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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 613 records · Page 34

All sources NASA NTRS DOE OSTI

DOE OSTI2015

Evaluating the Relationship between the Population Trends, Prices, Heat Waves, and the Demands of Energy Consumption in Cities

The demands of energy consumption have been projected as a key factor that affects an economy at the city, national, and international level. Contributions to total U.S. greenhouse gas emissions in 2012 by various urban sectors include electricity (31%), transportation (28%), industry (20%), agriculture (10%), and commercial and residential (10%). Moreover, the heavy demands of energy consumption in the cities by residents, commercial businesses, industries, and transportation are important for maintaining and sustaining sufficient economic growth. The purpose of this study is to investigate the relationships between population trends, historical energy consumptions, the changes of average electricity price, average annual temperature, and extreme weather events for three selected cities: New York, Chicago, and Los Angeles. These cities are exemplary of, metropolitan areas in the East, Middle, and the Western regions of the U.S. Here, we find that the total energy consumptions of New York, Chicago, and Los Angeles are influenced to various degrees by changes in population, temperature and the average price of electricity and that only one city, Los Angeles, does price significantly affect electricity use. Our finding has implications for policy making, suggesting that each city s climate, size and general economic priorities must be considered in developing climate change mitigation strategies and incentives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Immediate drop on demand technology (I-DOT) coupled with mass spectrometry via an open port sampling interface

Here, the immediate drop on demand technology to a mass spectrometer via the recently introduced open port sampling interface and ESI. A maximum sample analysis throughput of 5 s per sample was demonstrated. Signal reproducibility was 10% or better as demonstrated by the quantitative analysis of propranolol and its stable isotope-labeled internal standard propranolol-d7. The ability of the system to multiply charge and analyze macromolecules was demonstrated using the protein cytochrome c. In conclusion, this immediate drop on demand technology/open port sampling interface/ESI–MS combination allowed for the quantitative analysis of relatively small mass analytes and was used for the identification of macromolecules like proteins.

47 OTHER INSTRUMENTATION↗

NASA NTRS2016

NY TBO Research: Integrated Demand Management (IDM): IDM Concept, Tools, and Training Package

A series of human-in-the-loop simulation sessions were conducted in the Airspace Operations Laboratory (AOL) to evaluate a new traffic management concept called Integrated Demand Management (IDM). The simulation explored how to address chronic equity, throughput and delay issues associated with New Yorks high-volume airports by operationally integrating three current and NextGen capabilities the Collaborative Trajectory Options Program (CTOP), Time-Based Flow Management (TBFM) and Required Time of Arrival (RTA) in order to better manage traffic demand within the National Air Traffic System. A package of presentation slides was developed to describe the concept, tools, and training materials used in the simulation sessions. The package will be used to outbrief our stakeholders by both presenting orally and disseminating of the materials via email.

trajectory-based operations↗

DOE OSTI2011

Travel Demand Modeling

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming, and agent-based microsimulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Role of Storage and Demand Response, Greening the Grid

Greening the Grid provides technical assistance to energy system planners, regulators, and grid operators to overcome challenges associated with integrating variable renewable energy into the grid. This document, part of a Greening the Grid toolkit, examines storage and demand response as means to match renewable energy supply with demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

Field Testing and Modeling of Supermarket Refrigeration Systems as a Demand Response Resource

Supermarkets offer a substantial demand response (DR) resource because of their high energy intensity and use patterns; however, refrigeration as the largest load has been challenging to access. Previous work has analyzed supermarket DR using heating, ventilating, and air conditioning; lighting; and anti-sweat heaters. This project evaluated and quantified the DR potential inherent in supermarket refrigeration systems in the Bonneville Power Administration service territory. DR events were carried out and results measured in an operational 45,590-ft2 supermarket located in Hillsboro, Oregon. Key results from the project include the rate of temperature increase in freezer reach-in cases and walk-ins when refrigeration is suspended, the load shed amount for DR tests, and the development of calibrated models to quantify available DR resources. Simulations showed that demand savings of 15 to 20 kilowatts (kW) are available for 1.5 hours for a typical store without precooling and for about 2.5 hours with precooling using only the low-temperature, non-ice cream cases. This represents an aggregated potential of 20 megawatts within BPA's service territory. Inability to shed loads for medium-temperature (MT) products because of the tighter temperature requirements is a significant barrier to realizing larger DR for supermarkets. Store owners are reluctant to allow MT case set point changes, and laboratory tests of MT case DR strategies are needed so that owners become comfortable testing, and implementing, MT case DR. The next-largest barrier is the lack of proper controls in most supermarket displays over ancillary equipment, such as anti-sweat heaters, lights, and fans.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

EV Charging and the Impacts of Electricity Demand Charges

These slides were presented at the Los Angeles Cleantech Incubator's (LACI's) April 2022 combined Light-Duty and Goods Movement Working Group meetings. The talk summarized recent and ongoing research at NREL to model EV charging demands for passenger and commercial M/HD vehicles and estimate the impacts of utility demand charges on charging costs.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2021

Detailed Evaluation of Electric Demand Load Shifting Potential of Heat Pump Water Heaters

Future utilities will emphasize home appliances to reduce greenhouse gas emissions while providing electric load shifting and demand profile management. Heat pump water heaters (HPWH) have demonstrated the ability to cut water heating energy by more than 65% compared with conventional electric resistance waters (ERWH). Laboratory research was conducted on the load shifting potential of grid-connected heat pump water heaters (HPWH), compared to ERWHs. Testing applied different CTA-2045 command designs. Highly-controlled laboratory experiments were conducted on one ERWH and four HPWHs, including a prototype incorporating the new CTA-2045-B protocol feature allowing ‘advanced’ load up above the tank setpoint. The prototype unit with the B-protocol increased tank temperature by 15oF (8.3C) under the advanced load up providing an increase of 1.8 kWh of storage for a 50-gallon (189 liter) tank. The prototype includes a built-in mixing valve to meet anti-scalding codes. With the load-shaping ability of CTA-2045-B, utilities might be able to store excess renewable energy in connected tanks when renewable wind and solar resource production is high. Tests were performed under baseline conditions (no load shift) and under several load-shifting schemes, including load up and advanced load up, ahead of shed commands. Grid-connected HPWHs reduced peak demand by as much as 0.5 kW, depending on tank volume, time of day, control scheme, and draw profile.

heat pump water heater, Demand flexibility, load s↗

DOE OSTI2018

Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations

While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid–centric mixed–integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. Furthermore, the linearized model often underestimates the amount of DR needed in our case studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗

DOE OSTI2025

Capacitated p -hub approach for park-and-ride facility location problem under nested logit demand function: polyhedral approaches

By generalizing the unconstrained p-hub approach for the park-and-ride (P&R) facility location problem under the multinomial logit demand function, the capacitated p-hub approach for the problem under the nested logit demand function captures a broader range of real-world cases. To solve this problem optimally, we introduce a mixed-integer linear program and accelerate its solution by enhancing the branch-and-cut procedure. To address the problem at a large scale, we introduce two other polyhedral approaches: variable neighborhood search (VNS) and adaptive randomized rounding (ARR). Downtown areas in Seoul have a high modal share of public transportation and congested road traffic, yet P&R has not been widely implemented. Therefore, we apply the ARR procedure to solve a real-world problem using traffic and geographic data from the Seoul metropolitan area. ARR performs better than VNS and addresses real-world cases. The solutions obtained by ARR present a phased expansion plan that encourages policymakers to start installing a small number of P&Rs immediately.

Capacitated p-hub approach↗

DOE OSTI2020

Optimal electric-distribution-grid planning considering the demand-side flexibility of thermal building systems for a test case in Singapore

The planning of district-scale electric grids, i.e., distribution grids, has traditionally relied on finding the most cost-effective design such that they are able to supply the peak loads in a district. With the advent of electric demand side flexibility (DSF), there is the opportunity to reshape peak loads such that the investment cost of the electric grid decreases in exchange for a minor increase in the operation cost. This paper formulates an optimal planning approach for the electric grid at the district scale, which incorporates the DSF from thermal building systems, e.g., heating ventilation and air-conditioning (HVAC) systems. The problem is formulated as a mixed-integer linear program (MILP) and aims at minimizing the investment cost for the grid along with the operation cost of the flexible loads. This is subjected to the fixed electricity demand and thermal comfort constraints of building occupants. To this end, linear models for the thermal comfort in the buildings and the power flow in electric grid are considered. The approach is tested on a district planning test case based in Singapore, where the results show up to 30.9 % reductions in investment cost and up to 3.7 % reduction in total annualized cost. Urban planning authorities, developers and utility companies can all benefit from the presented approach to make optimized investment decisions. For building operators, the results point to the need of adopting their control systems for DSF.

Troitzsch, Sebastian↗

DOE OSTI2026

Bipolar Membrane Electrodialyzers as Flexible Demand Response Resources: Co-Optimization of Cost Savings and Product Formation

Bipolar membrane electro dialyzers (BPMED) are widely used for chemical production and processing, including in the emerging ocean alkalinity enhancement (OAE) industry. In this paper, we explore the potential of BPMED devices as flexible electrochemical loads within power system operations. Using a multi-objective optimization framework, we evaluate BPMED operation across 24-hour and monthly horizons to examine how dispatch strategies respond to electricity price and grid conditions. Simulation results show that altering the relative weights of the choices in the objective function strongly shape the operating patterns, with cost-focused strategies that suppress the operation during peak prices. Furthermore, we propose alternative formulations that optimize operations to achieve both cost savings and alignment with periods of lower grid-side carbon intensity (CI), as low grid-side CI is key to maximize OAE efficiency. Additionally, a detailed sensitivity analysis highlights the importance of device properties, where low area-specific resistance (ASR) of membrane and high current efficiency (CE) are observed to jointly unlock cost-effective operation. However, even modest shunt efficiency losses are observed to erode performance and decrease system value. Importantly, the analysis demonstrates that BPMED can serve as a controllable and flexible demand response resource, shifting load to support multiple grid-side objectives, including (but not limited to) renewable integration, alleviate peak demand, and provide co-benefits for system reliability. These findings underscore BPMED’s dual role as a process technology and a grid-supporting asset, pointing to promising pathways for operational optimization of multiple objectives.

Bhattacharya, Saptarshi (ORCID:0000000308902060)↗

DOE OSTI2019

Controlling risk and demand ambiguity in newsvendor models

We use distributionally robust optimization (DRO) to model a general class of newsvendor problems with unknown demand distribution. The goal is to find an order quantity that minimizes the worst-case expected cost among an ambiguity set of distributions. The ambiguity set consists of those distributions that are not far—in the sense of the total variation distance—from a nominal distribution. The maximum distance allowed in the ambiguity set (called level of robustness) places the DRO between the risk-neutral stochastic programming and robust optimization models. An important problem a decision maker faces is how to determine the level of robustness—or, equivalently, how to find an appropriate level of risk-aversion. We answer this question in two ways. Our first approach relates the level of robustness and risk to the regions of demand that are critical (in a precise sense we introduce) to the optimal cost. Our second approach establishes new quantitative relationships between the DRO model and the corresponding risk-neutral and classical robust optimization models. To achieve these goals, we first focus on a single-product setting and derive explicit formulas and properties of the optimal solution as a function of the level of robustness. Then, we demonstrate the practical and managerial relevance of our results by applying our findings to a healthcare problem to reserve operating room time for cardiovascular surgeries. Finally, we extend some of our results to the multi-product setting and illustrate them numerically.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Long-Term Implications of Alternative Light-Duty Vehicle Technologies for Global Greenhouse Gas Emissions and Primary Energy Demands

This study assesses global light-duty vehicle (LDV) transport in the upcoming century, and the implications of vehicle technology advancement and fuel-switching on greenhouse gas emissions and primary energy demands. Five different vehicle technology scenarios are analyzed with and without a CO2 emissions mitigation policy using the GCAM integrated assessment model: a reference internal combustion engine vehicle scenario, an advanced internal combustion engine vehicle scenario, and three alternative fuel vehicle scenarios in which all LDVs are switched to natural gas, electricity, or hydrogen by 2050. The emissions mitigation policy is a global CO2 emissions price pathway that achieves 450 ppmv CO2 at the end of the century with reference vehicle technologies. The scenarios demonstrate considerable emissions mitigation potential from LDV technology: with and without emissions pricing, global CO2 concentrations in 2095 are reduced about 10 ppmv by advanced ICEV technologies and natural gas vehicles, and 25 ppmv by electric or hydrogen vehicles. All technological advances in vehicles are important for reducing the oil demands of LDV transport and their corresponding CO2 emissions. Among advanced and alternative vehicle technologies, electricity- and hydrogen-powered vehicles are especially valuable for reducing whole-system emissions and total primary energy.

Light-duty vehicles, Transportation emissions miti↗

DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Demand-side solutions in the US building sector could achieve deep emissions reductions and avoid over $100 billion in power sector costs

Buildings are energy-intensive and a primary source of US end-use sector carbon emissions. Although building emissions today are 25% below their 2005 peak, far deeper reductions are needed to reach the US 2050 net-zero emissions goal. However, plausible decarbonization pathways that consider both buildings and their interactions with the power grid remain poorly understood. Here, we couple detailed modeling of building energy use and the grid to quantify building decarbonization potential and associated grid impacts. We find up to a 91% reduction in building CO 2 emissions from 2005 levels by 2050 using a portfolio of building efficiency, demand flexibility, and electrification measures alongside rapid grid decarbonization. Building efficiency and flexibility could generate up to $107 billion in annual power system cost savings by 2050, offsetting over a third of the incremental cost of full grid decarbonization. Our results underscore multiple benefits of demand-side solutions for deep decarbonization of US buildings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

How demand for and trade of construction materials affects greenhouse gas emissions

Construction materials generate nearly one-third of global carbon emissions, yet conventional accounting captures only a fraction of this impact. Using EXIOBASE data spanning 25 years, we tracked emissions across construction supply chains for cement, steel, metals, and plastics. While global construction demand nearly tripled, regional patterns diverged significantly. The EU reduced emissions despite increased demand through renewable energy adoption and emissions trading, while China's construction boom—driving most global growth—significantly increased domestic emissions. Manufacturing contributes most to embodied emissions compared to resource extraction and waste treatment. Increased reliance on offshore production undermines domestic emission control strategies, highlighting the need for expanded carbon border adjustment mechanisms. Without policies addressing full supply chain emissions, even aggressive climate initiatives will be compromised by carbon leakage, creating an emissions trajectory incompatible with global climate targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗