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Case study of a DOE 2021 Housing Innovation Award winning custom home in a cold climate that got a HERS 5 with PV, with 3,934 square feet, ENERGY STAR appliances and windows, LED lighting, and a heat pump water heater.
Future utilities will emphasize home appliances to reduce greenhouse gas emissions while providing electric load shifting and demand profile management. Heat pump water heaters (HPWH) have demonstrated the ability to cut water heating energy by more than 65% compared with conventional electric resistance waters (ERWH). Laboratory research was conducted on the load shifting potential of grid-connected heat pump water heaters (HPWH), compared to ERWHs. Testing applied different CTA-2045 command designs. Highly-controlled laboratory experiments were conducted on one ERWH and four HPWHs, including a prototype incorporating the new CTA-2045-B protocol feature allowing ‘advanced’ load up above the tank setpoint. The prototype unit with the B-protocol increased tank temperature by 15oF (8.3C) under the advanced load up providing an increase of 1.8 kWh of storage for a 50-gallon (189 liter) tank. The prototype includes a built-in mixing valve to meet anti-scalding codes. With the load-shaping ability of CTA-2045-B, utilities might be able to store excess renewable energy in connected tanks when renewable wind and solar resource production is high. Tests were performed under baseline conditions (no load shift) and under several load-shifting schemes, including load up and advanced load up, ahead of shed commands. Grid-connected HPWHs reduced peak demand by as much as 0.5 kW, depending on tank volume, time of day, control scheme, and draw profile.
Full decarbonization in buildings requires the replacement of combustion appliances with electric ones, and air source heat pumps (ASHP) are a candidate alternative. However, technical limitations, such as the efficiency decrease when operating in cold weather, limit their adoption in the global heating market. Among several options to improve ASHP efficiency operating in colder climates, thermal energy storage (TES) has been considered, as it may provide heating when it is cold and shift ASHP operation to times when the weather is warmer. It may also take advantage of time of use electricity rates and support defrosting when necessary. The evaluation of ASHP-TES systems, however, is still limited because traditional metrics do not capture their full economic and environmental benefits. In this work, a python framework is presented to model the ASHP with and without the presence of TES. Metrics are proposed to analyze the system performance in terms of costs, equivalent CO2 emission, and efficiency metrics to evaluate and compare alternative systems. Model validation against experimental data obtained for a commercial heat pump is provided, as well as an application example using Denver, Colorado, to highlight the model capabilities.
Replacing combustion appliances with heat pumps can be part of a strategy to electrify buildings. When their electricity is supplied by renewables, the adoption of heat pumps for space heating may help curb the carbon dioxide emissions inherent to traditional heating systems that burn natural gas or coal. When operating in very cold climates, however, heat pump performance deteriorates, sometimes requiring backup heating supplied by electric resistances or other sources to continue to function. To avoid the use of electric resistances, complementary technologies, like thermal energy storage, solar collectors, or alternative refrigerants, have been explored in recent years. In this study, we present an open-source Python-based numerical model developed to evaluate the annual hourly performance of an air source heat pump (ASHP) operating in the USA. Simulation results include coefficient of performance and heating capacity. The model, validated with experimental results, will be employed in the future to investigate the feasibility of integrating thermal energy storage and other technologies into ASHPs operating in cold climates.
Heat pumps offer a great low carbon emission method to heat homes. Thermalize Juneau 2021 was a clean energy campaign that helped homeowners in Juneau, Alaska install heat pumps into their homes. Juneau is located near the climactic limit of many heat pumps, and we were interested in how well the heat pumps can function in cold climates. This was done by using Sense meters to remotely monitor the energy usage of 10 homes with heat pumps. The Sense meters are small devices that connect to the electrical panel and monitor the energy use of multiple appliances through machine learning. First, we recorded the energy usage of a heat pump in the lab using both the Sense meter and the existing lab datalogger. Both recorded very similar energy usage data which confirmed that the Sense meter can accurately measure the fluctuations in the heat pump energy use. Next, we looked at the energy data from the Sense meters in the homes with heat pumps and paired it with local weather data to see how well the heat pumps functioned in the cold weather.
Recent technological advancement has created a growing convergence of innovation. From machine learning to ubiquitous computing to wireless networks and automation, the world is seeing new technology increasingly capable of connecting with each other. Devices and systems use open communications networks to interact, process information, and react. This is called the Internet of Things (IoT) and is comprised of physical devices that exchange data over networks, creating revolutionary possibilities. The most common way most people interact with an IoT is through ‘smart home’ products like Amazon’s Alexa, which use microphones, speakers, and phones to control a variety of devices, from lights and thermostats, to cameras, to appliances and vacuum cleaners. But the open nature of IoT networks—necessary for their ability to communicate and operate—also introduces privacy and security concerns. At a personal level, this might mean a hack into a home to steal private information, but when applied in broader industries like healthcare, transportation, manufacturing, or the military, this vulnerability can have serious consequences. As IoT usage and interconnectivity increases, so too does the susceptibility to malicious actors. And the entire system is only as secure as its least secure member. This creates particular risk and vulnerability to radiological material industries, as a competent insider adversary could utilize the IoT to potentially steal or access classified or sensitive information about employees, sites, or systems; or simply sabotage security or maintenance from a more remote—and less secure—device. The IoT relies on a secure network across the entire system, especially in transport which may lack the security of more permanent locations; if one device fails, it can create a ripple effect and an insider threat may seek to exploit that connectivity. While IoT benefits drive increased innovation and usage, there are also vulnerabilities an insider threat could exploit; this risk of an IoT to radiological material must be addressed in any mitigation effort.
Informal recycling is a new and expanding low cost recycling practice in managing Waste Electrical and Electronic Equipment (WEEE or e-waste). It occurs in many developing countries, including China, where current gaps in environmental management, high demand for second-hand electronic appliances and the norm of selling e-waste to individual collectors encourage the growth of a strong informal recycling sector. This paper gathers information on informal e-waste management, takes a look at its particular manifestations in China and identifies some of the main difficulties of the current Chinese approach. Informal e-waste recycling is not only associated with serious environmental and health impacts, but also the supply deficiency of formal recyclers and the safety problems of remanufactured electronic products. Experiences already show that simply prohibiting or competing with the informal collectors and informal recyclers is not an effective solution. New formal e-waste recycling systems should take existing informal sectors into account, and more policies need to be made to improve recycling rates, working conditions and the efficiency of involved informal players. A key issue for China's e-waste management is how to set up incentives for informal recyclers so as to reduce improper recycling activities and to divert more e-waste flow into the formal recycling sector.
Effective procurement policies can help governments save considerable amounts of money while also reducing energy consumption. Additionally, private sector companies which purchase large numbers of energy-consuming devices can benefit from procurement policies that minimize life-cycle energy costs. Both public and private procurement programs offer opportunities to generate market-transforming demand for energy efficient appliances and lighting fixtures. In recent years, several governments have implemented policies to procure energy efficient products and services. When deploying these policies, efforts have focused on developing resources for implementation (guidelines, energy efficiency specifications for tenders, life cycle costing tools, training, etc.) rather than defining monitoring systems to track progress against the set objectives. Implementation resources are necessary to make effective policies; however, developing Monitoring and Evaluation (M and E) mechanisms are critical to ensure that the policies are effective. The purpose of this article is to provide policy makers and procurement officials with a preliminary map of existing approaches and key components to monitor Energy Efficient Procurement (EEP) programs in order to contribute to the improvement of their own systems. Case studies are used throughout the paper to illustrate promising approaches to improve the M and E of EEP programs, from the definition of the system or data collection to complementary instruments to improve both the monitoring response and program results.
Solutions for deep decarbonization need to be sustainable as well as affordable to garner widespread adoption. The past two decades have seen the introduction of new technologies and paradigms such as electric vehicles (EVs), solar photovoltaics (PVs), and increased work-from-home (WFH), which impact the overall energy consumption and cost of a household in different ways. However, much attention has not been paid to explore the joint impact these technologies have on a household's energy (electricity cost) burden. Leveraging Residential Energy Consumption Survey (RECS), this study presents an integrated model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. RECS data revealed that, compared to the baseline (i.e., households that did not own any of these technologies), households that own EVs, PVs, and engage in WFH observed a 13.5% decline in their electricity bills, despite a 25% increase in electricity consumption. The model results indicated that bundled adoption of EVs-PVs reduces electricity costs more significantly for a household, compared to the increased costs imposed by more household members staying at home. This holistic assessment presents an opportunity for decision makers to contextualize the changing energy costs and design effective strategies to foster a sustainable and affordable future. Policies such as bundled incentivization to motivate co-adoption, education about energy-management strategies, and add-on subsidies to adopt energy-efficient appliances, will not only accelerate decarbonization but increase household financial savings.
Since its creation in 1945, the French Atomic Energy and Alternative Energies Commission has carried out research programmes on civil use of nuclear, and its application. CEA has set up and run various types of installations from research (accelerators, pond reactors, fast reactors, laboratories) to industry (power reactor, processing facilities). There are currently more than several tens of facilities which are presently closed down pending dismantling or in the process of being dismantled by CEA, the governmental body for research and development in nuclear and renewable energies. When operating, all these installations have generated radioactive waste. When they are shutdown, either as a result of the programmes for which they were set up, or due to equipment wearing out or being obsolete, these installations must be cleaned up, dismantled and decommissioned, taking into account their nuclear nature and requirements related to safety and environment. During the decommissioning process, the implementer has to deal with safety and site remediation issues. Dealing with these installations is a long-term programme which must be carefully planned and financed in order to optimise its management. The paper will describe the CEA's decommissioning policy, strategies adopted, and draw up lessons learned and feedback experience, showing that: - Safety is guaranteed, - Dismantling is a technically controllable operation, but R and D actions are to be developed for specific problems as characterization or operation in hostile environment. Moreover all the methods and techniques improve as a result of feedback experience, - The amount of material and waste produced by the dismantling are manageable. The very low, low and intermediate level waste can be stored on existing shallow land burial centres, some new repositories have to be created for specific waste (graphite, radium,...) and for high and intermediate level long live waste, which are stored in interim storage , - The dismantling must be optimized, especially by appliance of a good strategy based on rational industrial choices. - Communication and transparency with stakeholders is essential for global process acceptance, - The lessons learned and how feedback experience could benefit for future decommissioning projects. (authors)
The dynamics of energy generation, delivery, and consumption in the United States continue to change as new energy sources come online and consumers purchase more energy-intensive devices. The percentage of electricity generation from variable renewable energy sources like wind and solar power has grown year after year. Meanwhile, the evolution of electric vehicles, smart devices, and consumer appliances is changing the way that electricity is used. As a result of these trends, nuclear power plants (NPPs) are being asked to operate more flexibly. Newly constructed, advanced nuclear power plants (A-NPPs) will likely need to adapt their business models with these market shifts in mind. In the future, A-NPPs might operate in an electricity system that values flexibility (e.g. fast ramping) more than stability (e.g. baseload supply). The current fleet of NPPs is struggling to remain economically viable in competitive markets that favor low natural gas prices and renewable energy sources with low marginal costs. Rising construction costs and ambiguous national policies have only added to the challenges facing plant operators. The industry as a whole is in decline, with 19 reactors in the process of being decommissioned and 5 of these plants retiring in the past decade. However, NPPs also provide several performance and environmental benefits by maintaining a high capacity factor and generating almost zero greenhouse gas (GHG) emissions over the life cycle of the plant. As more areas of energy consumption in the United States are electrified, NPPs could potentially replace coal and natural gas as a source of heat and power to help decarbonize the U.S. energy mix. Improving the flexibility of newly constructed A-NPPs to increase revenue through market hedging and reduce costs via stable operation was explored in this analysis. The increasing penetration of variable energy resources could contribute to the uncertainty of electricity demand, which could in turn increase the need for dispatchable electricity generating units. This role is currently filled by inexpensive natural gas combined cycle (NGCC) plants, whereas coal and NPPs have traditionally been called upon to provide baseload power by maintaining a constant power output. As wholesale electricity prices continue to fall due to low natural gas prices and federally subsidized renewable energy, this business model has begun to fail, resulting in incidences of NPPs selling electricity at a loss. Since it has not been economically favorable for NPPs to operate flexibly in this situation in the past, due to their low variable costs compared to fossil fuel power plants, NPPs in the United States were not built with this purpose in mind. Therefore, legacy NPPs have had difficulty adapting to these shifting dynamics due to regulatory and technical constraints, coupled with a lack of meaningful experience. NPPs in France have operated in this manner for years, due to a much higher percentage of baseload generators and a lower percentage of fast-ramping NGCC plants. The integration of energy storage technologies (ESTs) alongside newly constructed A-NPPs will be investigated in this analysis as an alternative method for helping NPPs resolve the economic challenges they currently face. Although the deregulation of energy markets in the United States is responsible for much of the increased competition from natural gas and renewable energy that has negatively impacted NPPs, this process has also led to the creation of ancillary service markets. ESTs can provide many of these services, so an integrated energy storage system could supplement both the reliability of the electricity grid and the economic performance of a NPP by bidding into these markets. However, many technical, financial, and regulatory barriers still prevent ESTs from operating profitably in this space. The purpose of this body of work is to compile a transparent database of metrics for available ESTs and identify barriers to widespread deployment. An analytical selection methodology was also developed to identify favorable technologies for integration with specific NPPs based on information supplied by an interested developer. These tools could help reduce the perceived risk surrounding investment in energy storage systems, which could help both NPPs in need of economic assistance and ESTs in need of further research and development.
Solutions for deep decarbonization need to be affordable as well as sustainable to garner widespread adoption. The past two decades have seen the introduction of new technologies and paradigms such as electric vehicles (EVs), solar photovoltaics (PVs), and increased work-from-home (WFH), which impact a household's overall energy consumption and cost in different ways. However, much attention has not been paid to explore the joint impact these technologies have on a household's electricity cost burden. Leveraging the 2020 Residential Energy Consumption Survey (RECS), this study presents an integrated model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. National level RECS data revealed that, compared to the baseline (i.e., households that did not own any of these technologies), households that own EVs, PVs, and engage in WFH observed a 13.5% decline in their electricity bills, despite a 25% increase in electricity consumption. The model results indicated that bundled adoption of EVs-PVs reduces electricity costs more significantly for a household, compared to the increased costs imposed by more household members staying at home. This holistic assessment presents an opportunity for decision makers to contextualize the changing energy costs and design effective strategies to foster a sustainable and cost saving mechanism. Policies such as bundled incentivization to motivate co-adoption, education about energy-management strategies, and add-on subsidies to adopt energy-efficient appliances, will not only accelerate decarbonization but increase household financial savings.
Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.
Clothes drying is an energy-intensive process that causes significant electricity consumption and carbon emissions in the US. Approximately 83% of Households in the US own a tumble clothes dryer at home and 80% of dryers are electrical resistance dryers with low energy efficiency. Heat pump technology makes it possible for highly efficient and clean drying. Additionally, the ventless design of heat pump clothes dryers (HPCD) provides more installation flexibility. HPCD involves three primary mediums: wet clothes, a closed air loop, and a refrigerant circuit. The evaporator is used to dehumidify the wet air and the condenser is used to re-heat the dry air. One of the critical technological barriers to HPCD market penetration is its long drying time, primarily due to the relatively low discharging temperature and the slow response during the warm-up period. In this study, the thermal energy storage (TES) technology is adopted to address this challenge by providing pre-heating of air prior to the condenser to increase the operating temperature of the process air. The heat pump will charge the phase change material (PCM) in the TES device with heating energy during clothes washing and the PCM will discharge the stored heat to facilitate air heating during clothes drying. To analyze the optimal design and potential for energy saving and drying time reduction, a mathematical model of the HPCD system was developed. The HPCD is a highly dynamic system with a coupled heat and mass transfer and heat pumping cycle. This paper provides solutions to simulate the transient behavior of the system while maintaining low computational cost. The modeling result indicates reduced energy consumption and drying time by integrating TES with HPCD, as compared to electrical resistance dryers. The study can provide significant insights into improving building flexibility with TES and smart appliances.
A refrigerator is an essential appliance for both commercial and residential applications. Thermal (Cold) energy storage can effectively reduce the carbon emission from refrigerators. This preliminary study investigated the demand flexibility of a domestic refrigerator using thermal (cold) energy storage through dynamic modeling and controls. The models include both fresh and frozen compartments, a vapor compression cycle loop, and thermal storage panels. Results show that thermal energy storage can effectively achieve temperature control of the compartments within the 4-hour time window. The load reduction and cost savings (46.3%) were achieved through the TES using TOU rates.
This handout summarizes useful resources and links from the Take Five: Buying Low Carbon Food Services training, a short training video that helps federal procurement stakeholders purchase more energy efficient food service appliances.
Dryers are integral appliances in modern households, yet their significant power consumption remains a critical challenge for reducing energy bills and upgrades. This work presents the outcomes of a comprehensive investigation aimed at lowering the operating energy costs of the dryer through modeling and experimental approaches. Specifically, the objectives of this research are twofold: (1) to reduce energy consumption without negatively impacting drying performance or time; and (2) to ensure affordability by developing retrofittable solutions characterized by low cost and a quick payback period. These strategies promise universal applicability to all dryer categories, encompassing both gas and electric models, by tackling core issues such as unnecessary heat loss and excess energy supply, both prevalent across dryer designs. Our methodology combines robust modeling frameworks and experimental validation to target energy efficiency improvements through three primary pathways: (1) effective heat loss management using ultralow-cost insulation materials tailored for dryer systems; (2) heat management across the drying cycle, facilitated by passive heat transfer mechanisms, and (3) optimization of heat supply to the load to ensure precise energy delivery, minimizing waste. These innovations are designed to seamlessly integrate with existing dryer configurations, providing a scalable and retrofittable solution that ensures affordability without requiring substantial redesigns or expensive components. Using these methods, the study demonstrates improvement opportunities in dryer energy consumption while maintaining the desired performance. The modeling component utilizes computational simulations to evaluate the thermal and energy performance of these innovations under varied operational conditions, providing foundational insights for experimental design. By addressing critical areas such as heat loss, energy recovery, and supply optimization, this work proposes impactful solutions for lowering the energy costs in domestic clothes dryers. The ultralow-cost, retrofittable nature of the proposed strategies ensures widespread adoption potential across diverse dryer categories, making significant strides toward affordable household energy practices.
Oversizing, the common engineering practice of specifying devices with capacity exceeding the actual load requirement, is a widespread practice across virtually all building technologies end-use categories, including HVAC, electrical systems, lighting, appliances, and plug loads. This practice, driven by factors like design uncertainty, institutional pressures, and risk aversion, results in wasted capital investment, control difficulties, and excessive energy consumption due to inefficient part-load operation. Part-load operation, where devices run below maximum capacity, is the dominant operating mode in most energy systems and presents a complex design challenge. Solutions to match output to load fall into three broad categories: constraining the output, adjusting the device’s internal behavior, and linking output to energy storage or other waste-heat reuse applications. The energy implications of part-load are critical, as efficiency often drops sharply as load decreases across a wide diversity of devices. To quantify the extent of this problem, we derive a dimensionless Part-Load Metric (PLM) based on device efficiency and its frequency distribution of operating hours at various output levels. The PLM quantifies the deviation of a device's actual efficiency from its maximum design efficiency. This metric also serves as a measure of "capital inefficiency," enabling engineers to compare the impact of different part-load solutions and providing a unified framework for evaluating performance across various devices and systems.