Pricing Frequency Response Market Products for Inverter-Based Resources.
Abstract not provided.
Garcia, Manuel↗
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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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Chapter 2: Evaluating a Concentrating Solar Power Plantas an Extended-Duration Peaking Resource
We explore the ability of a concentrating solar power (CSP) plant with thermal energy storage (TES) to provide peaking capacity. We focus on future power systems, wherein net load patterns may be significantly different than they are today (e.g., due to higher renewable-energy penetrations). We examine 28 locations in the southwestern United States over an 18-year period. The hourly operation of the CSP plants are simulated to determine their potential to provide energy during an eight-hour peak-load window for each day up to 365 days per year. Our result shows that for the large majority of locations and years, CSP plants with certain configurations (i.e., in terms of solar field and TES sizes) can provide nearly 100% peak-load capacity. We examine also the amount of supplemental energy (e.g.,by using natural gas as a supplemental thermal-energy source) that would be required to ensure that a CSP plant could serve the eight highest-load hours of every day of the year. We find that in most cases, a CSP plant supplemented with natural gas would require less than 5% of the fuel that is used by a natural-gas fired power plant providing the same level of reliable capacity. A series of sensitivity analyses show that these results are robust to the number of peak-load hours and days that are considered and the configuration of the CSP plant.
capacity value↗
Nontechnical Barriers to Geothermal Development
Geothermal energy presents a significant opportunity for the United States (US). The US has the largest known geothermal resource in the world, with over 31 GW of conventional geothermal (i.e., hydrothermal) potential. Despite this, the development of geothermal power plants has lagged other renewable resources. Though some of the gaps stem from technical barriers numerous non-technical barriers are preventing geothermal energy from reaching its full potential. The gaps include a need to reduce the cost impacts of seismic risk, environmental risk, exploration, drilling and permitting cost risk, and reduced summer capacity on plant profitability. This report identifies pathways to overcome these barriers. We find that geothermal energy could increase its market presence by acting as a complement to lower cost renewables, providing flexible or baseload power in low carbon scenarios. We also outline several contractual and operational strategies (including the use of hybrid systems) that plant operators can pursue to improve the value of their resource, such as multi part remuneration mechanisms that guarantee revenue (e.g., availability payments or a Contract for Differences approach) or resource risk hedging approaches (e.g., shaped market products and portfolio resource approaches).
15 GEOTHERMAL ENERGY↗
Functionalized Mesoporous Carbon Materials for Effective Recovery of Rare Earth Elements from NdFeB Magnet Scrap
The increasing demand and high dependence on international supplies of rare earth elements (REEs) have prompted the US to explore alternative resources and sustainable technologies for domestic REE production. One potential resource is the secondary electronic and industrial wastes at DoD complexes and across the US. These hazardous wastes require high costs for the maintenance and remediation, but their high-REE contents offer unique opportunities for REE recovery. Current hydrometallurgical technologies for REE extraction are energy intensive and costly, with low REE recovery efficiency and the generation of hazardous secondary wastes. Thus, it is crucial to develop high efficiency and environmental-friendly technologies for REE extraction and recovery from the secondary wastes. This project aims to develop novel functionalized mesoporous carbon materials for effective recovery of REEs from NdFeB magnet scrap. The proposed novel functionalized materials will be applied in a solid-liquid separation platform that can effectively extract, recover and separate REEs from NdFeB magnet scrap (the current focus), but also from other secondary electronic/industrial wastes in the future, minimize the generation of secondary hazardous wastes, and offset the management costs for existing waste streams.
36 MATERIALS SCIENCE↗
HIPSTER (Harmonized Impacts across Products, Scenarios, and Technologies for Environmental and Resource metrics) [SWR-24-17]
Development of a prototype, code-based life cycle assessment framework, the Harmonized Impacts across Products, Scenarios, and Technologies for Environmental and Resource metrics (HIPSTER), to coherently assess outputs of prospective NREL models across environmental and resource use metrics. Eventually, HIPSTER is aimed to also cover socioeconomic and justice metrics. At present, NREL model scenario impact assessments are performed incoherently, as they as based on varying assumptions and inputs, specifically life cycle inventories, and cover different system boundaries. HIPSTER was created to align the assumptions and boundaries with those of cradle-to-grave life cycle assessment. In FY22, HIPSTER was successfully applied using scenarios from the ReEDS™ model, NREL's flagship capacity expansion model resulting in cradle-to-grace time-series life cycle impacts across LCA midpoints and resource use metrics.
Ghosh, Tapajyoti↗
Dynamic Linking of Upstream Energy and Freight Demands for Bio and Fossil Energy Pathways in the Global Change Analysis Model
Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.
Sampedro Martinez de Estivariz, Jon↗
Microbiome Adaptation Could Amplify Modeled Projections of Global Soil Carbon Loss With Climate Warming
Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally. We assume that a key trait of microbes, their resource allocation to production of exoenzymes (which facilitate decomposition of organic matter)—is optimized to environmental temperatures by natural selection. We find that eco-evolutionary optimization results in microbes allocating more resources to enzyme production under warming. When applied at the global scale, eco-evolutionary optimization enhances the biological realism of soil carbon models and significantly amplifies global soil carbon loss by 2100. Our results highlight the significant potential of microbial eco-evolutionary responses to influence carbon cycle feedbacks to climate change, and motivate an urgent need for more comprehensive data to accurately quantify the adaptive potential of microbiomes in response to climate change.
Biodiversity & Conservation↗
Introduction to this special section: Geothermal energy
Geothermal energy is a global renewable resource that has the potential to provide a significant portion of baseload energy in many regions. In the United States, it has the potential to provide 8.5% of the electric generation capacity by the middle of the century. In general, geothermal systems require heat, permeability, and water to be viable for energy generation. However, with current technologies, only heat is strictly necessary in a native system. Engineered geothermal systems (EGS) introduce water into the subsurface at elevated pressures and reduced temperatures and enhance permeability through hydraulic and/or shear fracturing. Additionally, although moderate- to high-temperature resources currently dominate geothermal energy production, low-temperature resources have been utilized for direct-use cases. When well balanced and maintained, geothermal resources can produce significant amounts of heat and achieve long-term sustainability on the order of an estimated tens to hundreds of years.
15 GEOTHERMAL ENERGY↗
Grassland productivity responds unexpectedly to dynamic light and soil water environments induced by photovoltaic arrays
Abstract Agrivoltaic (AV) systems are designed to coproduce photovoltaic (PV) energy on lands simultaneously supporting agriculture (food/forage production). PV infrastructure in agroecosystems alters resources critical for plant growth, and water‐limited agroecosystems such as grasslands are likely to be particularly sensitive to the unique spatial and temporal patterns of incident sunlight and soil water inherent within AV systems. However, the impact of resource alteration on forage production, the primary ecosystem service from managed grasslands, is poorly resolved. Here, we evaluated seasonal patterns of soil moisture (SM) and diurnal variation in incident sunlight (photosynthetic photon flux density [PPFD]) in a single‐axis‐tracking AV system established in a formerly managed semiarid C 3 grassland in Colorado. Our goals were to (1) quantify dynamic patterns of PPFD and SM within a 1.2 MW PV array in a perennial grassland, and (2) determine how aboveground net primary production (ANPP) and photosynthetic parameters responded to the resource patterns created by the PV array. We hypothesized that spatial variability in ANPP would be strongly related to SM patterns, typical of most grasslands. We measured significant reductions in ANPP directly beneath PV panels, where SM and PPFD were both low. However, in locations with significantly increased SM from the shedding and redistribution of precipitation by PV panels, ANPP was not increased. Instead, ANPP was greatest in locations where plants were shaded in the afternoon but received high levels of PPFD in the morning hours, when air temperatures and vapor pressure deficits were relatively low. Thus, contrary to expectations, we found relatively weak relationships between SM and ANPP despite significant spatial variability in both. Further, there was little evidence that light‐saturated photosynthesis ( A sat ) and quantum yield of CO 2 assimilation (ϕCO 2 ) differed for plants growing directly beneath (lowest PPFD) versus between (highest PPFD) PV panels. Overall, the AV system established in this semiarid managed grassland did not alter patterns of ANPP in ways predictable from past studies of controls of ANPP in open grasslands. However, our results suggest that the diurnal timing of low versus high periods of PPFD incident on plants is an important determinant of productivity patterns in grasslands.
14 SOLAR ENERGY↗
Alternate routes to acetate tolerance lead to varied isoprenol production from mixed carbon sources in Pseudomonas putida
ABSTRACT Lignocellulose is a renewable resource for the production of a diverse array of platform chemicals, including the biofuel isoprenol. Although this carbon stream provides a rich source of sugars, other organic compounds, such as acetate, can be used by microbial hosts. Here, we examined the growth and isoprenol production in a Pseudomonas putida strain pre-tolerized (“PT”) background where its native isoprenol catabolism pathway is deleted, using glucose and acetate as carbon sources. We found that PT displays impaired growth in minimal medium containing acetate and often fails to grow in glucose-acetate medium. Using a mutant recovery-based approach, we generated tolerized strains that overcame these limitations, achieving fast growth and isoprenol production in the mixed carbon feed. Changes in the glucose and acetate assimilation routes, including an upregulation in PP_0154 (SpcC, succinyl-CoA:acetate CoA-transferase) and differential expression of the gluconate assimilation pathways, were key for higher isoprenol titers in the tolerized strains, whereas a different set of mechanisms were likely enabling tolerance phenotypes in media containing acetate. Among these, a coproporphyrinogen-III oxidase (HemN) was upregulated across all tolerized strains and in one isolate required for acetate tolerance. Utilizing a defined glucose and acetate mixture ratio reflective of lignocellulosic feedstocks for isoprenol production in P. putida allowed us to obtain insights into the dynamics and challenges unique to dual carbon source utilization that are obscured when studied separately. Together, this enabled the development of a P. putida bioconversion chassis able to use a more complex carbon stream to produce isoprenol. IMPORTANCE Acetate is a relatively abundant component of many lignocellulosic carbon streams and has the potential to be used together with sugars, especially in microbes with versatile catabolism such as P. putida . However, the use of mixed carbon streams necessitates additional optimization. Furthermore, the use of P. putida for the production of the biofuel target, isoprenol, requires the use of engineered strains that have additional growth and production constraints when cultivated in acetate and glucose mixtures. In this study, we generate acetate-tolerant P. putida strains that overcome these challenges and examine their ability to produce isoprenol. We show that acetate tolerance and isoprenol production, although independent phenotypes, can both be optimized in a given P. putida strain. Using proteomics and whole genome sequencing, we examine the molecular basis of both phenotypes and show that tolerance to acetate can occur via alternate routes and result in different impacts on isoprenol production.
de Siqueira, Guilherme M. V. (ORCID:00000002364563↗
Prospects of hydrogen cogeneration and carbon dioxide utilization in electrochemical refineries for ethylene production via oxidative coupling of methane: A techno-economic assessment
Ethylene is recognized as one of the most significant chemicals globally. The projected ethylene production for 2023 stands at 227.6 million tonnes, with expectations of continued demand growth. The primary use of ethylene, accounting for over 76%, lies in the production of plastics such as polyethylene, polyvinyl chloride, and polystyrene. Despite the enduring carbon retention potential of plastics themselves, traditional ethylene feedstock production from fossil resources contributes substantially to greenhouse gas emissions, ranging from 0.29 to 2.29 kg CO2 /kg Ethylene depending on various factors like feedstock and process design. Various decarbonization strategies, including the utilization of hydrogen as a primary heat source, have been proposed to reduce the carbon footprint of ethylene production, but their efficacy remains limited, offering at best a 0–30% reduction in on-site CO 2 emissions.
09 BIOMASS FUELS↗
BOOTS: Bayesian Optimization for Optimal Test Selection
BOOTS is a package for optimal selection of candidate operating points in large-scale manufacturing applications. It is designed to optimally select operating points to maximize predicted values of product quality and resource efficiency according to a data-driven model. The package is based on the use of a multi-input multi-output (MIMO) Gaussian process model to describe the relationships between inputs (operating points) and outputs (product quality and resource efficiency). This package does not provide any site- or process-specific information.
Villez, Kris [Oak Ridge National Laboratory (ORNL)↗
Engineering Novosphingobium aromaticivorans to produce cis,cis -muconic acid from biomass aromatics
ABSTRACT The platform chemical cis,cis- muconic acid ( cc MA) provides facile access to a number of monomers used in the synthesis of commercial plastics. It is also a metabolic intermediate in the β-ketoadipic acid pathway of many bacteria and, therefore, a current target for microbial production from abundant renewable resources via metabolic engineering. This study investigates Novosphingobium aromaticivorans DSM12444 as a chassis for the production of cc MA from biomass aromatics. The N. aromaticivorans genome predicts that it encodes a previously uncharacterized protocatechuic acid (PCA) decarboxylase and a catechol 1,2-dioxygenase, which would be necessary for the conversion of aromatic metabolic intermediates to cc MA. This study confirmed the activity of these two enzymes in vitro and compared their activity to ones that have been previously characterized and used in cc MA production. From these results, we generated one strain that is completely derived from native genes and a second that contains genes previously used in microbial engineering synthesis of this compound. Both of these strains exhibited stoichiometric production of cc MA from PCA and produced greater than 100% yield of cc MA from the aromatic monomers that were identified in liquor derived from alkaline pretreated biomass. Our results show that a strain completely derived from native genes and one containing homologs from other hosts are both capable of stoichiometric production of cc MA from biomass aromatics. Overall, this work combines previously unknown aspects of aromatic metabolism in N. aromaticivorans and the genetic tractability of this organism to generate strains that produce cc MA from deconstructed biomass. IMPORTANCE The production of commodity chemicals from renewable resources is an important goal toward increasing the environmental and economic sustainability of industrial processes. The aromatics in plant biomass are an underutilized and abundant renewable resource for the production of valuable chemicals. However, due to the chemical composition of plant biomass, many deconstruction methods generate a heterogeneous mixture of aromatics, thus making it difficult to extract valuable chemicals using current methods. Therefore, recent efforts have focused on harnessing the pathways of microorganisms to convert a diverse set of aromatics into a single product. Novosphingobium aromaticivorans DSM12444 has the native ability to metabolize a wide range of aromatics and, thus, is a potential chassis for conversion of these abundant compounds to commodity chemicals. This study reports on new features of N. aromaticivorans that can be used to produce the commodity chemical cis,cis -muconic acid from renewable and abundant biomass aromatics.
09 BIOMASS FUELS↗
STOCHASTIC OPTIMAL POWER FLOW FOR REAL-TIME MANAGEMENT OF DISTRIBUTED RENEWABLE GENERATION AND DEMAND RESPONSE (Final Report)
To meet the grand challenge of a sustainable energy future, there has been a surge of interest in renewable energy. Today, the uncertainty associated with renewable resources is handled by using operating reserves. The high penetration of renewable resources, however, introduces difficult-to-control dynamics and challenges for power system operation. Decision support tools are necessary at the bulk system operational level to recognize and efficiently utilize renewable resources and distributed demand response products in concert with traditional grid resources. It is envisaged that responsive load can potentially have very significant cost advantages over either spinning or non-spinning ramping reserve. Critical decisions are made during hour(s)-ahead and real-time power system operation regarding the commitment and dispatch of generators to ensure power delivery is both reliable and economic. These decisions are typically made by a security constrained optimal flow, which determines future generator commitments, dispatches, and ensures adequate reserves are available in the event of a contingency (unexpected outage) or if future system conditions deviate from forecasts. However, security has been always based on a pre-specified subset of contingency constraints whose enforcement does not guarantee security under all possible future possibilities while also giving little or no weight to the likelihood of each contingent event or the severity of its consequences. Existing tools, which are based exclusively on deterministic optimization models, do not yield optimal operational decisions to address these new challenges, in terms of both reliability and cost-effectiveness. This project has focused on developing a stochastic optimal power flow (SOPF) framework, which integrates renewable resource uncertainty, load uncertainty, distributed storage (DS), demand response (DR) products, in a holistic manner to address the uncertainty associated with ever-increasing renewable resources, along with the inclusion of distributed demand response products in future power systems. A proof-of-concept problem was created using the Pennsylvania-Jersey-Maryland (PJM) power system network. Synthetic wind generation was added to the system to simulate 50% wind penetration. A 1-hour test of SOPF operation indicated more than 6% operational cost savings. The project continued by adding the Midwestern Independent System Operator (MISO) as a partner, with focus shifting from SOPF to Stochastic Look-Ahead Unit Commitment (SLAC). Unlike PJM, MISO is faced with significant renewable energy resources within its footprint and is challenged with substantial uncertainty in its operations. The SLAC distinguishes itself from existing tools that operators use. At best, today’s tools solve two to three cases independently, where one or two system parameters, such as forecasted load level (e.g., a low, base, and high forecast), are varied and the resulting scenarios are analyzed independently. The stochastic-based optimization of SLAC leverages statistical information from an ensemble of potential operational scenarios and their respective likelihood. The SLAC output can be translated into valuable information to the operator such as suggested commitments, optimal scheduling and dispatch of resources, reserve requirements at both locational and zonal resolutions, ramping availability and requirements, availability of demand response including operational guidance concerning the near-term and real-time coordination between distributed energy resources, and utilization of distributed storage resources. The developed SOPF/SLAC tool, a stand-alone tool compatible with existing EMSs, will provide system operators with unprecedented visibility, flexibility and predictability to these resources and operational guidance concerning the real-time coordination between DERs and DR/DS products. The game changing and practical impact of this disruptive technology will be dramatic and will usher in a new era in the electric power industry, wherein green energy concepts are fully embraced, and electric power costs are lowered throughout the nation.
42 ENGINEERING↗
Online Learning and Pricing for Network Revenue Management with Reusable Resources
We consider a price-based network revenue management problem with multiple products and multiple reusable resources. Each randomly arriving customer requests a product (service) that needs to occupy a sequence of reusable resources (servers). We adopt an incomplete information setting where the firm does not know the price-demand function for each product and the goal is to dynamically set prices of all products to maximize the total expected revenue of serving customers. We propose novel batched bandit learning algorithms for finding near-optimal pricing policies, and show that they admit a near-optimal cumulative regret bound of $O(J\sqrt{XT})$, where J, X, and T, are the numbers of products, candidate prices, and service periods, respectively. As part of our regret analysis, we develop the first finite-time mixing time analysis of an open network queueing system (i.e., the celebrated Jackson Network), which could be of independent interest. Our numerical studies show that the proposed approaches perform consistently well.
42 ENGINEERING↗
Simulating competition in the US bioeconomy to produce hard‐to‐electrify transportation fuels using limited biomass resources
This study presents a novel bioeconomy optimization framework, BiOpt, designed to address critical questions regarding the strategic use of limited US biomass resources for biofuel production. By integrating detailed techno-economic analyses, life cycle assessments, and resource assessment data, BiOpt optimizes resource distributions across competing technologies to maximize economic performance and/or minimize greenhouse gas emissions. Using feedstock scenarios from the 2023 Billion Ton Study, the analysis explores optimal biomass allocations across sustainable aviation fuel, diesel, and marine biofuel conversion pathways given varying production targets and policy incentives. Results demonstrate distinct feedstock preferences and pathway utilizations when prioritizing economic returns vs. emissions reductions. For instance, fats, oils, and greases were highly favored in cost-optimized scenarios, while low-carbon feedstocks such as wet waste dominated greenhouse gas-minimized strategies. The findings underscore the pivotal role of policy incentives and technological advances in shaping biofuel supply chains and provide actionable insights for scaling sustainable biofuel production to decarbonize hard-to-electrify sectors. This framework offers a robust tool for policymakers and stakeholders to evaluate biofuel strategies that balance energy output, economic viability, and environmental impact.
09 BIOMASS FUELS↗
Critical Literature Review of Quantitative Sustainability Assessment Methods for the Circular Economy
The circular economy (CE) has been proposed to be an operational framework for sustainable development that decouples economic growth from resource consumption. The CE ambition is to maximize the retention of value in products, materials, and resources in the economy over time with the help of CE strategies such as selling a service rather than a product, reusing and repairing products or their components, and recycling. The social, economic, and environmental performances of CE strategies need to be measured against their linear counterparts to avoid strategies that increase circularity but have other unintended externalities. However, there is currently no tool specifically designed to compare circular to linear systems and, thus, various methods from different fields have been applied. This session aims at reviewing, contrasting, and critiquing different methods that have been applied to assess the CE until now, along with an up to date state of the science in this field. Methods from the industrial ecology field have most often been applied to study the CE. However, the transition to CE involves both technological improvements as well as social changes. New business models such as collaborative consumption models (e.g., Uber or Airbnb) are examples of the new patterns of production and consumption of the CE, which may be difficult to analyze from a purely industrial ecology perspective. Methods from complexity science and humanities could therefore complement the industrial ecology perspective to extend the scope of the analysis. Such an approach could also answer a longstanding criticism of the CE which, in contrast with sustainability, solely focuses on the environment and the economy. Moreover, the hybridization of two or more existing methods can yield additional capabilities, which may enable the exploration of additional CE-related research questions. The 90 minutes session will have several presentations and conclude with a moderated, interactive panel discussion on how methods from different fields could be combined to harness their relative strengths.
circular economy↗
GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.
15 GEOTHERMAL ENERGY↗