Engineering topics

Lekov, Alex

Publications and source records attributed to Lekov, Alex.

6 records

DOE OSTI2018

The United States Federal Energy Management Program lighting energy efficiency 2017 update and impacts

Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO2 offset under the high compliance scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Estimating Residential Appliance Lifetime for Energy Efficient Policy Analysis

Accurate knowledge of appliance lifetimes helps policy makers set effective energy efficiency policy. The cost-effectiveness of an appliance efficiency measure depends on the length of time consumers will use an appliance. National shipments projections depend on the rate of replacement in the installed appliance stock. Furthermore, early retirement programs need an accurate understanding of the remaining useful life in order to calculate lifetime savings and cost-effectiveness of a program. This paper uses a method for calculating lifetime of appliances based on the most recent data available on appliance shipments, total appliance stock, and the fraction of surviving appliances by age bins. An example of the calculation methodology is provided for residential gas boilers. This paper concludes by comparing the results to other available data on gas boiler lifetime.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Survey of Hearth Products in U.S. Homes

There are over 7 million gas-fired fireplaces currently installed in US households. On an annual basis, these use substantial energy. However, the details of the fireplace energy use and the factors that influence it are currently not well understood. Fireplaces are a type of hearth product, which is a product category that primarily consists of fireplaces, stoves, and gas log sets. For the purpose of this study, the fuels used in hearth products are primarily natural gas, propane, or electricity. They may be vented or unvented. This study reports the results of a web survey of 2,100 respondents in the United States performed in February 2016. The responses were cleaned and weighted using the raking method to form a nationally representative population. The reported data include hearth product characteristics, usage information, and repair and maintenance practices. The hearth product characteristics include the hearth product type, fuel type, ignition system type, features, venting, and installation details. The usage information includes seasonal usage of the main burner and standing pilot (if present), daily usage, and the primary utility (whether decorative or for heating). These raw data are further processed and combined with values from the literature to estimate the annual operating hours and energy use and to assess how these are impacted by the hearth product type, features, age, and the main heating appliance, if present. Based on the survey responses, the estimated average annual hours of usage was 234 for the main burner, and 4,593 for the standing pilot. The results presented provide the most comprehensive data regarding hearth products in the United States published to date. These new data allow for an improved understanding of hearth products’ energy use, which in turn may facilitate the development of more informed analyses, and ultimately more efficient hearth products and reduced energy use. These new data also provide insight into topics not previously studied, such as the effect of hearth product features on energy use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2011

Incorporating Experience Curves in Appliance Standards Analysis

The technical analyses in support of U.S. energy conservation standards for residential appliances and commercial equipment have typically assumed that manufacturing costs and retail prices remain constant during the projected 30-year analysis period. There is, however, considerable evidence that this assumption does not reflect real market prices. Costs and prices generally fall in relation to cumulative production, a phenomenon known as experience and modeled by a fairly robust empirical experience curve. Using price data from the Bureau of Labor Statistics, and shipment data obtained as part of the standards analysis process, we present U.S. experience curves for room air conditioners, clothes dryers, central air conditioners, furnaces, and refrigerators and freezers. These allow us to develop more representative appliance price projections than the assumption-based approach of constant prices. These experience curves were incorporated into recent energy conservation standards for these products. The impact on the national modeling can be significant, often increasing the net present value of potential standard levels in the analysis. In some cases a previously cost-negative potential standard level demonstrates a benefit when incorporating experience. These results imply that past energy conservation standards analyses may have undervalued the economic benefits of potential standard levels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2011

Effect of Energy Efficiency Standards on Natural Gas Prices

A primary justification for the establishment of energy efficiency standards for home appliances is the existence of information deficiencies and externalities in the market for appliances. For example, when a long-term homeowner purchases a new gas-fired water heater, she will maximize the value of her purchase by comparing the life-cycle cost of ownership of available units, including both total installed cost - purchase price plus installation costs - and operating cost in the calculus. Choice of the appliance with the lowest life-cycle costs leads to the most economically efficient balance between capital cost and fuel cost. However, if the purchaser's expected period of ownership is shorter than the useful life of the appliance, or the purchaser does not pay for the fuel used by the appliance, as is often the case with rental property, fuel cost will be external to her costs, biasing her decision toward spending less on fuel efficiency and resulting in the purchase of an appliance with greater than optimal fuel usage. By imposing an efficiency standard on appliances, less efficient appliances are made unavailable, precluding less efficient purchases and reducing fuel usage. The reduction in fuel demanded by residential users affects the total demand for such fuels as natural gas, for example. Reduced demand implies that residential customers are willing to purchase less gas at each price level. That is, the demand curve, labeled D 0 in Figure 1, shifts to the left to D 1 . If there is no change in the supply function, the supply curve will intersect the demand curve at a lower price. Residential demand is only one component of the total demand for natural gas. It is possible that total demand will decline very little if demand in other sectors increases substantially in response to a decline in the price. If demand does decrease, modeling studies generally confirm the intuition that reductions in demand for natural gas will result in reductions in its price as seen at the wellhead (Wiser 2007). The magnitude of the effect on price relative to the demand reduction, and the mechanism through which it occurs, is less well established. This report attempts to quantify the potential effects of reduced demand for natural gas in the residential sector, in response to the implementation of an energy efficiency standard for water heaters.

03 NATURAL GAS↗

DOE OSTI2011

Simulating a Nationally Representative Housing Sample Using EnergyPlus

This report presents a new simulation tool under development at Lawrence Berkeley National Laboratory (LBNL). This tool uses EnergyPlus to simulate each single-family home in the Residential Energy Consumption Survey (RECS), and generates a calibrated, nationally representative set of simulated homes whose energy use is statistically indistinguishable from the energy use of the single-family homes in the RECS sample. This research builds upon earlier work by Ritchard et al. for the Gas Research Institute and Huang et al. for LBNL. A representative national sample allows us to evaluate the variance in energy use between individual homes, regions, or other subsamples; using this tool, we can also evaluate how that variance affects the impacts of potential policies. The RECS contains information regarding the construction and location of each sampled home, as well as its appliances and other energy-using equipment. We combined this data with the home simulation prototypes developed by Huang et al. to simulate homes that match the RECS sample wherever possible. Where data was not available, we used distributions, calibrated using the RECS energy use data. Each home was assigned a best-fit location for the purposes of weather and some construction characteristics. RECS provides some detail on the type and age of heating, ventilation, and air-conditioning (HVAC) equipment in each home; we developed EnergyPlus models capable of reproducing the variety of technologies and efficiencies represented in the national sample. This includes electric, gas, and oil furnaces, central and window air conditioners, central heat pumps, and baseboard heaters. We also developed a model of duct system performance, based on in-home measurements, and integrated this with fan performance to capture the energy use of single- and variable-speed furnace fans, as well as the interaction of duct and fan performance with the efficiency of heating and cooling equipment. Comparison with RECS revealed that EnergyPlus did not capture the heating-side behavior of heat pumps particularly accurately, and that our simple oil furnace and boiler models needed significant recalibration to fit with RECS. Simulating the full RECS sample on a single computer would take many hours, so we used the 'cloud computing' services provided by Amazon.com to simulate dozens of homes at once. This enabled us to simulate the full RECS sample, including multiple versions of each home to evaluate the impact of marginal changes, in less than 3 hours. Once the tool was calibrated, we were able to address several policy questions. We made a simple measurement of the heat replacement effect and showed that the net effect of heat replacement on primary energy use is likely to be less than 5%, relative to appliance-only measures of energy savings. Fuel switching could be significant, however. We also evaluated the national and regional impacts of a variety of 'overnight' changes in building characteristics or occupant behavior, including lighting, home insulation and sealing, HVAC system efficiency, and thermostat settings. For example, our model shows that the combination of increased home insulation and better sealed building shells could reduce residential natural gas use by 34.5% and electricity use by 6.5%, and a 1 degree rise in summer thermostat settings could save 2.1% of home electricity use. These results vary by region, and we present results for each U.S. Census division. We conclude by offering proposals for future work to improve the tool. Some proposed future work includes: comparing the simulated energy use data with the monthly RECS bill data; better capturing the variation in behavior between households, especially as it relates to occupancy and schedules; improving the characterization of recent construction and its regional variation; and extending the general framework of this simulation tool to capture multifamily housing units, such as apartment buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗