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At least 1,297 records · Page 72

Development of a micro-combined heat and power powered by an opposed-piston engine in building applications

Residential homes and light commercial buildings usually require substantial heat and electricity simultaneously. A combined heat and power system enables more efficient and environmentally friendly energy usage than that achieved when heat and electricity are produced in separate processes. However, due to financial and space constraints, residential and light commercial buildings often limit the use of traditional large-scale industrial equipment. Here we develop a micro–combined heat and power system powered by an opposed-piston engine to simultaneously generate electricity and provide heat to residential homes or light commercial buildings. The developed prototype attains the maximum AC electrical efficiency of 35.2%. The electrical efficiency breaks the typical upper boundary of 30% for micro–combined heat and power systems using small internal combustion engines (i.e., <10 kW). Moreover, the developed prototype enables maximum combined electrical and thermal efficiencies greater than 93%. The prototype is optimally designed for natural gas but can also run renewable biogas and hydrogen, supporting the transition from current conventional fossil fuels to zero carbon emissions in the future. The analysis of the unit’s decarbonization and cost-saving potential indicate that, except for specific locations, the developed prototype might excel in achieving decarbonization and cost savings primarily in US northern and middle climate zones.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A simplified model for estimating population-scale energy impacts of building envelope air-tightening and mechanical ventilation retrofits

Changing the air exchange rate of a home (the sum of the infiltration and mechanical ventilation airflow rates) affects the annual thermal conditioning energy. Large-scale changes to air exchange rates of the housing stock can significantly alter the residential sector's energy consumption. However, the complexity of existing residential energy models is a barrier to the accurate quantification of the impact of policy changes on a state or national level. The Incremental Ventilation Energy (IVE) model developed in this study combines the output of simple air exchange models with a limited set of housing characteristics to estimate the associated change in energy demand of homes. The IVE model was designed specifically to enable modellers to use existing databases of housing characteristics to determine the impact of ventilation policy change on a population scale. The IVE model estimates of energy change when applied to US homes with limited parameterisation are shown to be comparable to the estimates of a well-validated, complex residential energy model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The Effects of Development Constraints on Forest Management at the Urban‐Forest Interface

Abstract This article develops a model of a forest owner operating in a small, open‐city environment where the rent for developed land is increasing concave in nearby preserved open space and is rising over time, reflecting an upward trend in households’ income. Within this framework we examine how changes in nearby preserved open space and alternative development constraints affect an individual private landowner's decisions regarding both regeneration harvests and conversion dates. We also examine how such policies affect development density once conversion from forestry to residential use occurs. We show that when residential rents change over time, the nature of the timber problem at the urban‐forest interface changes because it forces us to allow for the possibility of changes in land use from timber to residential use. As a result, the traditional Faustmann setup is not suitable to study forest management decisions at this interface. We also find that in the presence of rising income, increases in permanently preserved open space hasten regeneration and conversion cuts of a neighboring parcel currently under forest use and leads to lower density development once conversion takes place compared to the case where income is constant. We also find that both a binding development moratorium and a binding minimum‐lot‐size policy can postpone regeneration and conversion cut dates and thus help to protect open space, even if only temporarily. However, the policies do not have the same effects on development density of converted forestland. While the former leads to high‐density development, the latter encourages low‐density development.

Cunha‐e‐Sá, Maria A.↗

Developing High PV Penetration Cases for Frequency Response Study of U.S. Western Interconnection

Recent large penetrations of solar photovoltaic (PV) generation and the inertial characteristics of inverter-based generation technologies have caught the attention of those in the electric power industry in the United States. This paper presents a systematic approach to developing test cases of high penetrations of PV for the Western Interconnection. First, to examine the accuracy of the base case model, the Western Electricity Coordinating Council (WECC) model is validated by using measurement data from synchronized phasor measurement units. Based on the 2022 Light Spring case, we developed four high PV penetration cases for the WECC system that are of interest to the industry: 5% PV+15 % wind, 25% PV+15% wind, 45% PV+15% wind, 65% PV+15% wind). Additionally, a method to project PV is proposed that is based on collected, realistic PV distribution information, including the current and future PV power plant locations and penetrations in the WECC system. Both the utility-scale PV plant and residential rooftop PV are included in this study.

14 SOLAR ENERGY↗

Data-Enabled Building Energy Savings (D-E BES)

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building's physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. Herein this paper illustrates that creating an affinity between a building resident's thermal preferences and a building apartment's unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents' thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building's physical characteristics as well as the variation in residents' thermal preferences. The work further demonstrates that both the unregulated temperature and the resident's preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment's geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Transactive Home Energy Management Systems: The Impact of Their Proliferation on the Electric Grid

Approximately 100 million singlefamily homes in the United States account for 36% of the electricity load, and often they determine the peak system load, especially on hot summer days when residential air-conditioning use is high. Traditional building power profiles are changing. Currently, there is an increased use of energy-efficient building materials and designs, which decreases building loads. In addition, there is an increased adoption of rooftop solar photovoltaic (PV), which leads to bidirectional power flow and significant power ramps as PV output decreases in the late afternoon. Building power profiles are likely to change even more as residential energy storage products proliferate. Therefore, a better understanding of residential electricity demand is key to addressing the envisioned transition of the electric power system from its traditional structure to one that is transactive.

25 ENERGY STORAGE↗

Feeder Power Disaggregation: A Data-Efficient Matrix Completion Approach

This paper presents a data-driven algorithm for the feeder power disaggregation problem in distribution systems. Leveraging spatio-temporal power patterns in residential homes, residential power is discomposed into three components: sparse-switching loads, periodic loads, and photovoltaic (PV) generation, which are characterized through the design of two sparse matrices and a low-rank matrix. The matrix completion process is data-efficient because of the matrix sparsity and low rankness, along with the use of power system models. The proposed approach is tested using real-world residential data set on a 33-bus distribution system, demonstrating accurate power disaggregation with efficient matrix completion.

distribution system↗

A Supervised Machine Learning Approach to Control Energy Storage Devices

This paper introduces a supervised machine learning approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/Battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control (EMPC) approach is used as the benchmark. The residential load and PV data used in the paper are 1-minute data downloaded from the the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four machine learning algorithms (i.e. neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the machine learning approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based Machine Learning engine. Furthermore, the work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.

25 ENERGY STORAGE↗

A Decision Support Tool for Distributed Solar and Storage Investments: A Case Study in Austin, TX

This study builds a decision support tool to evaluate when it is a good economic decision (least cost with minimum discomfort) for the residential customer to invest in distributed energy resources (DERs) based on different electricity rate structures, DER ownership frameworks, and DER rebates offered by electric utilities. Here, the tool is demonstrated using empirical electricity consumption data from Pecan Street Inc. (a non-profit entity based on Austin, Texas), residential rates from Austin Energy (the municipal electric utility in Austin, Texas), DER ownership costs from various nationwide pilot programs, and incentives offered by electric utilities in the United States. Results show that for constant electricity rates, the overall expenditure is least when the customer owns solar panels without storage, while for time-varying pricing structures, the least expensive scenario is one where the customer does not own any DERs. As the capital costs for DERs decline, utilities incentivize customer ownership of DERs, and more residential customers face the decision of whether to invest in DERs, this study aims to be a key tool in aiding that decision-making process.

14 SOLAR ENERGY↗

TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies)

NREL's TEMPEST model combines electrical and thermal modeling for residential-scale battery systems. The model and default parameters are based off of two common commercial residential batteries. Key inputs to the model include house load, temperature of the battery location, and the solar profile. The key output of the TEMPEST model is the battery profile and internal battery temperature. If internal battery temperature exceeds limits, a derating will occur and this will be reflected in the battery profile. In addition to thermal modeling, the TEMPEST model uses realistic operating principals that are common in today's residential batteries. Mainly, the TEMPEST model incorporates "modes" of operation that constrain battery setpoint based on house load, solar output, and state of charge. The TEMPEST model can be used to analyze the effect of battery temperature, deratings, and mode setpoints on utility network loading and customer bills.

Blonsky, Michael↗

ResStock™ v3.2.0 [SWR-19-15 and SWR-20-07]

The ResStock™ analysis tool was built on NREL's OpenStudio® platform, and is a project geared at modeling existing residential building stocks at national, regional, or local scales with a high-degree of granularity (e.g., one physics-based simulation model for every 200 dwelling units), using the EnergyPlus® simulation engine. Information about ComStock™, a sister tool for modeling the commercial building stock, can be found here: https://www.nrel.gov/buildings/comstock.html This repository contains: Housing characteristics of the U.S. residential building stock, in the form of conditional probability distributions stored as tab-separated value (.tsv) files. Comments at the bottom of each file document data sources and assumptions for each. A library of housing characteristic "options" that translate high-level characteristic parameters into arguments for OpenStudio measures, and which are referenced by the housing characteristic .tsv files and building energy upgrades defined in project definition files Project definition files: v2.3.0 and later: buildstockbatch YML files openable in any text editor v2.2.5 and prior: Project folder openable in PAT Unit-level OpenStudio Measures for automatically constructing OpenStudio Models of each representative dwelling unit model: v3.0.0 and later: OpenStudio-HPXML Measures v2.5.0 and prior: OpenStudio Measures Higher-level OpenStudio Measures for controlling simulation inputs and outputs This repository does not contain software for running ResStock simulations, which can be found as follows: Versions 2.3.0 and later only support the use of buildstockbatch for deploying simulations on high-performance or cloud computing. Version 2.3.0 also removed separate projects for single-family detached and multifamily buildings, in lieu of a combined project_national representing the U.S. residential building stock. See the changelog for more details. Versions 2.2.5 and prior support the use of the publicly available OpenStudio-PAT software as an interface for deploying simulations on cloud computing. Read the documentation for v2.2.5.

Horowitz, Scott↗

A comparison of methods used to unveil the genetic and metabolic pool in the built environment

BACKGROUND:A majority of indoor residential microbes originate from humans, pets, and outdoor air and are not adapted to the built environment (BE). Consequently, a large portion of the microbes identified by DNA-based methods are either dead or metabolically inactive. Although many exceptions have been noted, the ribosomal RNA fraction of the sample is more likely to represent either viable or metabolically active cells. We examined methodological variations in sample processing using a defined, mock BE microbial community to better understand the scope of technique-based vs. biological-based differences in both ribosomal transcript (rRNA) and gene (DNA) sequence community analysis. Based on in vitro tests, a protocol was adopted for the analysis of the genetic and metabolic pool (DNA vs. rRNA) of air and surface microbiomes within a residential setting. RESULTS:We observed differences in DNA/RNA co-extraction efficiency for individual microbes, but overall, a greater recovery of rRNA using FastPrep (> 50%). Samples stored with various preservation methods at - 80°C experienced a rapid decline in nucleic acid recovery starting within the first week, although post-extraction rRNA had no significant degradation when treated with RNAStable. We recommend that co-extraction samples be processed as quickly as possible after collection. The in vivo analysis revealed significant differences in the two components (genetic and metabolic pool) in terms of taxonomy, community structure, and microbial association networks. Rare taxa present in the genetic pool showed higher metabolic potential (RNA:DNA ratio), whereas commonly detected taxa of outdoor origins based on DNA sequencing, especially taxa of the Sphingomonadales order, were present in lower relative abundances in the viable community. CONCLUSIONS:Although methodological variations in sample preparations are high, large differences between the DNA and RNA fractions of the total microbial community demonstrate that direct examination of rRNA isolated from a residential BE microbiome has the potential to identify the more likely viable or active portion of the microbial community. In an environment that has primarily dead and metabolically inactive cells, we suggest that the rRNA fraction of BE samples is capable of providing a more ecologically relevant insight into the factors that drive indoor microbial community dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

Estimating the Effects of Soil Remediation on Children’s Blood Lead near a Former Lead Smelter in Omaha, Nebraska, USA

Lead exposures from legacy sources threaten children’s health. Soil in Omaha, Nebraska, was contaminated by emissions from a lead smelter and refinery. The U.S. Environmental Protection Agency excavated and replaced contaminated soil at the Omaha Lead Superfund Site between 1999 and 2016. The goal of this study was to assess the association of soil lead level (SLL) and soil remediation status with blood lead levels (BLLs) in children living near or on the site. We linked information on SLL at residential properties with children’s BLLs and assigned remediation status to children’s BLL measurements based on whether their measurements occurred during residence at remediated or unremediated properties. We examined the association of SLL and remediation status with elevated BLL (EBLL). We distinguished the roles of temporal trend and the intervention with time-by-intervention-status interaction contrasts. All analyses estimated odds ratios (ORs) with a generalized estimating equations approach to ensure robustness under the complex correlations among BLL measurements. All analyses controlled for relevant covariates including children’s characteristics. EBLL (> 5 μg/dL ) was associated with both residential SLL [e.g., OR=2.00; 95% confidence interval (CI): 1.83, 2.19; > 400–800 vs. ≤ 200 ppm] and neighborhood SLL [e.g., OR=1.85 (95% CI: 1.62, 2.11; > 400–800 vs. ≤ 200 ppm)] before remediation but only with neighborhood SLL after remediation. The odds of EBLL were higher before remediation [OR 1.52 (95% CI: 1.34, 1.72)]. Similarly, EBLL was positively associated with preremediation status in our interaction analysis [interaction OR=1.18 (95%CI: 1.02, 1.37)]. Residential and neighborhood SLLs were important predictors of EBLLs in children residing near or on this Superfund site. Neighborhood SLL remained a strong predictor following remediation. Our data analyses showed the benefit of soil remediation. Results from the interaction analyses should be interpreted cautiously due to imperfect correspondence of remediation times between remediation and comparison groups. https://doi.org/10.1289/EHP8657

54 ENVIRONMENTAL SCIENCES↗

Model America - data and models of every U.S. building

The 5-year goal of the 'Model America' concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) - OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus(v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy's (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). 'Model America - data and models of every U.S. building. [Data set].' Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

24 POWER TRANSMISSION AND DISTRIBUTION↗

AmeriFlux US-KUT KUOM Turfgrass Field

This is the AmeriFlux version of the carbon flux data for the site US-KUT KUOM Turfgrass Field. Site Description - The site was a low-maintenace lawn consisting of cool-season turfgrasses, typical of residential lawns or urban parks in the study area. It was mowed to a height of 70 mm approximately once per week with clippings left to decompose on the surface, was not irrigated, and received one application of inorganic N fertilizer per year. The surrounding suburban residential neighborhood experienced rapid residential development in the 1950s; prior to that time, farms and nurseries were the predominant land-use types. Note that the land management of the field site changed (removal of the turfgrass lawn and planting of trees) AFTER the flux study was completed--this means that imagery after 2009 does not show the land conditions during the flux study.

McFadden, Joe↗

Plug-in Hybrid Initiative

Our main project objective was to implement Plug-in Electric Vehicles (PEV) and charging infrastructure into our electric distribution service territory and help reduce barriers in the process. Our research demonstrated the desire for some to be early adopters of electric vehicles and the effects lack of education plays on others. The response of early adopters was tremendous: with the initial launch of our program we had nearly 60 residential customers interested in taking part in our program. However, our program only allowed for 15 residential participants. Our program provided assistance towards purchasing a PEV and installation of Electric Vehicle Supply Equipment (EVSE). The residential participants have all come to love their PEVs and are more than enthusiastic about promoting the many benefits of driving electric.

33 ADVANCED PROPULSION SYSTEMS↗

TEP Power Partners Project [Tucson Electric Power]

The Arizona Governor’s Office of Energy Policy, in partnership with Tucson Electric Power (TEP), Tendril, and Next Phase Energy (NPE), formed the TEP Power Partners pilot project to demonstrate how residential customers could access their energy usage data and third party applications using data obtained from an Automatic Meter Reading (AMR) network. The project applied for and was awarded a Smart Grid Data Access grant through the U.S. Department of Energy. The project participants’ goal for Phase I is to actively engage 1,700 residential customers to demonstrate sustained participation, reduction in energy usage (kWh) and cost ($), and measure related aspects of customer satisfaction. This Demonstration report presents a summary of the findings, effectiveness, and customer satisfaction with the 15-month TEP Power Partners pilot project. The objective of the program is to provide residential customers with energy consumption data from AMR metering and empower these participants to better manage their electricity use. The pilot recruitment goals included migrating 700 existing customers from the completed Power Partners Demand Response Load Control Project (DRLC), and enrolling 1,000 new participants. Upon conclusion of the project on November 19, 2013; 1,390 Home Area Networks (HANs) were registered; 797 new participants installed a HAN; Survey respondents’ are satisfied with the program and found value with a variety of specific program components; Survey respondents report feeling greater control over their energy usage and report taking energy savings actions in their homes after participating in the program; On average, 43 % of the participants returned to the web portal monthly and 15% returned weekly; and An impact evaluation was completed by Opinion Dynamics and found average participant savings for the treatment period1 to be 2.3% of their household use during this period.2 In total, the program saved 163 MWh in the treatment period of 2013.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Analysis of the Chinese Market for Building Energy Efficiency

China will account for about half of the new construction globally in the coming decade. Its floorspace doubled from 1996 to 2011, and Chinese rural buildings alone have as much floorspace as all of U.S. residential buildings. Building energy consumption has also grown, increasing by over 40% since 1990. To curb building energy demand, the Chinese government has launched a series of policies and programs. Combined, this growth in buildings and renovations, along with the policies to promote green buildings, are creating a large market for energy efficiency products and services. This report assesses the impact of China’s policies on building energy efficiency and on the market for energy efficiency in the future. The first chapter of this report introduces the trends in China, drawing on both historical analysis, and detailed modeling of the drivers behind changes in floorspace and building energy demand such as economic and population growth, urbanization, policy. The analysis describes the trends by region, building type and energy service. The second chapter discusses China’s policies to promote green buildings. China began developing building energy codes in the 1980s. Over time, the central government has increased the stringency of the code requirements and the extent of enforcement. The codes are mandatory in all new buildings and major renovations in China’s cities, and they have been a driving force behind the expansion of China’s markets for insulation, efficient windows, and other green building materials. China also has several other important policies to encourage efficient buildings, including the Three-Star Rating System (somewhat akin to LEED), financial incentives tied to efficiency, appliance standards, a phasing out of incandescent bulbs and promotion of efficient lighting, and several policies to encourage retrofits in existing buildings. In the third chapter, we take “deep dives” into the trends affecting key building components. This chapter examines insulation in walls and roofs; efficient windows and doors; heating, air conditioning and controls; and lighting. These markets have seen significant growth because of the strength of the construction sector but also the specific policies that require and promote efficient building components. At the same time, as requirements have become more stringent, there has been fierce competition, and quality has at time suffered, which in turn has created additional challenges. Next we examine existing buildings in chapter four. China has many Soviet-style, inefficient buildings built before stringent requirements for efficiency were more widely enforced. As a result, there are several specific market opportunities related to retrofits. These fall into two or three categories. First, China now has a code for retrofitting residential buildings in the north. Local governments have targets of the number of buildings they must retrofit each year, and they help finance the changes. The requirements focus on insulation, windows, and heat distribution. Second, the Chinese government recently decided to increase the scale of its retrofits of government and state-owned buildings. It hopes to achieve large scale changes through energy service contracts, which creates an opportunity for energy service companies. Third, there is also a small but growing trend to apply energy service contracts to large commercial and residential buildings. This report assesses the impacts of China’s policies on building energy efficiency. By examining the existing literature and interviewing stakeholders from the public, academic, and private sectors, the report seeks to offer an in-depth insights of the opportunities and barriers for major market segments related to building energy efficiency. The report also discusses trends in building energy use, policies promoting building energy efficiency, and energy performance contracting for public building retrofits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗