid with storage, rather than natural gas generation, as the marginal resource.