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This success story highlights the benefits to the Native Village of Kiana of implementing energy efficiency and heat recovery projects through the DOE Remote Alaska Communities Energy Efficiency (RACEE) competition.
In this study, to foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures.
With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.
With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.
While energy efficiency can contribute significantly towards improving access to modern energy services, energy sector investments in many developing countries have largely focused on increasing energy access by increasing supply. This is because the links between energy efficiency and energy access, is often overlooked. This oversight of energy efficiency is frequently a missed opportunity, as efficiency is often a very cost-effective energy resource. In combination with grid expansion and new clean energy generation, efficiency efforts can help to ensure that reliable power is provided to the maximum number of customers at a lower cost than would be required to increase generation alone. Here in this paper we describe an analysis method for determining a country's energy efficiency priorities and devising an action plan to integrate energy efficiency as a resource for meeting a nation's energy access goals. We illustrate this method with a detailed case study of Uganda. If the most efficient technologies on the market were adopted in Uganda, 442 MW of generation-level demand could be offset and energy access for an additional 6 M rural customers could be achieved by 2030. Of this technical potential for efficiency, 91% is cost-effective, and 47% is economically achievable under conservative assumptions.
The energy efficiency of optoelectronic switches based on high-voltage silicon photodiodes, phototransistors, and photothyristors controlled by picosecond laser pulses during the formation of voltage pulses on resistive load R{sub L} is studied for the first time. It is shown that at the given values of the resistive load R{sub L}, pulse amplitude U{sub R} and duration t{sub R}, there exist optimum device areas, energies, and absorbances of control radiation providing a maximum total switch efficiency of ~0.92. All three switch types feature almost the same efficiency at short t{sub R}; at longer t{sub R}, photothyristors have a noticeable advantage.
To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.
This paper develops and utilizes an optimization based framework to investigate the maximal energy efficiency potentially attainable by HVAC system operation in a non-predictive context. Performance is evaluated relative to the existing state of the art set-point reset strategies. The expected efficiency increase driven by operation constraints relaxations is evaluated.
Energy Efficiency Resource Standards (EERS) are a long-standing policy used to advance state goals and priorities, such as reliability, affordability and decarbonization. They ensure cost-effective energy efficiency is used to lower energy consumption, reduce peak demand, and enable grid flexibility. Adapting EERS through strategic design, and integrating EERS into broader energy plans and policies, can help states optimize their approach to meet modern and evolving power system needs and achieve sustainable energy outcomes. This report examines how states consider energy efficiency in the context of four policy priorities: providing grid benefits, addressing load growth, reducing emissions, and promoting affordability. It provides examples of states that are pursuing these priorities, reviews the role of energy efficiency in them and identifies opportunities to further promote energy efficiency alongside the priorities. Decisionmakers and stakeholders can draw directly from these examples and findings to design an EERS that maximizes co-benefits, captures synergies, and promotes consideration of the all the possible solutions to advance their goals.
Improving the efficiency of energy production and consumption and switching to lower carbon energy sources can significantly decrease carbon dioxide (CO2) emissions and reduce climate change impacts. A growing body of research has found that these measures can also directly mitigate many non-climate change related human health hazards and environmental damage. Positive impacts of policies and programs that occur in addition to the intended primary policy goal are called co-benefits. Policy analysis relies on forecasting and comparing the costs of policy and program implementation and the benefits that accrue to society from implementation. GHG reduction and energy efficiency policies and programs face political resistance in part because of the difficulty of quantifying their benefits. On the one hand, climate change mitigation policy benefits are often global, long-term, and subject to large uncertainties, and subsidized energy pricing can reduce the direct monetary benefits of energy efficiency policies to below their cost. On the other hand, the co-benefits that accrue from these efforts’ resultant reductions in conventional air pollution (such as improved health, agricultural productivity, reduced damage to infrastructure, and local ecosystem improvements) are generally near term, local, and more certain than climate change mitigation benefits and larger than the monetary value of energy savings. The incorporation of co-benefits into energy efficiency and climate mitigation policy and program analysis therefore might significantly increase the uptake of these policies. Faster policy uptake is especially important in developing countries because ongoing development efforts that do not consider co-benefits may lock in suboptimal technologies and infrastructure and result in high costs in future years. Over the past two decades, studies have repeatedly documented that non-climate change related benefits of energy efficiency and fuel conversion efforts, as a part of GHG mitigation strategies, can be from between 30% to over 100% of the costs of such policies and programs strategies. Policy makers around the world are increasingly interested in including both GHG and non-GHG impacts in analyses of energy efficiency and fuel switching policies and programs and a set of methodologies has matured from the efforts of early moving jurisdictions such as the European Union, the United States, and Japan.
Here, this paper examines the relationships between energy efficient (EE) capital technology and household energy insecurity in the United States. The theoretical model of these relationships employs household production theory to capture the demand for and production of household energy services, and a stochastic production frontier approach to describe how having access to and the usage intensity of EE capital technology could help alleviate inefficiency in the production of household energy services. A working hypothesis formulated from the theoretical model posits that having EE capital technology in the home will reduce the level of household energy insecurity experienced. The extent of energy insecurity experienced is inferred from an energy insecurity index value assigned to each household, generated via the application of a dichotomous Rasch model to questions contained in the 2015 Residential Energy Consumption Survey. Noting the potential simultaneous relationship that exists between a household having access to and the usage intensity of EE capital technology and the experience of being energy insecure, an instrumental variables approach was employed to estimate a series of ordered logit models. Results suggest access to EE capital technology in the form of Energy Star® appliances, Energy Star® windows, or a SMART thermostat does not reduce the probability of experiencing a greater level of energy insecurity. Nor does the usage intensity of EE capital. Thus, policy instruments designed to alleviate household energy insecurity may need to go beyond simply helping households obtain EE capital technology.
The increasing frequency and intensity of extreme weather events, such as heatwaves and cold snaps, present significant challenges to building energy performance and occupant comfort. Highly correlated with climate events are widespread long duration power interruptions that may affect thousands of buildings and millions of customers. This study evaluates the impact of building energy performance and occupant thermal comfort in medium-sized office buildings in a cold climate region. Using energy models representing pre-1980 and 2019 vintages, simulations were conducted to assess energy performance under typical weather conditions and occupant thermal comfort during power interrupted extreme cold snap and heatwave climate events under both current 2020s and future 2050s weather conditions. The results show a projected 33% increase in cooling energy demand and a 19% reduction in heating energy by 2050. Findings reveal that older buildings are more susceptible to cold discomfort during cold snaps, while modern airtight buildings are more vulnerable to overheating during heatwaves. Various passive energy efficiency measures, such as improved infiltration control, thermal windows, solar-controlled windows, and cool envelopes, were evaluated for their ability to mitigate thermal discomforts. Solar controlled windows and weatherstripping contribute to reducing cold thermal discomfort by 21% during a power-interrupted cold snap. Solar-controlled windows were found to reduce hot thermal discomfort by 34% during a future power-interrupted heatwave. The study highlights the importance of targeted retrofitting strategies to enhance thermal resilience, especially during power outages, to ensure occupant safety and comfort during extreme climate events.
In addition to benefiting all customers by reducing the total electric system cost, utility customer-funded energy efficiency programs provide direct benefits to the participants. Understanding the current demographic and household characteristics of participants will help assess the extent of inequities in program participation and figure out what characteristics need to be targeted to achieve equitable outcomes. This report describes how 11 demographic and household characteristics including income, race and ethnicity, and education affect participation in residential utility customer-funded energy efficiency programs. It compiles previous work on this topic and adds new primary analysis of four datasets with different levels of detail from the Residential Energy Consumption Survey (RECS), two New England states, and a Midwestern state.
Following the calculation of optimal energy transfer in thermal environment in our first paper [J. L. Wu, F. Liu, Y. Shen, J. S. Cao, and R. J. Silbey, New J. Phys. 12, 105012 (2010)], full quantum dynamics and leading-order 'classical' hopping kinetics are compared in the seven-site Fenna-Matthews-Olson (FMO) protein complex. The difference between these two dynamic descriptions is due to higher-order quantum corrections. Two thermal bath models, classical white noise (the Haken-Strobl-Reineker (HSR) model) and quantum Debye model, are considered. In the seven-site FMO model, we observe that higher-order corrections lead to negligible changes in the trapping time or in energy transfer efficiency around the optimal and physiological conditions (2% in the HSR model and 0.1% in the quantum Debye model for the initial site at BChl 1). However, using the concept of integrated flux, we can identify significant differences in branching probabilities of the energy transfer network between hopping kinetics and quantum dynamics (26% in the HSR model and 32% in the quantum Debye model for the initial site at BChl 1). This observation indicates that the quantum coherence can significantly change the distribution of energy transfer pathways in the flux network with the efficiency nearly the same. The quantum-classical comparison of the average trapping time with the removal of the bottleneck site, BChl 4, demonstrates the robustness of the efficient energy transfer by the mechanism of multi-site quantum coherence. To reconcile with the latest eight-site FMO model which is also investigated in the third paper [J. Moix, J. L. Wu, P. F. Huo, D. F. Coker, and J. S. Cao, J. Phys. Chem. Lett. 2, 3045 (2011)], the quantum-classical comparison with the flux network analysis is summarized in Appendix C. The eight-site FMO model yields similar trapping time and network structure as the seven-site FMO model but leads to a more disperse distribution of energy transfer pathways.
This project explored an innovative modeling approach for assessing the energy efficiency potential of the existing low-income Oceano neighborhood, in unincorporated San Luis Obispo County, and its effects on renewable generation requirements for achieving zero net energy (ZNE). One particular challenge faced was ascertaining the technical energy savings potential across a large neighborhood in a timely manner. The project overcame this challenge through the use of the URBANopt simulation platform, developed at the National Renewable Energy Laboratory. URBANopt manages the automated model creation, simulation, and result aggregation for different design scenarios of existing or candidate buildings in a large neighborhood. Customized energy efficiency packages considered for the residential and commercial sector in the neighborhood included lighting, HVAC, plug load, water heating, envelope, and energy education measures. As a result of the analysis for the Oceano community, energy efficiency measures that are available through incentive programs may provide a 7.4% reduction in the required distributed energy resource (DER) size, corresponding to a 25,000 ft2 decrease in PV array area, to achieve ZNE. Through this work, a computational tool was developed and is being leveraged by the County of San Luis Obispo (CoSLO). The tool has enabled the CoSLO staff to perform similar analysis of the technical potential for energy efficiency, and an understanding of the DER asset requirements and potential savings in other communities within the county.
This paper summarizes efforts to develop new—and enhance existing—analytical frameworks and metrics to quantify the value that grid-interactive efficient homes with solar (GEB-solar homes) can provide. Industry is working to characterize and understand these capabilities and benefits, but existing analytical frameworks for evaluating energy efficiency (EE) are often siloed from those that evaluate distributed energy resources (DERs). Five metrics were adapted from an extensive literature review and applied to case studies of a modeled home in Riverside, California: ramp up/down, cover factor demand/supply, and curtailable load. Eight different technology scenarios were analyzed using a more tightly connected suite of building-to-grid models (BEopt, REopt, ReEDS, and PLEXOS). Additionally, an initial version of the Cambium tool was developed, characterizing the marginal prices and emissions from NREL’s 2018 Standard Scenarios. These grid costs were extended to a time-varying proxy retail rate and applied as part of a new grid alignment metric. In the results, a more integrated combination of GEB-solar technologies led to a higher cover factor demand—the percentage of gross home load covered by on-site solar—however, a benchmark was required to determine what range of cover factor was “best” for given grid conditions. To that end, a grid alignment cost metric was applied to the case study scenarios. The average cost to serve the net load of the home decreased from a median of ~$0.24/kWh to ~$0.10/kWh when the most integrated technology scenario was optimized towards the grid pricing proxy versus the time-of-use (TOU) rate.
The U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy offers resources that can help identify and quantify energy savings opportunities, and then to understand how to access this potential.
The energy efficiency upgrades project at Hardin County General Hospital did not include research nor was it a demonstration project. The project enabled the hospital to replace outdated systems with modern efficient models. Hardin County General Hospital is a 501c3, nonprofit hospital and the sole community provider for Hardin and Pope Counties of Illinois. This project provided much needed equipment and facility upgrades that would not have been possible through locally generated funding. Task 1 was a reroofing of the hospital. The hospital architect designed the replacement to increase the energy efficiency of the hospital roof/ceiling structure. Task 2 was replacement and installation of a new more efficient CT scanner for the hospital. Included in the project was replacement of HVAC equipment for the entire radiological suite. Task 5 was a replacement and installation of a new higher capacity diesel-fueled emergency generator for the hospital replacing a 50+ year old gas-fired generator. Task 7 was the replacement of 50+ year-old walk-in cooler/freezer with a newer, energy efficient model. Task 8 was the replacement of 10+ year-old washing machines in the hospital laundry with higher capacity, energy efficient models. Task 9 was replacement of 50-year old single pane curtain window system with double-pane insulated windows. Additionally, insulation was added around ventilation systems and the curtain wall system.