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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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PACE AI and LBNL partnered under this CRADA to test and evaluate the PACE5 edge node prototype, an AI/ML edge and cloud-based suite, at FLEXLAB.The objective of the test was to evaluate the PACE5 edge node prototype's ability to perform demand shed and take to dynamic price signals, and to demonstrate advanced fault detection and microgrid monitoring capabilities.
The causal basis for many of the relationships in models used to estimate the indirect effects of U.S. biofuels on global agricultural markets has not been adequately established. This paper addresses this gap by examining causal interactions among corn market variables through which the indirect effects of U.S. corn use for ethanol would be transmitted. Specifically, structural break and causal analyses of U.S. corn supply, uses, trade, and price are performed using quarterly data for marketing years 1986 to 2017. The structural break analysis identifies three breaks in corn use for ethanol that reflect the policy-driven evolution of U.S. corn ethanol production and other market factors. The causality analysis finds that U.S. corn use for ethanol is not a driver of the corn price and net corn exports. Changes in corn supply and domestic corn use are found to be the key factors in accommodating the large increase in corn use for ethanol between 2003 and 2010. These results mean that common assumptions linking U.S. corn ethanol production to large reductions in corn availability and exports, and higher global corn prices merit reconsideration.
This study investigates the operational cost competitiveness of battery electric vehicles (BEVs) in the United States, considering regional climates, energy prices, and driving patterns. By comparing BEVs with plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and the alternative use of BEVs and conventional vehicles (Convs), the analysis incorporates thermal dynamometer tests, real-world vehicle miles traveled (VMT), and state-specific energy prices. Using detailed simulations, the study evaluates energy consumption across varying temperatures and driving distances. The findings reveal that, while BEVs remain cost-effective for short trips in moderate climates, PHEVs are more economical for long-range trips and cold environments, due to the excessive cost of using external direct current fast chargers (DCFCs) and reduced BEV efficiency at low temperatures. HEVs are identified as the most cost-efficient option in regions like New England, characterized by high residential electricity prices. These insights are critical for shaping vehicle electrification strategies, particularly under diverse regional and seasonal conditions, and for advancing policies on alternative energy and fuels.
Improvements in vehicle technology impact the purchase price of a vehicle and its operating cost. In this study, the monetary benefit of a technology improvement includes the potential reduction in vehicle price from using cheaper or smaller components, as well as the discounted value of the fuel cost savings. As technology progresses over time, the value and benefit of improving technology varies as well. In this study, the value of improving a few selected technologies (battery energy density, electric drive efficiency, tire rolling resistance, aerodynamics, light weighting) is studied and the value of the associated cost saving is quantified. The change in saving as a function of time, powertrain selection and vehicle type is also quantified. For example, a 10% reduction in aerodynamic losses is worth $24,222 today but only $8,810 in 2030 in an electric long haul truck. The decrease in value is primarily due to expected battery cost reduction over time. The same loss reduction in a 2030 conventional long haul truck is worth $5,117 and is primarily driven by a reduction in diesel expenditure. For conventional vehicles, the value of technology improvement is likely to increase over time as diesel price is expected to increase over the next decades while for electric vehicles, the value of technology improvement is likely to decrease as battery energy density increases and cost decreases. The study focuses on medium- and heavy-duty applications where cost of driving is of paramount importance. The savings realized from each technology improvement serve as an upper limit for how much can be spent to acquire that improvement in technology.
The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.
To make concentrating solar power (CSP) more cost competitive, rigourous optimizations must be run to improve plant design and operations. However, these optimizaitons rely on time consuming annual simulations that solve an electricity dispatch scheduling problem to maximize plant revenue. To reduce the runtime of annual dispatch simulations of CSP plants, a data clustering approach is utilized. This approach assumes that like days of revenue and electricity generation can be identified using weather and price data. Although weather and price are important factors for electricity production, this work investigates how thermal energy storage (TES) inventory at the beginning of a day, denoted as Si, can be used as a supplemental feature to group like days. A framework for creating and training a deep neural network to predict Si is proposed. This model is validated and assessed using eleven sets of testing data that were not used during training. Then, the data clustering approach is performed three seperate times with features of weather and price along with either Si from the neural network, Si from the full annual simulation, or no Si. Ultimately, the results suggest that using Si as an additional clustering feature improves the data clustering simulation accuracy by 1.4%.
Transmission congestion can cause a divergence between wholesale power prices at the individual pricing nodes where power is generated and the more-liquid trading hubs where that power is often delivered and sold. This nodal price difference is commonly referred to as the “locational basis” (or just “basis”). Because the basis varies over time, it can—if not hedged—unpredictably affect a wind plant’s revenue and/or value, which increases investor risk and potentially slows deployment. We find wind plants typically face a larger and more-negative basis than do thermal generators, and hence are more-negatively impacted by congestion. Moreover, while most thermal generators can effectively hedge basis risk by purchasing conventional fixed-volume financial transmission rights (FTRs), these fixed-volume FTRs do not effectively hedge basis risk for variable wind generation. More-effective hedging mechanisms may be required to support those generators most-impacted by congestion, and to promote continued investment in variable generation resources in congested markets.
The thermal inertia of buildings, along with the flexibility associated with thermostatically controlled loads (TCL) allows heating ventilation and cooling (HVAC) systems to be used for grid demand response (DR). In this work, we consider an HVAC system that serves multiple units in a residential building to meet their space heating requirements. We aim to determine the optimal power flow to each unit that minimizes the power costs incurred by the building's occupants while keeping in consideration their thermal comfort. The DR program is assumed to allow the building temperatures to deviate from the set-points up to a maximum limit. Despite the complex, non-linear structure of the problem, we show how the optimal solutions can be obtained efficiently using quadratic programming. Since HVAC systems can run on either electricity or natural gas, we study the efficacy of the DR regime for both hourly electricity prices and flat gas prices over the course of 24 hours. Finally, we run simulations to determine the optimal thermal power and the evolution of unit temperatures for various energy pricing schemes.
Thermal energy storage (TES) is an increasingly popular tool to level out the daily electrical demand and add stability to the electrical grid as more intermittent renewable energy sources are installed. TES systems can locally decouple high thermal loads from the operation of a heat pump or reduce the electrical energy demand of the heat pump by providing a more favorable temperature gradient. In addition, many policy makers and utility providers have introduced time-of-use (TOU) rate schedules for residential customers to better reflect the price of electricity generation and demand for specific times. TOU rate schedules price grid-provided electricity differently throughout the day depending on the region’s climate, time of year, and electrical production. Large differences between on-peak and off-peak electrical prices may create an economic advantage for a residential customer to install a TES system. In this work, the economic and energy savings are calculated for a modeled 2,400 square foot residential building with water/ice-based TES using a TOU rate structure. The weather data is from Fresno County, CA, ASHRAE climate zone 3B, and a representative residential TOU utility rate structure from Pacific Gas and Electric (PG&E) was used. The results showed that total energy consumption could be reduced by 14.5% with an 87.5% reduction in on-peak energy usage when the TES is installed. The cost of operating this system for space cooling was reduced by nearly 20% using the sample utility rate plan.
Fission Batteries (FBs) are nuclear reactors defined by five characteristics which enable large-scale deployment: cost competitive, standardized sizes for economic mass production, easy installation and removal, secure and safe unattended operation with high reliability. FBs are not defined by technology or power level. Technical and market considerations suggest that most FBs will produce 20 to 30 MWt. This proceedings reports on the outcomes of two workshops that were held in January 2021 to better define markets and economic challenges for FBs. Three major markets were identified. The largest market is the industrial and commercial heat market. There are about 4000 industrial users (excluding utilities) that require more than one megawatt of heat. The number of customers versus size of heat demand was determined. In a low-carbon world there is the potential for many additional customers—including expanded biofuels production and district heat. The second market is for non-grid electricity. This includes co-generation plants that produce heat and electricity for a single customer. The third market is the maritime market with ~100,000 ships worldwide. In the United States, natural gas is the low-cost energy option today and will remain so unless constraints or taxes impact its use. If restrictions on greenhouse gas emissions, the FB competition includes natural gas with carbon capture, biofuels, hydrogen and grid electricity. Natural gas with carbon capture is not economically viable on a small scale. Biofuels may be expensive but may be the economically preferred option for locations with small energy demands of a few megawatts. Hydrogen is a potential competitor with many of the characteristics of natural gas. Grid electricity is not a competitive source of heat. For FBs to be economically competitive, the price of delivered heat must be $20-50/MWh ($6-15/million BTU). The economically competitive range for non-grid electricity is estimated at $70-100/MWh. These electricity prices are competitive with the retail prices of electricity in many parts of the United States for the customer. FBs are not expected to be competitive selling wholesale electricity to the grid. To achieve the aforementioned cost targets for heat and electricity markets, FB designers must (1) maximize the power output within the constraints of a FB (e.g., truck transportability, passive decay heat removal), (2) drastically reduce the size of onsite staff, (3) adopt core designs with low fuel costs (enrichment and fabrication), and (4) develop a system design that is efficiently manufactured in factories. The business case depends upon more than being just a replacement for natural gas. The largest incentives for adoption of FBs is where they create new markets and new sources of revenue. An example is the paper and pulp industry that burns biomass wastes to provide heat and electricity to make paper. An external heat source could meet the demand for heat and electricity by the paper process and enable converting waste biomass into liquid biofuels rather than burning to provide heat. Other markets, such as data centers, are driven by special energy requirements such as extreme reliability. Most customers are not in the energy business but need heat and electricity to produce a product—a manufactured good, education, retail sales (shopping malls), marine transport or some other product. As a consequence, there will be large incentives to lease rather than own FBs. Leasing avoids the regulatory challenges that remain with the owner of the FB. Leasing creates large incentives for FP standardization of sizes and transportability to maintain the value of the FB at the end of the lease—similar to the leasing of jet engines and aircraft. The economic constraints combined with technical constraints suggest competitive FBs will likely have outputs exceeding 10 MWt. There appear to be little incentives for very long-lived reactor cores because such machines require much larger inventories of fuel. Maintenance requirements and the options to provide technology updates may favor shorter lifetimes (~5 years). The assessment is that there is the potential for FBs to be economically viable and play a major role in global decarbonization in three markets: heat, non-grid electricity and maritime applications.
High Temperature Gas-cooled Reactors (HTGRs) can be used for the generation of electricity and their process heat can be used to improve the efficiency of chemical processes such as hydrogen production. The JAEA-operated High-Temperature engineering Test Reactor (HTTR-GT/H2) is exploring using the reactor for electricity and hydrogen production. A RELAP5-3D model of the HTTR-GT/H2 secondary system has been developed using design information. The various components and heat exchangers in the secondary system were modeled and results were compared to the design conditions. The results for the sole-power generation mode were shown to fit the design conditions very well. The largest temperature difference was on the order of 7 K, and the largest pressure difference was on the order of 0.05 MPa. The results for the hydrogen cogeneration mode did not match the design conditions nearly as well. The largest temperature difference was about 39 K and the largest pressure difference was about 0.27 MPa at the compressor outlet. The larger differences for the hydrogen cogeneration mode are attributed to the various complex components and the flow being split in the secondary loop. A transient reduction in heat removal capability of the secondary system was investigated. Reactor temperatures are anticipated to rise as a result. The core reactivity response due to this increase in temperature is investigated and is expected to add negative reactivity to the reactor. An economic dispatch model was developed for a nuclear-driven iodine-sulfur cycle system to determine hydrogen sale prices that would make such a system profitable. The study focuses on the development of the economic model and the role that input data plays on final calculated values. It was found that the input electricity prices, whether using historical data or a host of synthetic time histories, produce significantly different breakeven hydrogen sale prices. As such, great care should be used in these economic dispatch analyses to select reasonable input assumptions.
With increasing integration of distributed energy resources (DERs), active distribution networks (ADNs) can actively participate in the electricity markets by dispatching their DERs, which can change the existing electricity market paradigm. It is essential to investigate the strategic behaviors of ADNs and their DER dispatch when they participate in the wholesale market as price-makers. This paper proposes a bi-level optimization model to study the strategic behavior of an ADN in both energy and reserve markets. The optimal scheduling of DERs in the ADN is modeled as the upper level problem and the joint energy and reserve market-clearing of the ISO is modeled as the lower-level problem. The two-level optimization models exchange bidding information and energy/reserve prices with each other. The proposed bi-Ievel optimization problem is converted to a mathematical programming with equilibrium constraints (MPEC) by using Karush-Kuhn Tucker (KKT) conditions and strong duality theory. Further, the MPEC problem is reformulated as a computationally-solvable mixed integer second order cone programming (MISOCP) model. The simulation results on an illustrative case demonstrate the impact of the strategic bidding of the ADN on the day-ahead energy and reserve market prices.
Energy-efficient homes save their occupants money through lower energy bills. These savings might be capitalized into higher home sale prices. They also improve the household’s net cash flow, which might make households better able to pay mortgage debt. The U.S. Department of Energy (DOE)’s Home Energy Score (HES) assigns a 1-10 score to homes and estimates annual energy bills based on modeled energy consumption. In this paper we investigated the relationship between HES metrics and two housing market outcomes: home sale price and mortgage performance. We found that the relationship was only statistically significant in places with a mandatory HES assessment at the time of sale. Using a sample of 26,291 home sales that occurred after HES assessments, we found that a one-point increase in HES in these locations was associated with a 0.5% increase in sale price, and an increase in $100 of estimated annual energy bills was associated with a 0.4% decrease. This magnitude of effect is consistent with estimated magnitudes of home sale premiums for other green or energy-efficient home certifications in the literature. We also found that a one-point increase in HES was associated with a 5.5% reduction in the odds of a loan going 30 days delinquent if the loan originated after the assessment occurred. Similarly, we found that a $100 decrease in estimated annual energy bills was associated with a 2.3% decrease in the odds of a loan going delinquent if it originated after the assessment occurred. Our results suggest that HES provides a valuable signal for housing market transactions in specific situations.
Escalation rates applied to the savings from energy savings performance contracts (ESPCs) and related financed energy projects play a large role in their scope and costs. The U.S. Department of Energy’sFederal Energy Management Program (FEMP) employs the National Institute of Standards and Technology (NIST) to package projections of real (uninflated) energy prices and general inflation forecasts developed by two other federal government entities. The main output from this exercise is NIST’s Energy Escalation Rate Calculator (EERC), which is strongly recommended by FEMP for use in performance contracts and relied on by federal agencies and others conducting performance contracts as an objective source for their projects’ escalation rates. This study investigated whether the rates prescribed by EERC (and a coarser NIST tool that preceded it) have provided users with estimates that approximate actual changes in energy prices over the years. The results are encouraging: the NIST tools slightly under-estimated actual electricity prices and somewhat over-estimated those for natural gas. Concern regarding the latter is mitigated, however, because a) it is seen as due primarily to the increasing surplus of natural gas from the “fracking revolution” in the 2010s, and b) natural gas savings were found to account for only 14.4% of the total savings from the largest population of federal ESPCs (FEMP’s indefinite quantity ESPC contract, representing roughly 430 projects), compared to almost four times that (56.8%) for electricity savings. Consequently, reliance on EERC appears to be a sound policy.
This paper describes the FECM/NETL CO2 Transport Cost Model (CO2_T_COM), a technoeconomic model of CO2 transport by pipeline, and the FECM/NETL CO2 Saline Storage Cost Model (CO2_S_COM), a technoeconomic model of storage of CO2 in a deep, subsurface saline formation. The results of applying CO2_T_COM to calculate break-even CO2 prices to transport CO2 at different mass flow rates and different distances are presented. Similarly, CO2_S_COM is used to calculate the break-even CO2 price for storing CO2 in 314 potential storage formations across the US. These break-even prices are used to construct cost-supply curves for CO2 storage which are presented on a national and regional basis. CO2_T_COM and CO2_S_COM are the most comprehensive open source technoeconomic models available for analyzing CO2 pipeline transport and CO2 saline storage. To be presented at the SPE?AAPG/SEG Carbon Capture Utilization and Storage Conference in Houston, TX, March 11-13, 2024.
As the penetration of renewable energy sources increases, the growing renewable power variability brings ramping issues to power systems. Meanwhile, the development of distributed energy resources (DERs) makes the distribution systems to provide both energy and ancillary services. To incentivise individual resources and customers to alleviate ramping issues on the demand side, a two-stage distribution locational marginal price (DLMP) calculation and decomposition method is developed to formulate the marginal power ramping price for DERs. In the first stage of the proposed method, a distribution system operator market scheduling model based on AC optimal power flow is designed to estimate the optimal operating point of the distribution system. Subsequently, the voltage and power flow constraints are linearised in stage two to calculate DLMP. Finally, based on the Lagrange function and sensitivity factors, DLMP is decomposed to the marginal costs for active/reactive power, voltage management, power loss and power variability. Case studies demonstrate that the proposed model can effectively smooth the power fluctuation and reduce the ramping flexibility requirements of distribution systems.
In this study, we explore how the energy and capacity values of coupled systems comprising solar photovoltaic arrays and battery storage (PV-plus-battery systems) could evolve over time based on the evolution of the bulk power system. Using a price-taker model with simulated hourly energy and capacity prices projected from the present to 2050, we simulate the revenue-maximizing dispatch of a range of DC-coupled PV-plus-battery configurations in three locations in the United States. These configurations are defined by the inverter loading ratio (ILR, the ratio of the PV array capacity to the inverter capacity, which we vary from 1.4 to 2.6) and the battery-inverter ratio (BIR, the ratio of the battery power capacity to the inverter capacity, which we vary from 0.25 to 1.0). Based on each configuration's total value, we estimate the breakeven costs needed to justify each incremental increase in ILR (holding BIR constant) or BIR (holding ILR constant). We find that, in a future with low-cost renewable energy technologies, PV-plus-battery system ILRs can be economically increased to around 2.0-2.4 at a BIR of 1.0, depending on solar resource. Our results indicate that a likely evolution of PV-plus-battery system design will be increasingly greater battery power capacity to mitigate the declining PV capacity value, which will, in turn, enable increasingly higher ILRs to further increase energy value. The extent to which PV-plus-systems will be deployed with increasingly higher ILRs depends primarily on whether PV cost declines outpace declining value and increasing curtailment over time.
As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero-carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.