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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physically Realistic Estimates of Electric Water Heater Demand Response Resource

Multiple grid integration studies have examined the value of demand response, including from water heaters, to bulk power systems. Common shortcomings of these studies include physically unrealistic assumptions. For example, studies often assume that all electric water heater load can be shed for up to an hour to provide capacity or contingency service, that all electric water heater load is shiftable, and that round-trip efficiencies for shifting service are 100%. Another issue is that simply summing up estimates of individual water heaters' flexibility bounds, an attractive idea for constructing the MW-scale resources necessary for direct inclusion in grid investment and operational models, can result in significant overestimates of actual resource, because, e.g., a water heater with the ability to reduce load may not be able sustain the reduction for as long as the aggregate model suggests is possible. This presentation demonstrates the impact of estimating the flexibility of electric water heaters with physically realistic models suitable for grid service analysis. Electric water heater flexibility representations are constructed based on ResStock simulations of New England. Specifically, we examine contingency resource (related to capacity, shed, and contingency reserve services) and energy shifting resource from electric resistance and heat pump water heaters. Different estimation methods are compared; and the surrogate models' event responses are validated against events directly simulated in EnergyPlus. We also demonstrate the impact of using electric water heaters for contingency reserves and energy shifting in detailed models of possible future ISO New England power systems.

building energy modeling↗
DOE OSTI2020

Optimal charging scheduling and management for a fast-charging battery electric bus system

Herein we discuss how battery electric buses (BEBs) are rapidly being embraced by public transit agencies because of their environmental and economic benefits. To address the problems of limited driving range and time-consuming charging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to charge buses in operation using high power. With on-route fast-charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, on-route fast-charging makes it more challenging to schedule and manage charging events for a BEB system. First, on-route fast-charging may lead to high electricity power demand charges. Second, it may increase electricity energy charges because of charging that occurs during on-peak hours. Without careful charging scheduling and management, on-route fast-charging may significantly increase fuel costs and reduce the economic attractiveness of BEBs. The present study proposes a network modeling framework to optimize the charging scheduling and management for a fast-charging BEB system, effectively minimizing total charging costs. The charging schedule determines when to charge a BEB, while the charging management strategically controls the actual charging power. Charging costs include both electricity demand charges and energy charges. The charging scheduling and management problem is first formulated as a nonlinear nonconvex program with time-continuous variables. A discretizing method and a linear reformulation technique are then adopted to reformulate the model as a linear program, which can be easily solved using off-the-shelf solvers, even for large-scale problems. Finally, the model is demonstrated with extensive numerical studies based on two real-world bus networks. The results demonstrate that the proposed model can effectively determine the optimal charging scheduling and management for a fast-charging BEB system, which carries the potential for use in large-scale real-world bus networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

An Agent-Based Modeling Approach for Spatiotemporal Optimization of Electric Vehicle Fast-Charging Station Demand

With increasing electric vehicle (EV) adoption, managing public fast-charging demand effectively is crucial to avoid grid strain. This study investigates the potential of using dynamic pricing schemes to address this challenge. Presented in this study is a scalable agent-based simulation framework, which is applied to a case study in Richmond, Virginia, that assumes a 50% EV adoption rate in 2040. Two pricing schemes are compared: (1) a dynamic-pricing scheme based on station utilization and (2) a dynamic-pricing scheme based on peak power at the station. These schemes are compared to two baseline scenarios: (1) unscheduled first-come, first-served and (2) scheduled with constant price. The study’s results suggest that dynamic pricing has the potential to influence EV charging behavior, inducing both spatial and temporal shifts, but does so at the cost of inducing inconvenience to EV drivers. The results suggest the peak-power dynamic pricing scheme has the potential to mitigate peak demand pressures on the grid with minimal inconvenience, offering a promising approach for sustainable EV charging infrastructure expansion.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

The attractive features of natural gas as well as the growing electric power demand worldwide have created increasing interest in natural-gas-based distributed generation applications for electric distribution networks. Here, this paper investigates the interdependency between a residential natural gas network and an electric distribution network that are linked together via fuel cells. The modeling of the natural gas network is introduced first, and then the algorithm for gas flow study is presented. The optimal placement and sizing of fuel cell based distributed generation systems are formulated to minimize the losses in both the natural gas network and the electric distribution grid, subject to the constraints imposed by both networks. In addition, a probabilistic model for both gas and electricity demands is developed based on historical electricity and natural gas demand data. A K-means clustering method is used to determine the hourly load states to solve the joint probabilistic optimization problem. Simulation studies are carried out on an integrated system consisting of the IEEE 69-bus distribution network and a radial 27-node natural gas network to verify the developed optimization model and the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Impact of electric vehicle charging on the power demand of retail buildings

As electric vehicle penetration increases, charging is expected to have a significant impact on the grid. Electric vehicle charging stations will greatly affect a building site's power demand, especially with the onset of fast charging with power levels as high as 350 kW per charger. Here, we assess how electric vehicle charging stations would impact a retail big box grocery store, exploring numerous station sizes, charging power levels, and utilization factors in various climate zones and seasons. We measure the effect of charging by assessing changes in monthly peak power demand, electricity usage, and annual electricity bill, computed using three distinct rate structures. We find that an electric vehicle station has the potential to dwarf a big box building's power demand if behind the same meter, increasing monthly peak power demand at the site by over 250%. Cold-climate areas paired with rate structures incorporating high demand charges are most susceptible for significant changes to the annual electricity bill, with increases as high as 88%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Comparison of the Environmental Effects of Open-Loop and Closed-Loop Pumped Storage Hydropower

Pumped storage hydropower (PSH) is a type of energy storage that uses the pumping and release of water between two reservoirs at different elevations to store water and generate electricity (Figure ES-1). When demand for electricity is low, a PSH project can use low cost energy to pump water from the lower reservoir to the upper reservoir for storage. When demand for electricity is high, a PSH project can release water from the upper reservoir through a powerhouse to generate electricity. Traditionally, this meant that PSH plants generated power during the day and pumped at night, with modest diurnal or seasonal variation.

13 HYDRO ENERGY↗
NASA NTRS1980

Regional price targets appropriate for advanced coal extraction

A methodology is presented for predicting coal prices in regional markets for the target time frames 1985 and 2000 that could subsequently be used to guide the development of an advanced coal extraction system. The model constructed is a supply and demand model that focuses on underground mining since the advanced technology is expected to be developed for these reserves by the target years. Coal reserve data and the cost of operating a mine are used to obtain the minimum acceptable selling price that would induce the producer to bring the mine into production. Based on this information, market supply curves can be generated. Demand by region is calculated based on an EEA methodology that emphasizes demand by electric utilities and demand by industry. The demand and supply curves are then used to obtain the price targets. The results show a growth in the size of the markets for compliance and low sulphur coal regions. A significant rise in the real price of coal is not expected even by the year 2000. The model predicts heavy reliance on mines with thick seams, larger block size and deep overburden.

Terasawa, K. L.↗
DOE OSTI2023

Variable renewable energy deployment in low-emission scenarios: The role of technology cost and value

While rapid deployment of variable renewable energy (VRE) technologies, namely wind and solar PV, is often projected in 2C pathways generated by integrated assessment models, there is a wide range in projected VRE deployment by mid-century. Such differences could be the result of differences in assumptions about future technology costs and/or differences in model approaches for capturing other aspects of technology competitiveness. Here we introduce a consistent competitiveness metric, profitability-adjusted levelized cost of electricity (or PLCOE), to an integrated assessment model (EPPA) to evaluate the representation of technology competition, including VRE, in low-emission scenarios. We show that representing the value of technology (alongside cost) may significantly impact VRE deployment relative to scenarios without such an adjustment. In addition, we show that varying VRE costs by about 35% in 2050 results in differences in VRE deployment that span much of the range in outcomes (over the same period) observed in likely 2C scenarios assessed by the IPCC, suggesting that both cost and value are key drivers of VRE deployment in such scenarios. Given the central role that VRE technologies play in the electricity mix across most scenarios, we also find that alternative cost assumptions for VRE technologies can lead to changes in electricity prices, the associated demand for electricity, and total final and primary energy consumption. However, the demand for fuels other than electricity is relatively insensitive to VRE assumptions in the 2C scenarios considered here.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Towards a Robust Sampling Approach: A Computational Review and Design

As pointed out in several other works, the estimation of the reliability of the electrical grid can not be conducted without the estimation of the stochastic phenomena of electicity demand and electricity production by variable renewable sources. Therefore, sampling procedures have become integral in the design of engineering structures and analysis. Commonly referred to as Monte Carlo uncertainty analysis or integration, the general objective of these procedures is to establish specifics about the uncertainty of an output characteristic of such an engineering system, given uncertainty about its input characteristics. These sampling procedures are applied in a context in which establishing such specifics cannot be performed through other means. Variance-reduction techniques are designed to lessen the variability among estimators to estimate statistics of those output uncertainties. Importance-sampling techniques, on the other hand, are designed to reduce the number of samples needed to estimate a particular statistic—e.g., a tail probability. The combination of these approaches can reduce the computational burden considerably for a particular estimator and statistic. Importance sampling—geared and designed as it is toward improving a particular estimator—suffers, unfortunately, from the unintended consequence of reducing the performance of other estimators in terms of their variance. The objective of this paper is to offer an alternative sampling procedure where this variance does not grow unacceptably large for a suite of estimators. Moreover, it is anticipated that, with additional knowledge of how an engineering output characteristic responds to its input characteristics, tuning parameters of the input’s sampling procedure can be set to improve the output characteristic’s estimation. This work proves the effectiveness of the suggested alternative approach. Such positive outcome will lead to a decrease of the computational burden of performing stochastic optimization of integrated energy systems (e.g., components dispatch, and portfolio composition). In particular, capturing the contribution to the overall system cost of rare and unlikely events and patterns of the electricity demand and production will become less computationally expensive. This is due to the fact that the approach demonstrated here will allow the sampling of those rare occurrences more frequently without misrepresenting their probabilistic impacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

The interesting properties of natural gas as well as the growing electric power demand worldwide have led to increasing attention to natural-gas-based distributed generation applications in electric distribution systems. This paper goes over the interdependency between a residential natural gas network and an electric distribution network that are coupled via fuel cells. The modeling of the gas network is introduced first, and then the algorithm for gas flow study is presented. The optimal placement and sizing of fuel cell based distributed generation systems are formulated to minimize the losses in both the gas and electric distribution networks, subject to their model constraints. In addition to this, in order to capture the probabilistic nature of the optimization problem under study, the K-means clustering algorithm is applied to the gas and electricity demands to determine hourly load states and their corresponding probabilities. Furthermore, simulation studies are carried out on an integrated system consisting of the IEEE 69-bus distribution feeder and a radial 27-node natural gas network to verify the developed optimization model and the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2024

Emerging magnetic materials for electric vehicle drive motors

Abstract Increasing demand for electric vehicles (EVs) is increasing demand for the permanent magnets that drive their motors, as approximately 80% of modern EV drivetrains rely on high-performance permanent magnets to convert electricity into torque. In turn, these high-performance permanent magnets rely on rare earth elements for their magnetic properties. These elements are “critical” (i.e., at risk of limiting the growth of renewable energy technologies such as EVs), which motivates an exploration for alternative materials. In this article, we overview the relevant fundamentals of permanent magnets, describe commercialized and emerging materials, and add perspective on future areas of research. Currently, the leading magnetic material for EV motors is Nd 2 Fe 14 B, with samarium-cobalt compounds (SmCo 5 and Sm 2 Co 17 ) providing the only high-performing commercialized alternative. Emerging materials that address criticality concerns include Sm 2 Fe 17 N 3 , Fe 16 N 2 , and the L1 0 structure of FeNi, which use lower cost elements that produce similar magnetic properties. However, these temperature-sensitive materials are incompatible with current metallurgical processing techniques. We provide perspective on how advances in low-temperature synthesis and processing science could unlock new classes of high-performing magnetic materials for a paradigm shift beyond rare earth-based magnets. In doing so, we explore the question: What magnetic materials will drive future EVs? Graphical abstract

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Emerging magnetic materials for electric vehicle drive motors [Slides]

Increasing demand for electric vehicles (EVs) is increasing demand for the permanent magnets that drive their motors, as approximately 80% of modern EV drivetrains rely on high-performance permanent magnets to convert electricity into torque. In turn, these high-performance permanent magnets rely on rare earth elements for their magnetic properties. These elements are "critical" (i.e., at risk of limiting the growth of renewable energy technologies such as EVs), which motivates an exploration for alternative materials. In this article, we overview the relevant fundamentals of permanent magnets, describe commercialized and emerging materials, and add perspective on future areas of research. Currently, the leading magnetic material for EV motors is Nd 2 Fe 14 B, with samarium-cobalt compounds (SmCo 5 and Sm 2 Co 17 ) providing the only high-performing commercialized alternative. Emerging materials that address criticality concerns include Sm 2 Fe 17 N 3 , Fe 16 N 2 , and the L10 structure of FeNi, which use lower cost elements that produce similar magnetic properties. However, these temperature-sensitive materials are incompatible with current metallurgical processing techniques. We provide perspective on how advances in low-temperature synthesis and processing science could unlock new classes of high-performing magnetic materials for a paradigm shift beyond rare earth-based magnets. In doing so, we explore the question: What magnetic materials will drive future EVs?

42 ENGINEERING↗
DOE OSTI2022

tell: a Python package to model future total electricity loads in the United States

The purpose of the Total ELectricity Load (tell) model is to generate 21st century profiles of hourly electricity load (demand) across the Conterminous United States (CONUS). tell loads reflect the impact of climate and socioeconomic change at a spatial and temporal resolution adequate for input to an electricity grid operations model. tell uses machine learning to develop profiles that are driven by projections of climate/meteorology and population. tell also harmonizes its results with United States (U.S.) state-level, annual projections from a national- to global-scale energy-economy model. This model accounts for a wide range of other factors affecting electricity demand, including technology change in the building sector, energy prices, and demand elasticities, which stems from model coupling with the U.S. version of the Global Change Analysis Model (GCAM-USA). tell was developed as part of the Integrated Multisector Multiscale Modeling (IM3) project. IM3 explores the vulnerability and resilience of interacting energy, water, land, and urban systems in response to compound stressors, such as climate trends, extreme events, population, urbanization, energy system transitions, and technology change

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Opportunities for Integrating Electric and Gas Planning

Growing demand for electricity in buildings is creating new opportunities and challenges for electric and natural gas planning. To date, electric and gas planning processes have largely been siloed, and only a few jurisdictions have started to examine how these two processes could be linked together. The report explores the current state of electric and gas system planning, and the benefits and challenges of integrating these planning processes. We frame the characteristics of an integrated electric and gas distribution planning process and identify indicators of progress toward the characteristics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

U.S. lithium resources from geothermal and extraction feasibility

As the demand for electric vehicles (EVs) increases, demand for lithium (Li) is expected to grow over the coming decade. To satisfy the demand for rechargeable batteries, it is imperative to devise solutions for sufficient Li production. Additionally, even though commercial Li currently comes from conventional saline salar brine and spodumene ore extractions, efforts to retrieve Li from geothermal brines are increasing in popularity. In this paper, we evaluate the United States’ potential to supply Li from geothermal brines using Systems Dynamics modeling. The main goals are to assess (1) the viability of US Li supply from geothermal brines and (2) the potential supply chain impact of extracting Li from this source. Results show that Li supply from geothermal brines could (1) reach around 4% to 8% of total US Li supply and (2) be economically viable.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

The energy-saving potential of occupancy-centric smart thermostats has been extensively explored in simulations but lacked field testing for energy savings quantification and sensor performance assessment in real buildings. This paper presents a long-term field study conducted in a single-family home in Texas, U.S. to evaluate the performance of occupancy-centric controls (OCC) of HVAC (heating, ventilation, and air-conditioning) system in terms of energy savings, sensor accuracy, and impact on electric peak demand. The test site was equipped with a commercial off-the-shelf (COTS) smart thermostat and multiple occupancy presence sensors for OCC implementation. Additionally, a sub-metering system was installed to monitor electricity consumption of various end-use equipment, including the HVAC system. A supplementary device was installed to track the ground-truth occupancy for the accuracy evaluation of the occupancy presence sensor. Scenarios of baseline and OCC controls were alternated weekly over the 20-month testing period. The results indicated an effective OCC execution, as evidenced by indoor temperature profiles. During the 2023 cooling season, OCC achieved total energy savings of 1,958 kWh, corresponding to a 17.6% energy savings ratio. Under certain conditions, daily HVAC energy savings reached as high as 17 kWh, with a savings ratio of 35%. Sensor performance showed an overall accuracy of 83.8%, a False Positive Rate (FPR) of 12.8%, and a False Negative Rate (FNR) of 47.4%. A key limitation was the sensor’s inability to detect stationary occupants during sleep, leading to a midnight FNR of nearly 100% and significantly compromising thermal comfort. Additionally, the implementation of OCC resulted in extended periods of high electricity demand on summer afternoons, affecting occupant’s thermal comfort and posing potential challenges to community-level grid operations if OCC were widely adopted. Furthermore, this study addresses a critical research gap by empirically investigating energy-saving potential and occupancy sensor performance in residential buildings. Through a comprehensive field-testing study, the research examines the interrelationship between sensor accuracy, energy savings, and thermal comfort, an area that has received limited attention in the current literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Communications Reliability for Vehicle Grid Integration

Electric Vehicles (EVs) adoption rate has been steadily increasing in the US leading to a growing number of charging stations including faster DC (Direct Current) chargers and slower Level 1 and Level 2 AC (Alternating Current) chargers. This increase in demand for electricity is further exacerbated by recent developments in Artificial Intelligence (AI) technology, advanced manufacturing, and digitization. These factors will require electric utilities to upgrade their infrastructure to keep up with the increasing electrical demand (especially during peak hours). An easy way to counteract the need for these upgrades is to shift a major chunk of active charge sessions (durations where there is energy transfer from charger to EV's propulsion battery) to off-peak hours thereby flattening the load curve and making the infrastructure more resilient. This concept is known as Smart Charge Management (SCM). EV owners also benefit from SCM since it lowers their charging costs and consequently their transportation costs by prioritizing charging during off-peak hours. SCM takes advantage of EV's capability to act as a controllable load or DER (Distributed Energy Resource). This report summarizes the reliability analysis performed on the communication required for two of these SCM use-cases. This analysis only focuses on SCM strategies for unidirectional charging (energy transfer from EVSE to EV or V1G) and not bidirectional charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗