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DOE OSTI2017

Measurements of intermediate-frequency electric and magnetic fields in households

Historically, assessment of human exposure to electric and magnetic fields has focused on the extremely-low-frequency (ELF) and radiofrequency (RF) ranges. However, research on the typically emitted fields in the intermediate-frequency (IF) range (300 Hz to 1 MHz) as well as potential effects of IF fields on the human body remains limited, although the range of household appliances with electrical components working in the IF range has grown significantly (e.g., induction cookers and compact fluorescent lighting). In this study, an extensive measurement survey was performed on the levels of electric and magnetic fields in the IF range typically present in residences as well as emitted by a wide range of household appliances under real-life circumstances. Using spot measurements, residential IF field levels were found to be generally low, while the use of certain appliances at close distance (20 cm) may result in a relatively high exposure. Overall, appliance emissions contained either harmonic signals, with fundamental frequencies between 6 kHz and 300 kHz, which were sometimes accompanied by regions in the IF spectrum of rather noisy, elevated field strengths, or much more capricious spectra, dominated by 50 Hz harmonics emanating far in the IF domain. The maximum peak field strengths recorded at 20 cm were 41.5 V/m and 2.7 A/m, both from induction cookers. Finally, none of the appliance emissions in the IF range exceeded the exposure summation rules recommended by the International Commission on Non-Ionizing Radiation Protection guidelines and the International Electrotechnical Commission (IEC 62233) standard at 20 cm and beyond (maximum exposure quotients EQ{sub E} 1.0 and {sub E}Q{sub H} 0.13). - Highlights: • Survey of residential electric and magnetic fields at intermediate frequencies (IF). • IF-EF and -MF emitted by 280 household appliances were characterised. • Strongest emitters were induction cookers, CFLs, LCD-TVs, and microwave ovens. • No emissions exceeded ICNIRP limits (highest exposure quotient was 1.00).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Beyond-the-Meter: Unaccounted Sources of Methane Emissions in the Natural Gas Distribution Sector

The United States Environmental Protection Agency maintains an inventory of greenhouse gas emissions in accordance with the Intergovernmental Panel on Climate Change. Methane (CH 4 ), a potent gas with a global warming potential 86–125× that of carbon dioxide (CO 2 ) over a twenty-year period, is the main component of natural gas (NG). As NG becomes an increasingly larger percentage of the energy resources used in the United States, it is ever more important to evaluate the CH 4 emissions inventory. However, the inventory also does not account for all possible sources of CH 4 leaks, contributing to uncertainty in the national CH 4 inventory. Discrepancies between top-down and bottom-up inventories of CH 4 emissions imply that there are significant unaccounted-for sources of CH 4 leaks, especially over cities. Diffuse CH 4 plumes above cities that are not attributable to distribution pipelines or other NG infrastructure suggest many small beyond-the-meter leaks together contribute to large emissions. Here, we evaluate the distribution sector of the CH 4 emissions inventory and make suggestions to improve the inventory by analyzing end-user emissions. Preliminary research into beyond-the-meter emissions suggests that while individually small, the appliances and buildings that make up the residential sector could contribute significantly to national scale emissions. Furnaces are the most leak-prone of appliances, contributing to 0.14% of total CH 4 emissions from the NG sector in the United States. Combining measurements from whole house emissions and steady-state operation of appliances, we estimate that residential homes and appliances could release 9.1 Gg CH 4 yearly in the United States, totaling over 2% of the CH 4 released from the NG sector. Here, while factors such as appliance age and usage, climate, and residential setting could influence the emissions profile of individual appliances, these preliminary estimates justify further exploration of beyond-the-meter emissions.

03 NATURAL GAS↗

DOE OSTI2013

Laboratory Evaluation of Energy Recovery Ventilators

Over the years, building scientists have characterized the relationship between building airtightness, exhaust-only appliances airflows, and building depressurization. Now, as the use of deep retrofit measures and new construction practices is growing to realize lower infiltration levels in increasingly tighter envelopes, performance issues can arise with the operation of exhaust-only appliances in a depressurized home. As the depressurization levels climb in tighter homes, many of these exhaust-only appliances see their rated airflows reduced and other related performance issues arise as a result. If sufficiently depressurized, atmospherically vented combustion appliances that may be present in the home can backdraft as well. Furthermore, when exhaust-only appliances operate and the tight home becomes depressurized, water vapor intrusion from outdoors can raise additional issues of mold in the building envelope in more humid climates. The use of energy recovery ventilators (ERVs) in tight homes in mixed-humid and hot-humid climates is already practiced. Unbalancing the ERV by reducing exhaust airflows can provide a means to supply makeup air and reduce the level of home depressurization to mitigate these issues, helping to realize the rated performance of the exhaust-only appliances, achieve safe operation of atmospherically vented combustion, provide the necessary level of fresh ventilation air, and/or improve envelope durability. The unbalanced operation of the ERV to address depressurization can obviously result in unintended consequences in other areas, such as further loss of humidity control in homes in more humid climates. In such tight home scenarios, it is expected that ERVs will operate a substantial amount of time in unbalanced mode with much higher supply than exhaust airflows. Although ERV design performance under balanced flow conditions is well documented, there is no information in the public domain that empirically establishes the effect of unbalanced flow on sensible and latent exchange, especially in the now dominant membrane-type ERV used in residential applications. Therefore, in this task, Gas Technology Institute (GTI) focused on a laboratory evaluation of the unbalanced flow (lowered exhaust airflow) performance of the RenewAire model EV 200, membrane-type ERV delivering 200 standard cubic feet per minute (SCFM) of supply air. The conducted tests show significant variation in ERV effectiveness at different levels of unbalanced air flows. Keeping supply airflow constant and lowering exhaust airflow increases effectiveness of both latent and sensible heat exchange. However, the increased effectiveness is accompanied by reduced capacity for load removal from the supply air, resulting in increased load to the conditioned space. Under decreasing exhaust airflow conditions, sensible load effectiveness exceeded a value of 1 at two sets of testing conditions. This was a result of the temperature rise of the supply air passing through the ERV augmented by the effect of motor heat gain, cross leakage heat gain, and casing heat gain. Consequently, measured sensible effectiveness must be qualified as the so-called Apparent Sensible Effectiveness of the ERV unit (the term used in the Canadian Standards Association C439 standard for testing ERVs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Decentralized control of units in smart grids for the support of renewable energy supply

Due to the significant environmental impact of power production from fossil fuels and nuclear fission, future energy systems will increasingly rely on distributed and renewable energy sources (RES). The electrical feed-in from photovoltaic (PV) systems and wind energy converters (WEC) varies greatly both over short and long time periods (from minutes to seasons), and (not only) by this effect the supply of electrical power from RES and the demand for electrical power are not per se matching. In addition, with a growing share of generation capacity especially in distribution grids, the top-down paradigm of electricity distribution is gradually replaced by a bottom-up power supply. This altogether leads to new problems regarding the safe and reliable operation of power grids. In order to address these challenges, the notion of Smart Grids has been introduced. The inherent flexibilities, i.e. the set of feasible power schedules, of distributed power units have to be controlled in order to support demand–supply matching as well as stable grid operation. Controllable power units are e.g. combined heat and power plants, power storage systems such as batteries, and flexible power consumers such as heat pumps. By controlling the flexibilities of these units we are particularly able to optimize the local utilization of RES feed-in in a given power grid by integrating both supply and demand management measures with special respect to the electrical infrastructure. In this context, decentralized systems, autonomous agents and the concept of self-organizing systems will become key elements of the ICT based control of power units. In this contribution, we first show how a decentralized load management system for battery charging/discharging of electrical vehicles (EVs) can increase the locally used share of supply from PV systems in a low voltage grid. For a reliable demand side management of large sets of appliances, dynamic clustering of these appliances into uniformly controlled appliance sets is necessary. We introduce a method for self-organized clustering for this purpose and show how control of such clusters can affect load peaks in distribution grids. Subsequently, we give a short overview on how we are going to expand the idea of self-organized clusters of units into creating a virtual control center for dynamic virtual power plants (DVPP) offering products at a power market. For an efficient organization of DVPPs, the flexibilities of units have to be represented in a compact and easy to use manner. We give an introduction how the problem of representing a set of possibly 10{sup 100} feasible schedules can be solved by a machine-learning approach. In summary, this article provides an overall impression how we use agent based control techniques and methods of self-organization to support the further integration of distributed and renewable energy sources into power grids and energy markets. - Highlights: • Distributed load management for electrical vehicles supports local supply from PV. • Appliances can self-organize into so called virtual appliances for load control. • Dynamic VPPs can be controlled by extensively decentralized control centers. • Flexibilities of units can efficiently be represented by support-vector descriptions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Assessing electrification readiness in U.S. single-family homes based on a nationwide survey of electrical panel capacities

Electrification of residential buildings is a key strategy for increasing the use of renewable energy sources. Central to this transition is understanding the capacity of existing electrical infrastructure—specifically electrical panels—to safely and effectively manage increased electricity demands from electrification technologies. However, comprehensive nationwide data on electrical panel capacities in U.S. single-family homes is currently lacking. To address this gap, we conducted a nationwide survey of single-family homes, collecting detailed data on electrical panel capacities, breaker slot availability, major electric and gas appliances, electrical panel models, and home characteristics such as construction year and floor area. Photographic documentation was used to verify electrical panel data and appliance information. Results show that approximately 60% of surveyed homes have electrical panels rated at ≥200 amperes (A), indicating that a significant portion of the existing housing stock can accommodate additional electric loads. However, 31% of homes possess panels rated at ≤100 A, potentially restricting their ability to adopt new electric appliances without significant upgrades. Panel capacities positively correlate with both home size and construction year, with newer and larger homes generally better suited for electrification. Homes with higher-capacity panels tend to have fewer gas appliances, reflecting a gradual shift toward electric technologies. Conversely, homes with lower-capacity panels frequently rely on multiple gas appliances, highlighting substantial electrification challenges. Additionally, approximately 3% of surveyed homes had potentially hazardous electrical panel models, emphasizing important safety considerations in the residential electrification process. Our findings underscore the need for targeted policies, financial incentives, and infrastructure investments designed specifically to address infrastructural and safety barriers, particularly in older and smaller homes, to support equitable and efficient electrification across the U.S. residential sector.

Gul, Sadia↗

DOE OSTI2016

Laboratory Evaluation of Energy Recovery Ventilators

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Laboratory Evaluation of Energy Recovery Ventilators

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Intelligent Energy Optimizer for Residential Buildings

Demand-side management in the buildings is essential for meeting grid flexibility needs in a highly renewable energy scenario. Appliance load monitoring helps decision making for demand-side management by providing the information on operation status/power consumption from different appliances in the buildings. Nonintrusive load monitoring (NILM) is an attractive option for appliance load monitoring using because it has lower cost for sensors and helps mitigate privacy concerns. In this study, the team used an event detection technique followed by two different methods for event classification. The results from k-means clustering showed that the events from a single appliance are often distributed in multiple clusters. Thus, the unsupervised method of NILM using k-means clustering used in this study was not very suitable for load disaggregation. The results from NILM showed that the F1 score for event classification was 0.77 for a heat pump water heater and very low for other appliances using the rule-based classification.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Modeled Performance of the Hathaway Solar Patriot house - Washington D.C

This Dataset contains field research raw data, analysis spreadsheet, photos, and final report from the Hathaway Solar Patriot House Building America Case Study project. This dataset details the monitored and modeled performance of a solar home outside of Washington, D.C. We modeled the home energy performance using DOE 2.2, performed numerous short-terms tests on the home, and monitored its occupied performance for 29 months. The home used modular construction, solar water heating, a ground-coupled heat pump, efficient appliances, and compact fluorescent lighting to reduce its energy consumption by 35% compared to the Building America research benchmark home. The addition of 6kW of photovoltaics (PV) increased the savings to 67% compared to the Building America research benchmark. A more efficient shell to reduce space conditioning loads would have brought the home closer to its zero energy goals. However, even with efficient lighting and appliances, the lights, appliance and plug loads were a significant energy consumer. About 4 kW of PV was required to meet the needs of these loads alone. To achieve the zero energy goal with no further efficiency increases, the Hathaway house would need about 2.6 kW of PV in addition to the 6.0 kW it now has. Applying advanced efficiency measures available or being developed, such as heat-recovery ventilation, superinsulation, and electrochromic windows, could reduce the heating, cooling, and domestic hot water (DHW) energy use to less than 1700 kWh per year, an 88% reduction in these loads from the Building America Benchmark. At this efficiency level, the appliance and plug loads come to dominate energy consumption and account for nearly 70% of the total energy use. This analysis points out that even with highly effective energy-savings technologies (pushed beyond levels currently practical), whole-house energy use reduction by efficiency measures is only about 60% without also reducing the energy use of appliances and plug loads largely considered outside the designers jurisdiction.

Array↗

DOE OSTI2013

Evidence of Progress - Measurement of Impacts of Australia's S&L Program from 1990-2010

Australia first put categorical energy efficiency labels on residential appliances in the mid-1980s, and the first Minimum Energy Performance Standards (MEPS) for refrigerators was implemented in 1999. Updated in 2005, these MEPS were aligned with US 2001 levels. Considered together, these actions set Australia apart as having one of the most aggressive appliance efficiency programs in the world. For these reasons, together with good data on product sales over time, Australia represents a potentially fruitful case study for understanding the dynamics energy efficiency standards and labeling (EES&L) programs impacts on appliance markets. This analysis attempts to distinguish between the impacts of labeling alone as opposed to MEPS, and to probe the time-dependency of such impacts. Fortunately, in the Australian case, detailed market sales data and a comprehensive registration system provides a solid basis for the empirical evaluation of these questions. This paper analyzes Australian refrigerator efficiency data covering the years 1993-2009. Sales data was purchased from a commercial market research organization (in this case, the GfK Group) and includes sales and average price in each year for each appliance model; this can be used to understand broader trends by product class and star rating category, even where data is aggregated. Statistical regression analysis is used to model market introduction and adoption of high efficiency refrigerators according to logistic adoption model formalism, and parameterizes the way in which the Australian programs accelerated adoption of high-efficiency products and phased out others. Through this analysis, the paper presents a detailed, robust and quantitative picture of the impacts of EES&L in the Australian case, but also demonstrates a methodology of the evaluation of program impacts that could form the basis of an international evaluation framework for similar programs in other countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2013

Evidence of progress. Measurement of impacts of Australia's S and L program from 1990-2010

Australia first put categorical energy efficiency labels on residential appliances in the mid-1980s, and the first Minimum Energy Performance Standards (MEPS) for refrigerators was implemented in 1999. Updated in 2005, these MEPS were aligned with US 2001 levels. Considered together, these actions set Australia apart as having one of the most aggressive appliance efficiency programs in the world. For these reasons, together with good data on product sales over time, Australia represents a potentially fruitful case study for understanding the dynamics energy efficiency standards and labeling (EES and L) programs impacts on appliance markets. This analysis attempts to distinguish between the impacts of labeling alone as opposed to MEPS, and to probe the time-dependency of such impacts. Fortunately, in the Australian case, detailed market sales data and a comprehensive registration system provides a solid basis for the empirical evaluation of these questions. This paper analyzes Australian refrigerator efficiency data covering the years 1993-2009. Sales data was purchased from a commercial market research organization (in this case, the GfK Group) and includes sales and average price in each year for each appliance model – this can be used to understand broader trends by product class and star rating category, even where data is aggregated. Statistical regression analysis is used to model market introduction and adoption of high efficiency refrigerators according to logistic adoption model formalism, and parameterizes the way in which the Australian programs accelerated adoption of high-efficiency products and phased out others. Through this analysis, the paper presents a detailed, robust and quantitative picture of the impacts of EES and L in the Australian case, but also demonstrates a methodology of the evaluation of program impacts that could form the basis of an international evaluation framework for similar programs in other countries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Using choice architecture to exploit a university Distinct Urban Mine

Highlights: • More than USD 11 million lie within this UK DUM for small household appliances. • Different collection systems should be established for working or broken appliances. • Consumer behaviour should inform choice architecture. •DUM evaluations should inform future collection system investments. - Abstract: There are widespread concerns regarding the potential future scarcity of ferrous and non-ferrous materials. However, there are already potentially rich reserves of secondary materials via high ownership of Electrical and Electronic Equipment (EEE) in economically-developed nations. Young people are particularly high consumers of EEE, thus university students and campuses may present an opportunity to harness this potential. University Distinct Urban Mines (DUM) may be used to exemplify how potential reserves of secondary metals may be exploited, and could contribute to the transition from a linear to a circular economy. This study aimed to evaluate small household appliances (SHA) DUM from a UK university, with the objectives to identify and quantify student households’ SHA ownership, WEEE recycling, stockpiling and discarding habits amongst student households, assess and evaluate the monetary potential of SHA DUM at UK level, and propose methods to exploit DUM for universities in the UK. To this purpose, a quantitative survey was undertaken to measure students’ ownership and discarding behaviour with respect to SHA. The amounts of ferrous and non-ferrous materials were then estimated and converted to monetary values from secondary materials market data to appraise the SHA DUM overall value. Thirty-five per cent of SHA are discarded in the general refuse. Broken personal care appliances (PCA) tend to be discarded due to hygiene and small size factors. When in working order, SHA tend to be equally reused, recycled or stockpiled. We conclude that a total of 189 tonnes of ferrous and non-ferrous materials were available via discarding or being stockpiled at the University of Southampton. Extrapolated to UK higher education level, discarded and stockpiled SHA represent a potential worth ∼USD 11 million. To initiate DUM exploitation within Higher Education campuses, we suggest improving users’ choice architecture by providing collection methods specific to broken SHA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Distributed Smart Grid Asset Control Strategies for Providing Ancillary Services

With large-scale plans to integrate renewable generation driven mainly by state-level renewable portfolio requirements, more resources will be needed to compensate for the uncertainty and variability associated with intermittent generation resources. Distributed assets can be used to mitigate the concerns associated with renewable energy resources and to keep costs down. Under such conditions, performing primary frequency control using only supply-side resources becomes not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in primary frequency control to maintain the stability of the system at an acceptable cost. The main objective of this project is to develop a novel hierarchical distributed framework for frequency based load control. The framework involves two decision layers. The top decision layer determines the optimal gain for aggregated loads for each load bus. The gains are computed using decentralized robust control methods, and will be broadcast to the corresponding participating loads every control period. The second layer consists of a large number of heterogeneous devices, which switch probabilistically during contingencies so that aggregated power change matches the desired amount according to the most recently received gains. The simulation results show great potential to enable systematic design of demand-side primary frequency control with stability guarantees on the overall power system. The proposed design systematically accounts for the interactions between the total load response and bulk power system frequency dynamics. It also guarantees frequency stability under a wide range of time varying operating conditions. The local device-level load response rules fully respect the device constraints (such as temperature setpoint, compressor time delays of HVACs, or arrival and departure of the deferrable loads), which are crucial for implementing real load control programs. The promise of autonomous, Grid Friendly™ response by smart appliances in the form of under-frequency load shedding was demonstrated in the GridWise Olympic Peninsula Demonstration in 2006. Each controller monitored the power grid voltage signal and requested that electrical load be shed by its appliance whenever electric power-grid frequency fell below 59.95 Hz. The controllers and their appliances responded reliably to each shallow under-frequency event, which was an average of one event per day and shed their loads for the durations of these events. Another objective of this project was to perform extensive simulation studies to investigate the impact of a population of Grid Friendly™ Appliances (GFAs) on the bulk power system frequency stability. The GFAs considered in this report are represented as demonstration units with water heaters individually modeled.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

A Field Study of Wall Furnace Venting and Coincident Exhaust Fan Usage in 16 Northern California Apartments

To inform efforts to improve combustion appliance testing in residential energy efficiency programs, we studied the frequency of coincident fan use and depressurization-induced downdrafting and spillage from atmospherically vented (i.e., natural draft) wall furnaces in airtight apartments. Indoor environmental conditions, heating appliance operation, use of exhaust fans, and cooking with stovetop or oven were monitored for approximately three weeks each in 16 apartment units in two buildings in Northern California. Apartments also were assessed using standard combustion appliance safety test methods and enhanced protocols. Monitoring occurred in February and March of 2016, with heating demand corresponding to 7.3 ± 0.5 heating degree-days at a 65ºF reference temperature. Most of the furnaces spilled combustion products when the apartments were depressurized in the “worst-case” challenge condition of all exhaust fans operating at their highest settings and all windows closed. Many also spilled under less challenging conditions (e.g., with kitchen exhaust fan on low and bathroom fan operating). On average, bathroom exhaust fans were operated 3.9% of monitored minutes (13.5% max), and cooking (burner or kitchen fan operation) occurred 4.6% of minutes (max 13.3%). Event lengths averaged 17 minutes (max 540) and 34 minutes (max 324), respectively. Their coincident operation averaged 0.34% of minutes (max 2.0%), with average event length of 13 minutes (max 92 minutes). This suggests that the operation of apartment units at or near the currently used worst-case challenge condition is quite rare. Wall furnace burners operated an average of 2.8% of minutes (max of 8.9%), with average burner cycle length of 14 minutes (max 162). Coincident bath fan use, cooking and wall furnace operation was very rare, occurring only a handful of times across all apartments. The highest rate was 0.075% of monitored minutes in one apartment, and the longest event length was 12 minutes. Exhaust fan operation in this study may have been more frequent than typical as participants were asked to use an exhaust fan whenever cooking or bathing. Consistent with the low levels of coincident operation, unambiguous spillage occurred in only 4 apartments and the longest event was 5 minutes. The frequency of partial spillage is unknown, owing to a lack of a clear signal from monitored parameters. Downdrafting during exhaust fan use occurred in all 13 of the apartments with relevant data, and 9 of these units had 10 or more events. Exhaust fans also sometimes led to weakened draft, even if downdrafting did not occur. Each unambiguous spillage event identified in the study was immediately preceded by downdrafting. The observed occurrence of downdrafting and spillage may have been impacted in those apartments with the most severe drafting problems (i.e., appliances spilled combustion pollutants under ‘natural’ test conditions), because occupants in these units were instructed to open windows whenever using the kitchen exhaust fan.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

BE-SATED: Building Energy Storage At The Edges of Demand

Otherlab is leading research efforts to validate and deploy battery-integrated appliances in real-world settings. The primary goal is to demonstrate the ability of these appliances to shift residential energy loads and reduce peak demand. Additionally, the project aims to secure safety certifications for battery-integrated appliances and establish a key industrial partnership with an appliance manufacturer for broader market adoption.

25 ENERGY STORAGE↗

DOE OSTI2017

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Laboratory Resources and Techniques to Evaluate Smart Home Technology: Preprint

Smart home technologies promise to make residential buildings more comfortable, less costly to operate, more energy efficient, and more integrated with the grid, but these systems are difficult to evaluate in traditional laboratory spaces. To aid in the development and evaluation of new home energy management systems (HEMS), NREL built the Systems Performance Laboratory (SPL) within the Energy Systems Integration Facility. SPL has electrical infrastructure for three residential buildings, complete with smart meters, breaker panels and a full complement of major and minor appliances in each 'home.' Cord and plug power connections mean that appliances can easily be swapped for different models. Distributed energy resources (DER), such as residential photovoltaic (PV) inverters and home batteries, are also available for integration. Sensors monitor the power consumption of all loads and controllers can simulate occupancy to drive the appliances in realistic ways. A novel hardware-in-the-loop application was developed to ensure air conditioner cycling by combining a building simulation with a physical air conditioner and a thermostat, which allows us to simulate performance in any climate with any building construction. Another application of the hardware-in-the-loop technique uses a grid simulation to model the grid impact for different control strategies related to smart home technologies. This laboratory is a powerful, flexible tool that can be employed to evaluate the effect that home energy management and other smart home technologies will have at the appliance, whole-home, and distribution feeder levels - all of which are important perspectives for different stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2011

TV Energy Consumption Trends and Energy-Efficiency Improvement Options

The SEAD initiative aims to transform the global market by increasing the penetration of highly efficient equipment and appliances. SEAD is a government initiative whose activities and projects engage the private sector to realize the large global energy savings potential from improved appliance and equipment efficiency. SEAD seeks to enable high-level global action by informing the Clean Energy Ministerial dialogue as one of the initiatives in the Global Energy Efficiency Challenge. In keeping with its goal of achieving global energy savings through efficiency, SEAD was approved as a task within the International Partnership for Energy Efficiency Cooperation (IPEEC) in January 2010. SEAD partners work together in voluntary activities to: (1) ?raise the efficiency ceiling? by pulling super-efficient appliances and equipment into the market through cooperation on measures like incentives, procurement, awards, and research and development (R&D) investments; (2) ?raise the efficiency floor? by working together to bolster national or regional policies like minimum efficiency standards; and (3) ?strengthen the efficiency foundations? of programs by coordinating technical work to support these activities. Although not all SEAD partners may decide to participate in every SEAD activity, SEAD partners have agreed to engage actively in their particular areas of interest through commitment of financing, staff, consultant experts, and other resources. In addition, all SEAD partners are committed to share information, e.g., on implementation schedules for and the technical detail of minimum efficiency standards and other efficiency programs. Information collected and created through SEAD activities will be shared among all SEAD partners and, to the extent appropriate, with the global public.As of April 2011, the governments participating in SEAD are: Australia, Brazil, Canada, the European Commission, France, Germany, India, Japan, Korea, Mexico, Russia, South Africa, Sweden, the United Arab Emirates, the United Kingdom, and the United States. More information on SEAD is available from its website at http://www.superefficient.org/.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗