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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Using facility-level emissions data to estimate the technical potential of alternative thermal sources to meet industrial heat demand

Industry is frequently highlighted as the world's largest energy-using end-use sector. More specifically, the demand for heat drives much of the demand for energy and fossil fuels in the industrial sector. We conduct a top-down analysis to characterize historical energy and fossil fuel use in 14 top-GHG-emitting industries in the United States, their heat demand requirements, and the potential to substitute heat from geothermal, solar thermal (including concentrating technologies), and small modular nuclear reactors to meet these needs while reducing fossil-fuel use and greenhouse gas (GHG) emissions. We base this analysis on publicly-available facility-level GHG emissions and fuel-combustion data, in addition to assumed requirements for process temperature, to demonstrate the potential value to industry energy analysts of the U.S. Environmental Protection Agency's Greenhouse Gas Reporting Program data. We estimate on a county level that the adoption of these alternative heat sources could have reduced 2015 fossil-fuel use by approximately 31% and combustion GHG emissions by approximately 24%. The most extensive substitution opportunities are in the ethyl alcohol manufacturing and wet corn-milling industries; petroleum refining represents the largest absolute emissions mitigation potential. This initial top-down analysis of substitution potential does not consider more detailed technical factors, including resource availability that will influence the actual deployment of alternative energy technologies. The analysis also does not consider the economic or market factors, including the expected cost to build and operate these generators. We do assume process byproducts that are extensively used for combustion fuels would not be good candidates for substitution for alternative energy generators. Based on these caveats, our analysis could be considered a top-range estimate for this mix of heat generators and industry heat demands. Any subsequent analysis of these alternate energy sources should increase the level of technical, economic, and policy detail.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Estimating the price (in)elasticity of off-grid electricity demand

Community-scale power infrastructure may be the only electrification option for tens of millions households that remain out of reach from centralized power grids. The responsiveness of household electricity demand to price is a crucial design input for off-grid systems. While the price elasticity of electricity demand of grid-connected consumers has been abundantly studied, few studies focus on off-grid communities where substantial econometric challenges arise, including the absence of metered consumption data and electricity prices that are simultaneously determined by cost and demand considerations. This study attempts to address these challenges for the case of off-grid micro hydropower consumers. It makes two core contributions: First, we propose the surface area of the contributing hydrologic catchment as a new instrumental variable to estimate elasticity using a cross sectional dataset of existing micro hydropower infrastructure. Second, we provide a first price-elasticity estimate (-0.15) for off-grid electricity demand in Nepal. We surmise that the small (in absolute value) elasticity value found in this study arises from the low levels of consumption observed off-the-grid. We use a Monte Carlo analysis to show that failing to account for this disparity can lead to substantial financial losses caused by suboptimal power infrastructure design.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Commercial building HVAC demand flexibility with model predictive control: Field demonstration and literature insights

Model Predictive Control (MPC) for building Heating Ventilation and Air Conditioning (HVAC) systems is beginning to gain traction in the market, with a few controls companies incorporating it into their product offerings. However, it remains difficult to assess whether the energy cost savings are enough to justify the cost of MPC implementation for a particular building, given the limited number of reported demonstrations. For small commercial and residential buildings with relatively uniform systems, standardized approaches can help lower implementation costs. In contrast, for large buildings or district systems, the potential magnitude of cost savings could justify more customized solutions. Estimating the cost-effectiveness of MPC becomes more challenging for medium and large commercial buildings, where a one-size-fits-all solution may not be suitable, and the potential energy cost savings may be insufficient to justify a customized solution. To make MPC technology more appealing, incorporating additional value streams beyond energy efficiency alone can significantly increase its attractiveness. One such revenue stream is demand flexibility, in response to dynamic electricity prices, where MPC can leverage the thermal mass of the building to shift the load and support the grid. Building on an extensive literature review of MPC field studies focused on cost savings and demand flexibility, this paper presents the results of implementing MPC control in a large office building HVAC system in Berkeley, CA. Four different dynamic electricity price profiles were integrated into the MPC objective function to shift building demand while maintaining comfort, and field testing was performed with each price profile across four seasons. The results show potential for 40–65 % demand decrease percentage and up to 61 % annual cost savings compared to the existing rule-based control strategy, under the tested dynamic price scenarios. This paper also presents a sensitivity analysis on the cost savings with respect to the price profile variability, discusses the implementation effort for the price-responsive MPC, and compares the cost savings found in this study to those found in literature on the basis of dynamic price variability, or so-called Electricity Price Relative Standard Deviation.

Zanetti, Ettore↗
DOE OSTI2019

Quantifying streamflow regime behavior and its sensitivity to demand

This paper presents a new framework for quantitatively identifying, characterizing and analyzing systematic hydrological cycles resulting from streamflow variability in a way that integrates water supply and water demand. The hydrological cycles in question are measures of the most severe drought and pluvial events in a historical record of streamflow, along with their respective durations. The metrics developed here to quantify such episodes are based on an extended sequent peak algorithm that tracks the dynamic shifts in hydrological behavior of streamflow by accounting for water supply and demand with respect to streamflow. In the interest of being able to analyze the largest possible scope of hydrological cycles and behavior, we apply the quantitative methods developed in this paper to streamflow reconstructions in the Upper Missouri River Basin (UMRB) as a case study. We find that the duration of dry periods increase conspicuously as a function of increasing demand levels, the duration of pluvial events decrease as a function of increasing demand levels, and that the general tendency is for streamflow gauges on or near the main stem of the river to have shorter dry spell durations and typically lower drought severity. On the other hand, being on or near the main stem tends to result in longer-duration pluvial events, though these pluvials are typically less severe than those off the main stem. Persistence and spatial variability of streamflow reconstructions were also analyzed to shed further light on the spatial patterns identified earlier, and to see if this variability and persistence may have an influence on the behavior of the streamflow as quantified by the metrics defined in this paper. Finally, it was found that there is a stochastic dependence between the length of a drought and the time to recovery from that drought, and this dependence is used to create simple conditional probability curves to help water managers prepare for future extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Automated electrical demand peak leveling in a manufacturing facility with short term energy storage for smart grid participation

Demand side management of energy is a vital function in the smart grid that allows for greater integration of renewable energy resources, and is facilitated with economic incentives in energy and demand pricing schedules. Manufacturing systems can take advantage of these incentives to reduce their energy costs through active energy management. This paper is a simulation analysis on the implementation of a novel algorithm that levelizes the maximum power load of an industrial bakery that operates under an on-peak demand price structure. The algorithm takes advantage of an untapped thermal energy storage resource in the facility, a chilled glycol buffer tank, to level the short intra-day power oscillations that the facility experiences. One of the key advantages of the algorithm is that it does not require precise demand forecasting or complex control algorithms, such as model predictive control. Even with substantial error in peak power estimates (5%), the algorithm is expected to result in at least a 2% peak reduction. Under ideal prediction, the algorithm reduces the on-peak facility power maximum by 7.3% of the possible 9.0% reduction the storage can provide. Further, the algorithm uses very few facility specific inputs, and can readily be adapted for any facility with short intra-day oscillatory power profiles and untapped storage capacity. The key insight of the paper is the employment of a novel algorithm to leverage untapped energy storage in manufacturing facilities to transform them into smart grid participants with no major capital investment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Energy management systems for forecasted demand error compensation using hybrid energy storage system in nanogrid

This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. Further, the power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange.

25 ENERGY STORAGE↗
DOE OSTI2021

Renewable energy analysis in indigenous communities using bottom-up demand prediction

This paper provides a methodology for the holistic analysis of hybrid renewable energy systems in rural communities. Electric demand is an important component for modeling and analysis of renewable energy systems. Typically, electric demand data is not available due to the internal privacy policies of utility providers. Therefore, this study proposes the use of bottom-up approaches for the development of the electric demand profile, considering the general homogeneity of residential and commercial buildings in rural communities. As a test case, this study develops the electric demand profile and investigates the technical and environmental feasibility of a hybrid renewable energy system for the New Town community on the Fort Berthold Indian Reservation (FBIR) in North Dakota. This study conducts the hybrid renewable energy system’s analysis by developing scripts in the LK scripting language and integrating System Advisor Model software’s open-source modules for modeling of renewable energy systems. Here, the results for the validation testbed of this study show that hybrid renewable resources have higher ratios of energy used for self-consumption to the total energy generated compared to stand-alone wind and PV farms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Buildings Sector Scenarios: Demand-side data to support energy system planning in the United States

The US energy system is in a period of high uncertainty about load growth, its implications for the energy generation mix, and downstream impacts on customer energy costs. In this context, there is a need for comprehensive, credible, and readily-customized projections of energy demand to ensure that planning decisions account for end-use management opportunities to improve system reliability and affordability. Here we introduce the Buildings Sector Scenarios (BSS) dataset, which includes a benchmark suite of such projections for the buildings sector — a key source of energy consumption, peak electricity demand, and consumer energy expenditures. The dataset contains projections through 2050 covering the contiguous United States (CONUS) resolved down to the county, hourly level by sector and end use for electricity demand and to the state, annual level by sector and end use for non-electric fuels. We summarize the BSS analysis workflow and the tools and datasets that support it, document key BSS scenario inputs and modeling assumptions, and outline BSS scenario outputs. We assess the technical quality of the dataset against historical surveys and projected estimates of buildings sector demand. Finally, we provide guidance on how stakeholders can access, use, and reproduce the dataset, and/or create new scenarios to explore their own analysis questions.

Langevin, Jared↗
DOE OSTI2020

Future material demand for automotive lithium-based batteries

The world is shifting to electric vehicles to mitigate climate change. Here, we quantify the future demand for key battery materials, considering potential electric vehicle fleet and battery chemistry developments as well as second-use and recycling of electric vehicle batteries. We find that in a lithium nickel cobalt manganese oxide dominated battery scenario, demand is estimated to increase by factors of 18–20 for lithium, 17–19 for cobalt, 28–31 for nickel, and 15–20 for most other materials from 2020 to 2050, requiring a drastic expansion of lithium, cobalt, and nickel supply chains and likely additional resource discovery. However, uncertainties are large. Key factors are the development of the electric vehicles fleet and battery capacity requirements per vehicle. If other battery chemistries were used at large scale, e.g. lithium iron phosphate or novel lithium-sulphur or lithium-air batteries, the demand for cobalt and nickel would be substantially smaller. Closed-loop recycling plays a minor, but increasingly important role for reducing primary material demand until 2050, however, advances in recycling are necessary to economically recover battery-grade materials from end-of-life batteries. Second-use of electric vehicles batteries further delays recycling potentials.

25 ENERGY STORAGE↗
DOE OSTI2023

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Abstract Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Towards Hybrid Online On-Demand Querying of Realtime Data with Stateful Complex Event Processing

Emerging Big Data applications in areas like e-commerce and energy industry require both online and on-demand queries to be performed over vast and fast data arriving as streams. These present novel challenges to Big Data management systems. Complex Event Processing (CEP) is recognized as a high performance online query scheme which in particular deals with the velocity aspect of the 3-V’s of Big Data. However, traditional CEP systems do not consider data variety and lack the capability to embed ad hoc queries over the volume of data streams. In this paper, we propose H2O, a stateful complex event processing framework, to support hybrid online and on-demand queries over realtime data. We propose a semantically enriched event and query model to address data variety. A formal query algebra is developed to precisely capture the stateful and containment semantics of online and on-demand queries. We describe techniques to achieve the interactive query processing over realtime data featured by efficient online querying, dynamic stream data persistence and on-demand access. The system architecture is presented and the current implementation status reported.

big data↗
DOE OSTI2012

Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response

Demand Response is playing an increasingly important role in smart grid control strategies. Modeling the behavior of populations of appliances under demand response is especially important to evaluate the effectiveness of these demand response programs. In this paper, an aggregated model is proposed for a class of Thermostatically Controlled Loads (TCLs). The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. However, an accurate characterization of the collective dynamics however requires the aggregate model to have a high state space dimension. Most of the existing model reduction techniques require the stability of the underlying system which does not hold for the proposed aggregated model. In this work, a novel model reduction approach is developed for the proposed aggregated model, which can significantly reduce its complexity with small performance loss. The original and the reducedorder aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D, which is a realistic open source distribution simulation software. Index Terms – demand response, aggregated model, ancillary

Zhang, Wei↗
DOE OSTI2019

The Social Cost of Individual Privacy in Aggregated Residential Demand Response

There is an increasing body of experimental and theoretical research related to demand response using smart, connected home equipment in smart grid. However, as technologies are deployed at scale, privacy has become a major concern. One possible approach to address this concern is to model an entire home as an aggregated unit of resource for engaging in demand response. Such an approach would allow residents to participate in demand response while abstracting the specifics of the appliance usage pattern from the utility provider. The benefits of privacy-preserving demand response notwithstanding, it implies a cost in terms of wasted capacity. This paper aims to explore the privacy/capacity tradeoff by simulating a fleet of homes and comparing the results with a fleet of individual appliances. The results show that a fleet of homes only bids about 70% of available capacity, and in the presence of an aggregator this number declines to 50%. Thus, privacy of individual homes comes at a cost of sacrificing part of otherwise available capacity.

Tsybina, Eve↗
DOE OSTI2012

Aggregate Model for Heterogeneous Thermostatically Controlled Loads with Demand Response

Due to the potentially large number of Distributed Energy Resources (DERs) – demand response, distributed generation, distributed storage - that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. Being able to accurately estimate the fast transients caused by demand response is especially important to analyze the stability of the system under different demand response strategies. On the other hand, a less complex model is more amenable to design feedback control strategies for the population of devices to provide ancillary services. The main contribution of this paper is to develop aggregated models for a heterogeneous population of Thermostatic Controlled Loads (TCLs) to accurately capture their collective behavior under demand response and other time varying effects of the system. The aggregated model efficiently includes statistical information of the population and accounts for a second order effect necessary to accurately capture the collective dynamic behavior. The developed aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D (an open source distribution simulation software) under both steady state and severe dynamic conditions caused due to temperature set point changes.

Zhang, Wei↗
DOE OSTI2018

Geospatial Framework for Estimating Household Electricity Demand for Urban Infrastructure Planning in Select African Countries

Power infrastructure planning in Sub-Saharan Africa requires accurate power demand estimates. However, these estimates are lacking in most countries which compounds the effects of unreliable electricity supply. This study utilizes a methodology that was used to forecast power demand in Uganda and extends it to other African countries of interest; Kenya, Nigeria, and South Africa. The goal of the study is to create spatial power demand maps that can be used for power infrastructure planning. The proposed approach utilizes a global population database at a fine resolution of 1 km2 as the foundational input data. The analysis indicates a deficit of about 22,630 MW (South Africa), 20,213 MW (Nigeria), 4,153 MW (Kenya), and 2,508 MW (Uganda) compared to the current installed capacity to meet the highest power demand scenario.

Kotikot, Susan M.↗
DOE OSTI2020

Selling Demand Response Using Options

Wholesale electricity markets in many jurisdictions use a two-settlement structure: a day-ahead market for bulk power transactions and a real-time market for fine-grain supply-demand balancing. This paper explores trading demand response assets within this two-settlement market structure. Here, we consider two approaches for trading demand response assets: (a) an intermediate spot market with contingent pricing, and (b) an over-the-counter options contract. In the first case, we characterize the competitive equilibrium of the spot market, and show that it is socially optimal. Economic orthodoxy advocates spot markets, but these require expensive infrastructure and regulatory blessing. In the second case, we characterize competitive equilibria and compare its efficiency with the idealized spot market. Options contract are private bilateral over-the-counter transactions and do not require regulatory approval. We show that the optimal social welfare is, in general, not supported. We then design optimal option prices that minimize the social welfare gap. This optimal design serves to approximate the ideal spot market for demand response using options with modest loss of efficiency. Our results are validated through numerical simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Integrating Demand Response and Distributed Resources in Planning for Large-scale Renewable Energy Integration

The electricity grid is transitioning from a centralized and uncoordinated set of large generators and loads to a framework that also includes decentralized and coordinated "distributed energy resources" (DER). Advances in renewable generation, energy storage, efficiency, and controls technology present a significant opportunity for demand-side investment that is matched to the needs of the future grid infrastructure and operations, but the complex interplay of controls technology and grid operations makes estimating and realizing the potential of DER a significant challenge. Supply curves for conserved energy have long been used to synthesize energy efficiency opportunities for electricity system planners and show how demand side resources compete with building new power plants. We have developed a similar approach for supporting policymakers who now face a range of technology options for DER, with a focus on describing the potential for demand response (DR) to provide flexibility to the grid. We describe our modeling approach using supply curves for demand response across four key dimensions: reshaping with rates, shedding at critical time, shifting to capture renewables, and fast-response "shimmy" to balance the grid. In a California-focused study, we find a significant potential for DR to support the grid, and a need for integration between DR and energy efficiency. The combined efficiency benefits from a better-controlled and commissioned facility can lead to significant reductions in the cost of DR, increasing the quantity that is cost competitive by 5-200%. The benefit stream from DR can alternatively be framed to "buy down" the cost of EE investment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Characterizing electrical demand and load diversity of low-power water and space heating appliances in US homes

Home renovation and remodeling projects can involve costly and time consuming electrical infrastructure upgrades at the household level. From the grid perspective they also lead to costly replacement of local infrastructure, such as transformers, and can add stress to the grid at peak times. The emergence of innovative, power-efficient household appliances offers a way to minimize these problems. These appliances are designed for lower power consumption, simplifying installation through standard plug-in connections, reducing the need for new electric circuits/panels/service, and minimizing the peak power demand for the home. Key examples include low-power heat pump water heaters (HPWHs) and cold climate window heat pumps that operate on standard 120V outlets. To assess the real-world impact of these solutions, we compiled and analyzed power metering data from several US field studies. This data provides insights into the effects on peak power demand of selecting lower-power appliances. Our analysis focuses on several key metrics, including the maximum power demand of individual appliances, their operational runtime, continuous operation and load diversity. While individual low-power 120V space and water heating appliances offer significant peak demand reductions compared to 240V heat pump or resistance alternatives, their longer runtimes might increase the likelihood of operation during whole-dwelling peak events, albeit at lower power levels.

Less, Brennan↗