Reducing China’s building material embodied emissions: Opportunities and challenges to achieve carbon neutrality in building materials
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We simulate pathways for achieving up to 100% renewable energy (RE) electric power systems for the contiguous United States. Under base conditions, the least-cost buildout has RE penetration growing up to 57% in 2050. Relative to this base scenario, average CO2 abatement costs of achieving 80%, 90%, 95%, and 100% RE are $25, $33, $40, and $61/ton, respectively, with system costs growing from $30 to $36/MWh at 95% (achieved in 2040) and $39/MWh at 100%. Incremental abatement costs from 99% to 100% RE reach $930/ton, driven primarily by the need for firm RE capacity. In addition to the base conditions, we also examined 22 alternative conditions for a buildout of up to 100%. These sensitivities capture different technology trajectories, compliance requirements, requirement timing, electrification, and transmission availability. Nonlinear marginal costs for the last few percent approaching 100% RE were found for all sensitivities, which might motivate alternative nonelectric-sector abatement opportunities.
Water is a critical resource for all of manufacturing. As such, mitigating risks to the supply of water to manufacturing plants facilitates maintaining a vital manufacturing sector. Reductions in water use through water efficiency is one method for mitigating risk, however little is known about its potential in the United States (U.S.) manufacturing sector. Herein, we address this gap and calculate the water reduction potential in U.S. manufacturing from commercially available water efficiency opportunities specific to the manufacturing sector. A list of water efficiency measures specific to the manufacturing sector is compiled and applied to estimates of U.S. manufacturing water withdrawals. The energy and carbon dioxide equivalent (CO 2 e) emission impacts from implementation of these measures are also quantified to better inform the resource, environmental, and economic trade-offs from implementation of these measures. We find that the water savings potential from the evaluated measures is up to 60 percent of U.S. manufacturing water withdrawal for certain subsectors, with the energy and emissions impact dependent on the measure. Further, we calculate the levelized cost of conserved water for several water efficiency measures (-$\$4,000$ to $\$85$/thousand m 3 , where negative values indicate net cost savings and positive values indicate net cost expenditures) and compare them to the levelized cost of alternative water sources ($\$600$ to $\$3,400$/thousand m 3 ). The results show significant opportunities for water and energy use reductions at levelized costs at least one order of magnitude lower than alternative water supplies, with some being revenue-generating.
Energy assessments can provide an effective way of identifying and implementing energy efficiency measures to save energy costs and avoid emissions throughout manufacturing facilities. Consequently, it generates significant economic and environmental benefits to localities, states, and the nation. Quantifying these benefits requires a systematic techno-economic-environmental framework for capturing the interactions. This article employs methodologies to improve the energy efficiency of small and medium-size industries through their sustainable industrial motor systems. Motor systems offer large opportunities to enhance energy efficiency through adopting advanced technologies and better-informed operations. Case studies presented illuminate the potential savings and impacts from implementing sustainable motor systems and the importance of energy assessments. Here, the integrated macro-economic analysis quantifies the regional sustainability impacts of implementing the industrial energy efficiency offered by an energy auditing program in Ohio over a ten-year period. Results show that implementing all the center’s motor recommendations have directly saved $\$702$ M in energy costs, avoided 2.7 million metric tons of carbon dioxide emissions, and created 3,445 jobs, resulting in a total annual economic impact of $\$788$ M stemming from direct, indirect, and induced regional economic impacts. It provides policy implications for encouraging sustainable industrial motor systems.
Cu-based catalysts for the electrochemical reduction of CO 2 and CO exhibit a perplexingly unique reactivity toward multicarbon based products compared to other studied electrocatalysts. Here, in this study, we use insights gained from a recent phenomenological 3-site microkinetic model and grand-canonical density functional theory calculations to clarify the importance of an underemphasized aspect critical to Cu’s unique reactivity: a population of so-called “reservoir” sites. Using model Cu surface motifs, we discuss how these types can be represented by undercoordinated structural defects like step edges and grain boundaries which form a network of highly anisotropic migration channels. These pathways are found to be amenable for feeding *CO over time to reactive sites like Cu adatoms more active toward C–C coupling. These results highlight an often overlooked aspect of catalyst optimization: reservoir site engineering, which exploits surface mobility and presents an equally important avenue for electrocatalyst engineering oversimply maximizing active site densities.
Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.
Abstract HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft 2 of electricity annually. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40%–60% of its total electricity usage and is equivalent to about 2%–3% of the total energy consumed by commercial buildings in the United States. Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Reducing refrigerant usage or using environmentally friendly alternatives can result in significant climate benefits. A challenge is the lack of publicly available data sets to benchmark the system performance and record the faulted performance. This paper identifies common faults of supermarket refrigeration systems and conducts an experimental study to collect the faulted performance data and analyze these faults. This work provides a foundation for future research on the development of FDD methods and field automated FDD implementation.
This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.
Abstract This study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy’s Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.
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In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing clean hybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation.
Direct current (DC) distribution systems is emerging as a favorable option over alternating current in buildings due to its energy and non-energy benefits (e.g., efficiency, resilience, safety). However, limited guidelines exist to evaluate the DC electrical distribution infrastructure. This paper aims to fill that gap by detailing our experience with metering a building-level electrical DC distribution system. Our case study is a DC microgrid at the Direct Energy Partners Research & Development facility located in Charlotte, North Carolina. This paper presents our approach, technical challenges, and lessons learned with DC metering and instrumentation.
Previously, we reported two-phase capillary-based cooling using narrow (200 to 1000 ..mu..m) heater bridge copper inverse opal (CIO) wicks with heat flux level of 1400 Wcm -2 and superheat ~ 10 degrees C. Here, we demonstrate the area scaling of the proposed technology to a large-area micro-cooler for high-heat-flux cooling of microprocessors and power electronics. We developed a hybrid single/two-phase micro-cooler that relies on capillary-wicking in a 25-..mu..m-thick CIO with an open silicon microchannel 3D-manifold for liquid delivery and vapor extraction, achieving a high heat flux ~ 400 Wcm -2 over a heated area of 10 x 10 mm 2 . For a range of inlet water flowrates from 5 to 60 g(min) -1 , we achieved total thermal resistances and vapor qualities of 0.68 cm 2 degrees CW -1 to 0.2 cm 2 degrees CW -1 and 0.55 to 0.12, respectively. The flowrates are 10x smaller than those of conventional single- or two-phase microchannel cooling technology. The corresponding two-phase thermal resistances ranges from 0.05 to 0.02 cm 2 degrees CW -1 with temperature superheat of 8 to 6 degrees C, respectively. While the overall performance of the large-area (10 x 10 mm 2 ) capillary-based micro-cooler degraded compared to the previous demonstration of the technology for a heated area of 5 x 5 mm 2 , however, preliminary CFD modeling indicates that an improved manifold design will be able to achieve comparable performance.
This paper proposes a non-linear programming (NLP) model to optimally size the energy storage system (ESS) and obtain an optimal energy management for energy arbitrage of an extreme fast charging station (XFCS) for electric vehicles (EVs), with minimized total cost of XFCS operation and ESS investment. Different from most reported work on sizing the ESS for EV charging stations, this paper proposes a pragmatic approach to model the ESS life degradation and accurately count the ESS cycles. Moreover, this work incorporates the peak demand charges in the operational cost of the charging station which are often overlooked in the literature. The proposed model is formulated and solved using AIMMS. Finally, a thorough sensitivity analysis is performed to offer insights into how different input parameters impact the ESS sizing and savings from the energy arbitrage perspective.
The bulk power system is experiencing a dramatic shift as renewable generation growth continues to accelerate. Large-scale renewables adoption will help societies transition to a low-carbon, low-cost, and environmentally friendly electrical power system. However, the transition from a paradigm of generation following load to one where load follows generation will require large-scale interconnection and coordinated operation of Distributed Energy Resources (DERs), supported by open communication protocols. In this future grid scenario, DER aggregations will provide critical grid services that enable high penetration levels of renewable generation. This position paper presents an Energy Service Interface (ESI) that defines scope for ensuring secure, trustworthy information exchange between grid service providers and DERs. The goal of the ESI is to encourage large-scale participation of DERs in order to provide grid services through dispatch of DER aggregations. This position paper also presents an open smart energy communications protocol that allows DERs to advertise their characteristics and participate in grid services, within constraints established by the ESI. The paper presents several monetization incentives that grid service providers could use to encourage large-scale DER participation.
An Entity Component System is a data-oriented architecture originally developed to streamline video game performance. Despite being quite new, Entity Component Systems are relatively well established within the video game industry due to the cutting edge nature of research into performance, especially around graphics. However, Entity Component Systems have not been widely examined or adopted outside of that industry. We propose adopting an Entity Component Systems framework to serve the needs of an Energy Service Interfaces. We examine the needs of an Energy Service Interface, give an overview of open-source Entity Component Systems (ECSs) libraries, examine some preliminary performance results for ECSs, and explore the traditional approach to fulfilling the needs of an Energy Service Interface (ESI) with database architectures.