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At least 1,405 records · Page 78

Load Disaggregation Technologies: Real World and Laboratory Performance

Low cost interval metering and communication technology improvements over the past ten years have enabled the maturity of load disaggregation (or non-intrusive load monitoring) technologies to better estimate and report energy consumption of individual end-use loads. With the appropriate performance characteristics, these technologies have the potential to enable many utility and customer facing applications such as billing transparency, itemized demand and energy consumption, appliance diagnostics, commissioning, energy efficiency savings verification, load shape research, and demand response measurement. However, there has been much skepticism concerning the ability of load disaggregation products to accurately identify and estimate energy consumption of end-uses; which has hindered wide-spread market adoption. A contributing factor is that common test methods and metrics are not available to evaluate performance without having to perform large scale field demonstrations and pilots, which can be costly when developing such products. Without common and cost-effective methods of evaluation, more developed disaggregation technologies will continue to be slow to market and potential users will remain uncertain about their capabilities. This paper reviews recent field studies and laboratory tests of disaggregation technologies. Several factors are identified that are important to consider in test protocols, so that the results reflect real world performance. Potential metrics are examined to highlight their effectiveness in quantifying disaggregation performance. This analysis is then used to suggest performance metrics that are meaningful and of value to potential users and that will enable researchers/developers to identify beneficial ways to improve their technologies.

Mayhorn, Ebony T.↗

Development of High Performance Composite Foam Insulation with Vacuum Insulation Cores

Development of a high performance thermal insulation (thermal resistance or R-value per inch of R-12 hr-ft2- F/Btu-in or greater), with twice the thermal resistance of state-of-the-art commercial insulation materials ( R6/inch for foam insulation), promises a transformational impact in the area of building insulation. In 2010, in the US, the building envelope-related primary energy consumption was 15.6 quads, of which 5.75 quads were due to opaque wall and roof sections; the total US consumption (building, industrial and transportation) was 98 quads. In other words, the wall and roof contribution was almost 6% of the entire US primary energy consumption. Building energy modeling analyses have shown that adding insulation to increase the R-value of the external walls of residential buildings by R10-20 (hr-ft2- F/Btu) can yield savings of 38-50% in wall-generated heating and cooling loads. Adding R20 will require substantial thicknesses of current commercial insulation materials, often requiring significant (and sometimes cost-prohibitive) alterations to existing buildings. This article describes the development of a next-generation composite insulation with a target thermal resistance of R25 for a 2 inch thick board (R12/inch or higher). The composite insulation will contain vacuum insulation cores, which are nominally R35-40/inch, encapsulated in polyisocyanurate foam. A recently-developed variant of vacuum insulation, called modified atmosphere insulation (MAI), was used in this research. Some background information on the thermal performance and distinguishing features of MAI has been provided. Technical details of the composite insulation development and manufacturing as well as laboratory evaluation of prototype insulation boards are presented.

Biswas, Kaushik↗

Statistical Analysis of Solar PV Power Frequency Spectrum for Optimal Employment of Building Loads

In this paper, a statistical analysis of the frequency spectrum of solar photovoltaic (PV) power output is conducted. This analysis quantifies the frequency content that can be used for purposes such as developing optimal employment of building loads and distributed energy resources. One year of solar PV power output data was collected and analyzed using one-second resolution to find ideal bounds and levels for the different frequency components. The annual, seasonal, and monthly statistics of the PV frequency content are computed and illustrated in boxplot format. To examine the compatibility of building loads for PV consumption, a spectral analysis of building loads such as Heating, Ventilation and Air-Conditioning (HVAC) units and water heaters was performed. This defined the bandwidth over which these devices can operate. Results show that nearly all of the PV output (about 98%) is contained within frequencies lower than 1 mHz (equivalent to ~15 min), which is compatible for consumption with local building loads such as HVAC units and water heaters. Medium frequencies in the range of ~15 min to ~1 min are likely to be suitable for consumption by fan equipment of variable air volume HVAC systems that have time constants in the range of few seconds to few minutes. This study indicates that most of the PV generation can be consumed by building loads with the help of proper control strategies, thereby reducing impact on the grid and the size of storage systems.

boxplot↗

Enhanced separation of rare earth elements

Industrial rare earth separation processes utilize PC88A, a phosphonic acid ligand, for solvent extraction separations. The separation factors of the individual rare earths, the equipment requirements, and chemical usage for these flowsheets are well characterized. Alternative ligands such as Cyanex® 572 and the associated flowsheets are being investigated at the pilot scale level to determine if significant improvements to the current separation processes can be realized. These improvements are identified as higher separation factors, reduced stage requirements, or reduced chemical consumption. Any of these improvements can significantly affect the costs associated with these challenging separation proccesses. A mid/heavy rare earth element (REE) separations flowsheet was developed and tested for each ligand in a 30 stage mixer-settler circuit to compare the separation performance of PC88A and Cyanex® 572. The ligand-metal complex strength of Cyanex® 572 provides efficient extraction of REE while significantly reducing the strip acid requirements. Reductions in chemical consumption have a significant impact on process economics for REE separations. Partitioning results summarized Table 1 indicate that Cyanex® 572 offers the same separation performance as PC88A while reducing acid consumption by 30% in the strip section for the mid/heavy REE separation. Flowsheet Effluent Compositions PC88A Cyanex® 572 Raffinate Mid REE Heavy REE 99.40% 0.60% 99.40% 0.60% Rich Mid REE Heavy REE 2.20% 97.80% 0.80% 99.20% Liquor Strip Acid Required 3.4 M 2.3 M Table 1 – Flowsheet results comparing separation performance of PC88A and Cyanex® 572 for a mid/heavy REE separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint

A data-driven technique for estimation of energy requirements for a proposed vehicle trip has been developed. Based on over 700,000 miles of driving data, the technique has been applied to generate a model that estimates trip energy requirements. The model uses a novel binning approach to categorize driving by road type, traffic conditions, and driving profile. The trip-level energy estimations can easily be aggregated to any higher-level transportation system network desired. The model has been tested and validated on the Austin, Texas, data set used to build this model. Ground-truth energy consumption for the data set was obtained from Future Automotive Systems Technology Simulator (FASTSim) vehicle simulation results. The energy estimation model has demonstrated 12.1 percent normalized total absolute error. The energy estimation from the model can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations, to reduce energy consumption. The model can also be used to determine more accurate energy consumption of regional or national transportation networks if trip origin and destinations are known. Additionally, this method allows the estimation tool to be tuned to a specific driver or vehicle type.

33 ADVANCED PROPULSION SYSTEMS↗

Analyzing How We Do Analysis and Consume Data, Results from the SciDAC-Data Project

One of the main goals of the Dept. of Energy funded SciDAC-Data project is to analyze the more than 410,000 high energy physics datasets that have been collected, generated and defined over the past two decades by experiments using the Fermilab storage facilities. These datasets have been used as the input to over 5.6 million recorded analysis projects, for which detailed analytics have been gathered. The analytics and meta information for these datasets and analysis projects are being combined with knowledge of their part of the HEP analysis chains for major experiments to understand how modern computing and data delivery is being used. We present the first results of this project, which examine in detail how the CDF, D0, NOvA, MINERvA and MicroBooNE experiments have organized, classified and consumed petascale datasets to produce their physics results. The results include analysis of the correlations in dataset/file overlap, data usage patterns, data popularity, dataset dependency and temporary dataset consumption. The results provide critical insight into how workflows and data delivery schemes can be combined with different caching strategies to more efficiently perform the work required to mine these large HEP data volumes and to understand the physics analysis requirements for the next generation of HEP computing facilities. In particular we present a detailed analysis of the NOvA data organization and consumption model corresponding to their first and second oscillation results (2014-2016) and the first look at the analysis of the Tevatron Run II experiments. We present statistical distributions for the characterization of these data and data driven models describing their consumption

Ding, P.↗

Personal Fuel Economy in Conventional and Hybrid Electric Vehicles: Integrating Driving Cycle Predictions with Power Distribution Optimization

Improving fuel efficiency and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel efficiency in laboratory controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This study uses standard driving cycles as a base for comparison, while aiming to develop fuel efficiency estimates based on personalized driving cycles that can inform consumers’ vehicle purchase and use decisions. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: aggressive, normal, or calm. Then the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate personal fuel efficiency for conventional vehicles and HEVs. To optimize personal fuel efficiency estimates, the Equivalent Consumption Minimization Strategy (ECMS) is applied. Depending on the driving style (aggressive, normal, or calm) and the driving scenario (city or highway driving), conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel efficiency was 13% lower in calm city driving, but almost 34% higher for aggressive highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on application of case-based reasoning and used to tune the power distribution in HEVs, their fuel consumption can improve by up to 12% in city driving. Implications and limitations of the findings are discussed in the paper.

Liu, Jun↗

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any type of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

25 ENERGY STORAGE↗

Power quality of service optimization for microgrids

Various examples are provided for power quality of service optimization for microgrids. In one example, among others, a microgrid includes a smart meter configured to control supply of electric power to loads based at least in part upon energy consumption scheduling of the loads, which is based at least in part upon an effective electric generation capacity associated with the microgrid. The energy consumption scheduling can be based at least in part upon estimated power output of a sustainable energy resource of the microgrid and a load demand characterization of the loads. In another example, a system comprises a plurality of microgrids and an advanced metering infrastructure (AMI) configured to monitor operations of the microgrids and to control supply of electric power from sustainable energy resources and energy storage systems to loads of the microgrids via smart meters based at least in part upon energy consumption scheduling of the loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗

National Energy Impacts of Heavy Electric Truck Adoption For Freight

Freight trucks are projected to account for nearly 30% of transportation sector energy use in 2050 in the United States. Potentially some of the freight energy use and associated emissions can be reduced by electrified vehicle technologies, smart technologies (e.g. platooning, driverless truck), and possible mode shifts. Several U.S. truck makers, new startups, and independent engine manufacturers made announcements of taking commericialized medium- and heavy-duty trucks into production for these emerging technologies. By analyzing the freight shipment by commodity and freight zones, the study objectives are 1) idenfiy long-haul freight miles that could be electrified without on-route charging infrastructure; and 2) quantify future energy consumption impacts. This paper utilized Argonne’s NEAT and VISION models to estimate the potential energy impacts of electric truck adoption by 2050 in the United States. Analysis results shows that either a Class 7 and 8 electric truck with 500-mile electric range could potentially reduce the petroleum consumption by 1.61 quad in 2050, while the electricity consumption increases by 0.99 quad.

Electric heavy duty truck, freight↗

Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint

One of the few methods electric utilities can use to motivate and change customer energy consumption is through their retail rate structures. Utilities are increasingly moving towards more dynamic rate-plans to achieve the objectives of motivating energy conservation, increasing self-consumption from onsite renewable generation, reducing peak demand and to flattening the demand profiles. This paper presents a set of rate-oriented customer load metrics that are the determinants of customers bill under four unique rate-plans. These metrics are not only indicative of which rate structure a customer should choose based on their load profiles, but also convey useful information about load consumption behavior. With these metrics, the utility can analyze their customers and identify customer classes that are rewarded under each rate-plan. This can help inform utilities whether the customer profiles being rewarded under each rate-plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on from a set of 300 customer profiles, examining the impact of four different rate-plans, different numbers of clusters and looking at customer bills and cluster load profile characteristics.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Modeling Electrolysis-based H2 Production for Grid Service Assessment

Hydrogen is a promising clean and renewable source of energy. Its high energy density offers a unique and effective means to store energy. The integration of hydrogen production devices with the power grid could lead to a cleaner and more reliable grid operation at reasonable costs. In this work, the capability of Proton Exchange Membrane (PEM) electrolyzers to perform grid services is assessed using a battery equivalent model approach. The Battery Equivalent Model (BEM) has been developed to represent the physics and operation of a fleet of PEM electrolyzers. The BEM model has been designed to be modular and readily incorporated into grid planning studies. This application of PEM electrolyzers can be integrated with other power generation and consumption devices, aggregating capabilities of each device to enhance the ability of the integrated system to provide grid services and improves grid stability. The developed model is evaluated using ancillary support services including peak management, frequency regulation, voltage regulation, artificial inertia, spinning reserve, and wholesale market price response using a representative price and power system profiles. The BEM model has also been evaluated to provide frequency regulation service that conforms to the IEEE 1547-2018 standard. Metrics such as energy efficiency, charging efficiency, hydrogen production rate, and service efficacy have been incorporated into the model to quantify the performance of the fleet while responding to grid services. Results show the electrolyzer fleet's effectiveness in quickly adjusting its real power consumption based on grid service requests. The energy efficiency of the fleet is maintained relatively high – in the range of 78% and 90% during the service operation. These results demonstrate the ability of PEM electrolyzers to effectively provide fast dispatch capability through storing off-peak hour energy while regulating power consumption during peak hours. The BEM model can assist in quantifying the stability and variability impacts from integration of PEM electrolyzers with intermittent renewable resources in grid integration studies.

08 HYDROGEN↗

Permanent Electrical Loads in New Homes

Numerous electrical products and components are installed by the builder during construction and prior to occupancy. Some of these components are required by safety codes, such as smoke detectors, others are needed to support the communications infrastructure, and still others provide features that future occupants will find useful, such as remotely controlled garage door openers. We compiled a list of over 50 builder-installed devices that are likely to have continuous power consumption, and measured the power consumptions for a smaller group. A procedure to measure these loads in a home was developed based on readings from the smart meter. A typical American home can easily have 80 W of continuous power – over 650 kWh/year – devoted to these components. New homes are likely to have more of these devices and higher loads. Techniques to reduce this energy use include: selection of lowest-power solutions when a range of power consumptions are available, more efficient circuitry and power management, a separate DC circuit to serve DC-powered appliances, and use of energyscavenging sensors and controls in place of grid-powered components. A protocol to define and measure builder-installed loads, along with a recommended ceiling, might also stimulate savings.

Meier, Alan↗

FINAL TECHNICAL AND ECONOMIC FEASIBILITY STUDY ON THE APPLICATION OF A HEAT INTEGRATED POST-COMBUSTION CO2 CAPTURE SYSTEM WITH HITACHI ADVANCED SOLVENT INTO EXISTING COAL-FIRED POWER PLANT

This report contains the results of a techno-economic assessment (TEA) conducted of a heat integrated post-combustion CO2 capture process with Hitachi advanced solvent for retrofit into an existing coal-fired power plant (but treated as greenfield plant on cost analysis). The process has been developed by the University of Kentucky Center for Applied Energy (UK CAER). EPRI was chiefly responsible for this analysis, with significant input from WorleyParsons, Hitachi Power Systems America (Hitachi) and UK CAER. The project also involves the design, fabrication, installation, testing, and analyses of a slipstream facility located at L&GE-KU’s E.W. Brown Generating Station to demonstrate the UK CAER carbon capture system that could utilize heat integration with the main power plant. The design, start-up, and baseline of the pilot system was performed with a generic 30 wt% MEA solvent to obtain data for direct comparison with the DOE/NETL Reference Case followed by testing Hitachi’s proprietary solvent H3-1. In this techno-economic analysis, two cases utilizing the UK CAER process are compared, using different approach temperatures and solvent, against the DOE/NETL Reference Case (Case 10). The results are shown comparing the energy demand for post-combustion CO2 capture and the net higher heating value (HHV) efficiency of the power plant integrated with the post-combustion capture (PCC) plant. A levelized cost of electricity (LCOE) assessment was performed showing the costs of the options presented in the study. The key factors contributing to the reduction of LCOE were identified as CO2 partial pressure increase at the flue gas inlet, thermal integration of the process, and performance of the Hitachi H3-1 solvent. Recent UK CAER process pilot-scale testing data and process simualtion data showed that the packing heights of absorber and stripper columns were significantly oversized in the prelimanary TEA (Task 2 of this project) and thus updated in this final TEA for the H3-1 case only. In addition, the solvent make-up cost for H3-1 was updated based on lattest test results. Finally, a heat integration with the main power plant was applied in this final TEA to increase overall energy effciency for both the MEA and H3-1 cases. Additonal reductions in capital and operational costs are expected but not taken into account here. Shorter columns result in reduced pressure drops, smaller blower head and pump hydraulic head requirements. An increase in overall energy efficiency resuls in a decreased size of the power plant, the CCS and a reduced parasitic steam requirement to the CCS. The net efficiency of the UK CAER integrated PC power plant with CO2 capture changes from 26.2% for the Reference Case 10 plant in 2010 revised DOE/NETL baseline report to 27.6% for the MEA options considered, and 29.1% for the options utilizing the Hitachi advanced solvent. The UK CAER Process + Hitachi case also produces an extra 30.9 MW of generation compared to the UK CAER Process + MEA case and total 60.9 MW more than DOE Case 10. LCOE ($/MWh) values are $172.08/MWh for the MEA option and $157.65/MWh for the Hitachi H3-1 solvent cases considered in comparison to $189.59/MWh in January 2012 dollar for the Reference Case 10. The UK CAER CCS process with MEA case lowers energy consumption for CO2 capture to 1340 Btu/lb-CO2 captured as compared to 1540 Btu/lb-CO2 in the Reference Case 10. The UK CAER CCS process with H3-1 case further lowers energy consumption for CO2 capture to 973 Btu/lb-CO2 captured, for an advantage of 36.8% less energy consumption than Case 10. The study also shows 38.1% less heat rejection associated with the carbon capture system from 3398 MBtu/hr (Case 10) to 2104 MBtu/hr for the UK CAER + MEA system. Heat rejection is reduced to 2464 MBtu/hr in the UK CAER + H3-1 case, for a 27.5 % decrease compared to Case 10. Modeling outputs show that in the UK CAER process, the cooling water that is 2-5°C cooler than conventional cooling tower water can be achieved for ambient conditions common to the midwest and other regions. The results from the techno-economic assessment show that the proposed technology can be investigated further as a viable alternative to conventional CO2 capture technology. The evaluation also shows the effect of the critical parameters on the LCOE, with the main variables being the approach temperature and CO2 partial pressure increase at the flue gas inlet. A summary of the key advantages of the UK CAER Process + H3-1 case for LCOE and other economic factors compared to the DOE Case 10 is as follows: • A lower variable operating cost by $1.56/MWh ($1.08MWh less than the UK CAER Process + MEA Case), a 11.7% reduction compared to the DOE Case 10 • A lower COE by $25.32MWh ($13.94/MWh lower than the UK CAER Process + MEA Case), a 16.9% reduction compared to the DOE Case 10 • A lower LCOE by $31.94/MWh ($17.51/MWh lower than the UK CAER Process + MEA Case), a 16.9% reduction compared to the DOE Case 10 • A lower cost of CO2 captured by $18.65/tonne CO2 ($9.44/tonne CO2 lower than the UK CAER Process + MEA Case), a 30.4% reduction compared to the DOE Case 10 • A lower cost of CO2 avoided by $34.95/tonne CO2 ($18.53 tonne CO2 lower than the UK CAER Process + MEA Case), a 38.7% reduction compared to the DOE Case 10

Bhown, Abhoyjit S.↗

Forecasting the Impact of Dual-credit Policy (2021-2023) on China's Electric Vehicle Market

China is well known for its ambition on large-scale vehicle electrification which currently is mainly driven by fuel economy and electric vehicle policies. This study adopts the New Energy and Oil Consumption Credits (NEOCC) model, a vehicle policy analysis tool developed by the Oak Ridge National Laboratory, to systematically quantify the potential impacts of the “Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation” which was released in June 2020 for the timeframe 2021-2023, so called dual-credit policy (2021-2023). It is found that, under the dual-credit policy (2021-2023), the sales of low fuel consumption conventional vehicles will increase faster than they were in 2018-2020, and the share of plug-in electric vehicles (PEVs) could reach 11.4% by 2023 if it keeps the expansion as it was in 2017. Besides, the BEVs with long electric range (such as 400 km) and the plugin hybrid SUVs would be the most popular PEV types.

Ou, Shawn↗

An Assessment of Impacts on Electric End-Use Load Profile of a Typical Residential Building from a Ground Source Heat Pump Systems Integrated with Underground Thermal Energy Storage

Buildings are the primary users of electricity, and a considerable amount of electric energy consumed in residential and commercial buildings is for meeting thermal demands (e.g., space heating/cooling, water heating, etc.). Therefore, integrating thermal energy storage with a building’s energy system has the potential to reshape the electric load profile of the building and mitigate the mismatch between the renewable power supply and the thermal demands of buildings. A novel ground-source heat pump system (GSHP) integrated with underground thermal energy storage (UTES) has been developed to enable flexible electric demand at buildings while meeting their thermal demands with an energy-efficient GSHP system. This paper assessed the potential impacts of the proposed system on the peak electrical demand and annual electricity consumption in a typical residential building at various climate zones in the United States. The results show that by replacing the conventional HVAC system (air conditioner and natural gas furnace) with the UTES integrated GSHP system, summer peak electric demand can be reduced by 27% to 50% depending on the weather. Although the winter peak electrical demand could be increased by up to 70% if the proposed system replaces natural gas furnace, the existing electricity supply system for single-family homes in most regions of the United States have adequate capacity for the replacement. While eliminating the natural gas consumption for space heating, the proposed system consumes more electricity on an annual basis in heating-dominated regions. However, it consumes less electricity in cooling dominated regions. The resulting variance in annual electricity consumption in various climate zones is within 15% compared with the baseline system.

Shi, Liang↗

A Sub-Threshold Low-Power Integrated Bandpass Filter for Highly-Integrated Spectrum Analyzers

Low-power analog filter banks provide frequency analysis with minimal power and space requirements, making them viable solutions for integrated remote audio- and vibration-sensing applications. Compared with their digital counterparts, analog filter banks are better suited to achieve the lower power consumption necessary for IoT applications, while maintaining high dynamic range and a wide operational frequency range. In this work, the design and implementation of a sub-threshold complementary metal-oxide semiconductor (CMOS) integrated low-power tunable analog filter channel for signal spectrum analysis and signal discrimination is presented. Project specifications required a programmable, high-order, monolithic bandpass filter channel with small chip area, low power consumption, high dynamic range, and wide operational frequency range. The 8th-order filter channel presented in this work provides an effective Q-factor of 4.5 and a minimum dynamic range (DR) of approximately 85 dB, while allowing for tuning across a range of center frequencies from 2 kHz to 100 kHz, with power consumption of a single 8th-order filter channel measured at 256 µW nominally at the highest center frequency. An integrated analog Gm-C filter topology was selected for this application. Functionally, the high-Q bandpass filter transfer function is implemented via four cascaded 2nd-order filter cells, resulting in a single 8th-order filter channel, fabricated in 130-nm 1.2-V CMOS technology, suitable for use in monolithic integrated spectral analysis (MISA1) applications.

Long, Gavin B.↗

ComStock/COVID-19: U.S. Commercial Building Stock Analysis of COVID-19 Mitigation Strategies

ComStock is a DOE platform, with NREL as the lead developer, that models the U.S. commercial building stock using OpenStudio/EnergyPlus to perform physics-based building energy modeling simulations. The ASHRAE Epidemic Taskforce (ASHRAE-ETF) has released several COVID-19 mitigation strategies for commercial buildings. ComStock was used to analyze the impact of four strategies on the existing commercial building stock. These strategies are upgrading to MERV-13 filters, disabling demand control ventilation (DCV), increasing minimum outdoor air to 100%, and HVAC flushing mode operation. Results show the impact of each measure on energy consumption, electric consumption, gas consumption and peak electric.

30 DIRECT ENERGY CONVERSION↗