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DOE OSTI2020

Bioenergy Technologies in Long-Run Climate Change Mitigation: Results from the EMF-33 Study

Bioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

bioenergy↗

DOE OSTI2024

Eco-driving Profile Optimization by Dynamic Programming for Battery Electric Vehicles

Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes-acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. Further, this study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption.

Autonomous vehicles↗

DOE OSTI2025

3D printing of packaging inserts from biomass-fungi composites: Environmental sustainability analysis

In this study, a comprehensive Life cycle assessment (LCA) is conducted on molded packaging inserts from expanded polystyrene (EPS) foam, molded packaging inserts from biomass-fungi composite, and 3D-printed packaging inserts from biomass-fungi composite under the low mix / high volume (LMHV) scenario and molded and machined packaging inserts from EPS foam, molded and machined packaging inserts from biomass-fungi composite, and 3D-printed packaging inserts from biomass-fungi composite under the high mix / low volume (HMLV) scenario. Six environmental impact categories—climate change, acidification, eutrophication, fossil resource scarcity, land use, and water consumption—are analyzed to evaluate the environmental trade-offs associated with each type of packaging inserts. Under the LMHV scenario, molded packaging inserts from biomass-fungi composite emerge as the best option due to their lower impact on climate change, acidification and water consumption compared to other types of packaging inserts. Conversely, molded packaging inserts from biomass-fungi composite face challenges in land use and eutrophication, primarily due to raw material production. LCA also reveals that 3D-printed packaging inserts from biomass-fungi composite are the most environmentally favorable option under the HMLV scenario, due to significantly lower contributions to climate change, eutrophication, and water consumption compared to other types of packaging inserts. Conversely, 3D-printed packaging inserts from biomass-fungi composite face challenges in acidification and land use, primarily due to raw material production. As part of the LCA, sensitivity analyses show that sourcing energy from 100% renewable sources substantially lowers climate change impacts across all packaging types, while varying transportation distances results in only minor changes, indicating the dominant role of upstream material and manufacturing processes. Additional sensitivity analysis is conducted under the HMLV scenario to assess the impact of material removal during machining on the environment. The amount of material removal is varied from 10 to 70% for the sensitivity analysis and it highlights that the amount of material removed during machining has no significant impact on climate change for packaging inserts from EPS foam. However, molded and machined packaging inserts from biomass-fungi composite show an increasing trend in climate change with higher amount of material removal, while 3D-printed packaging inserts from biomass-fungi composite exhibit a decreasing trend, driven by reduced raw material usage and energy consumption.

09 BIOMASS FUELS↗

DOE OSTI2020

Growth, total lipid, and omega-3 fatty acid production by Nannochloropsis spp. cultivated with raw plant substrate

Improving productivity and lipid concentration in microalgae is important for the economic success of both biofuel and microalgae coproducts production. Nannochloropsis spp. are marine microalgae currently being grown at large-scale for the production of biofuel and lipid coproducts. In this work, we demonstrate improvements of growth and omega-3 production in Nannochloropsis gaditana CCMP526 and Nannochloropsis oceanica CCAP849/10 with plant substrate addition, a potentially economical option for increasing microalgal productivity. Furthermore, we examine growth in the presence of corn stover, switchgrass, sugarcane bagasse, and yard waste. By examining the microbial ecology of N. gaditana cultures with and without plant substrate and with and without antibiotics, we discovered a potential bacterial interaction in these cultures, but its presence is not necessary for algal growth improvements in the presence of plant substrate. Analysis of plant substrate morphology by scanning electron microscopy (SEM) after cultivation in media with and without N. gaditana shows a degradation of specific plant structural features and a colonization of the plant phloem by N. gaditana. An examination of N. gaditana in a Congo red plate assay indicates potential cellulolytic activity, but a preliminary examination of the potential cellulase transcripts does not reveal differential expression of a candidate in the presence of plant. This evidence demonstrates potential raw plant utilization by a marine microalga for increased productivity and provides a potentially economical option for increasing coproduct concentration at industrial scale without genetic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

What is the long-term demand for liquid hydrocarbon fuels and feedstocks?

Liquid hydrocarbons made from crude oil serve many functions: (1) a dense, easy-to-store, easy-to-transport energy source, (2) a method for daily-to-seasonal energy storage, (3) a chemical feedstock, (4) a chemical reducing agent and (5) a method to enhance high-temperature heat transfer in many furnaces and industrial processes. Liquid hydrocarbons can be produced and used without increasing atmospheric carbon dioxide levels if made from non-fossil feedstocks such as carbon dioxide or biomass. Understanding future liquid hydrocarbon demand is the starting point in assessing the viability of such options. Our assessment is that U.S. demand for liquid hydrocarbons is unlikely to go below the equivalent of 10 million barrels per day of crude oil but could be as high as 20 million barrels per day. The costs to replace liquid hydrocarbons increases rapidly at lower liquid hydrocarbon consumption rates. Hydrocarbon biofuels from cellulosic feedstocks can meet such demands if produced with large external inputs of heat and hydrogen. Options based on more limited feedstocks (starches, plant oils, sugars, etc.) can’t meet such demands.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Model quantification of the effect of coproducts and refinery co-hydrotreating on the economics and greenhouse gas emissions of a conceptual biomass catalytic fast pyrolysis process

Here we present model results for a scaled-up conceptual process informed by bench scale biomass catalytic fast pyrolysis (CFP) and hydrotreating experimental data. This process uses a Pt/TiO 2 catalyst during CFP, which produces a partially deoxygenated organic biocrude intermediate that is then hydroprocessed to a hydrocarbon fuel blendstock; the catalyst also enables high yields of acetone and methyl-ethyl-ketone (MEK) coproducts. Two options for hydroprocessing were modeled: (A) co-hydrotreating at a petroleum refinery using hydrogen sourced from steam reforming of natural gas and (B) standalone hydrotreating at a biorefinery using hydrogen sourced from CFP off gases. The results revealed that Case A was economically advantageous with a modeled minimum fuel selling price (MFSP) of $\$$2.83/GGE or gallon gasoline equivalent (in 2016 US dollars), while the additional cost of standalone hydrotreating facilities in Case B increased the MFSP to $3.13/GGE. Conversely, greenhouse gas (GHG) emissions were lower for Case B (3.9 g CO 2 e/MJ) compared to Case A (21.5 g CO 2 e/MJ) due to the use of biogenic (Case B) and fossil-derived (Case A) hydrogen. In a third option (Case C), the requirements for separation and purification of acetone and MEK were removed from the refinery co-processing scenario (Case A) to evaluate the impacts of this process simplification. Elimination of these coproducts increased the MFSP to $3.21/GGE and GHG emissions to 35 g CO 2 e/MJ. These comparisons based on our detailed conceptual models provide economic and sustainability guidance regarding processing choices for future biorefineries. While refinery coprocessing using existing equipment and the production of relatively valuable coproducts can benefit the economics, the hydrogen-source and biogenic coproducts can have significant impacts on the sustainability of the process, and feasibility to use CFP off-gases or other renewable sources for hydrogen production can help lower GHG emissions.

09 BIOMASS FUELS↗

DOE OSTI2023

Predicting displacement damage for ion irradiation: Origin of the overestimation of vacancy production in SRIM full-cascade calculations

Ion irradiation and implantation have wide applications that demand accurate determination of displacement damage profile and distribution of implanted ion concentration. The prediction of vacancies is especially important to determine displacements per atom (dpa), the standard parameter of primary radiation damage in materials. However, significant discrepancies exist in estimations of vacancies between full-cascade (F-C) and quick calculation (Q-C) options in the popular computer code SRIM. This study inspected the SRIM code and a relatively new code called Iradina, which uses a similar methodology, to develop an understanding of the origin of vacancy overestimation in the F-C options for SRIM and Iradina. Here, we found that the default values of thresholds (namely final energy in SRIM and replacement energy in Iradina) in displacement production calculations results in excessively large number of calculated vacancies and very few replacements. After conducting multiple calculations using SRIM, Iradina, and MARLOWE (all based on the binary collision approximation), a comparison of the results indicates that there is a shortcoming in the SRIM and Iradina F-C methodology for treating near-threshold collisions. This issue is responsible for the deficiency of replacements and excess of calculated vacancies in the SRIM and Iradina F-C results. Drawing on the principles of collision physics, we propose recommendations for modifying the source codes to address these issues.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Simulating low-energy neutrino interactions with MARLEY

Monte Carlo event generators are a critical tool for the interpretation of data obtained by neutrino experiments. Several modern event generators are available which are well-suited to the GeV energy scale used in studies of accelerator neutrinos. However, theoretical modeling differences make their immediate application to lower energies difficult. In this paper, I present a new event generator, MARLEY, which is designed to better address the simulation needs of the low-energy (tens of MeV and below) neutrino community. The code is written in C++14 with an optional interface to the popular ROOT data analysis framework. The current release of MARLEY (version 1.2.0) emphasizes simulations of the reaction 40 Ar ( ν e , e − ) 40 K ⁎ but is extensible to other channels with suitable user input. This paper provides detailed documentation of MARLEY's implementation and usage, including guidance on how generated events may be analyzed and how MARLEY may be interfaced with external codes such as Geant4. Further information about MARLEY is available on the official website at http://www.marleygen.org. Program title:MARLEY 1.2.0 CPC Library link to program files:https://doi.org/10.17632/4v7zxnc8j3.1 Developer's respository link:http://github.com/MARLEY-MC/marley Code Ocean capsule:https://codeocean.com/capsule/9868179 Licensing provisions: GNU General Public License 3.0 Programming language: C++14 External routines/libraries used: GNU Scientific Library [1,2] (required), ROOT [3,4] (optional) Nature of problem: Simulation of neutrino-nucleus scattering events at energies of tens-of-MeV and below Solution method: Initial two-to-two scattering kinematics are sampled using the allowed approximation differential cross section and tables of precomputed nuclear matrix elements. Subsequent de-excitations of the remnant nucleus are simulated using a Monte Carlo implementation of the Hauser-Feshbach statistical model and tabulated γ-ray decay schemes for discrete nuclear levels. Additional comments including restrictions and unusual features: Input data are provided with the code that are suitable for producing simulations of the charged-current reaction 40 Ar ( ν e , e − ) 40 K ⁎ , coherent elastic neutrino-nucleus scattering on spin-zero target nuclei, and neutrino-electron elastic scattering on any atomic target. Preparation of new reaction input files (whose format is documented in Appendix B) would enable other reaction channels and nuclear targets to be handled by the existing code framework. Although there is no maximum neutrino energy enforced by the code itself, realistic neutrino-nucleus scattering events may be generated up to roughly 50 MeV. Above this energy, the effects of forbidden nuclear transitions, which are neglected in the current treatment of the cross sections (see section 2.1), become increasingly important.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Comparison of ammonia with methanol, liquefied natural gas and conventional marine transportation fuels through life cycle cost and emissions analysis

This work evaluates ammonia as a potential marine fuel for a SUEZMAX tanker and compares it with methanol, liquefied natural gas, and conventional fuel oils. The motivation arises from the need to identify low-emission, cost-competitive fuel options that can reduce greenhouse gas emissions from international shipping. The central hypothesis is that ammonia produced from renewable energy sources can achieve lower well-to-wake greenhouse gas emissions with varying life cycle costs based on the region. Life cycle assessment and techno-economic analysis were performed for a thirty-year vessel lifetime on two representative trade routes: from Saudi Arabia to Japan and from Saudi Arabia to the Netherlands. Four ammonia production pathways were assessed: natural gas, natural gas with carbon capture, natural gas pyrolysis, and renewable electricity–based synthesis. Results show that wind-based ammonia produced in Saudi Arabia achieved the lowest life cycle well-to-wake greenhouse gas emissions, between 0.58 and 0.64 million metric tons, among all fuels when using regional grid process electricity. With renewable process electricity, ammonia produced from natural gas pyrolysis in Saudi Arabia showed comparable emissions of 0.37 to 0.44 million metric tons with wind-based ammonia of 0.37 to 0.43 million metric tons. Liquefied natural gas exhibited the lowest life cycle cost, between 402 and 412 million United States dollars, and the only negative carbon abatement cost, ranging from −277 to –322 United States dollars per metric ton of greenhouse gas, compared with high sulfur fuel oil. The findings indicate that renewable ammonia offers a promising long-term pathway for reducing shipping emissions, while liquefied natural gas remains the most cost-effective option in the near term.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

The Role of Energy Storage in the Uptake of Renewable Energy: A Model Comparison Approach

The power sector needs to ensure a rapid transition towards a low-carbon energy system to avoid the dangerous consequences of greenhouse gas emissions. Storage technologies are a promising option to provide the power system with the flexibility required when intermittent renewables are present in the electricity generation mix. This paper focuses on the role of electricity storage in energy systems with high shares of renewable sources. The study encompasses a model comparison approach where four models (GENeSYS-MOD, MUSE, NATEM, and urbs - MX) are used to analyse the storage uptake in North America. The analysis addresses the conditions affecting storage uptake in each country and its dependence on resource availability, technology costs, and public policies. Results show that storage may promote emissions reduction at lower costs when renewable mandates are in place whereas in presence of carbon taxes, renewables may compete with other low-carbon options. The study also highlights the main modelling approach shortcomings in the modelling of electricity storage in integrated assessment models.

electricity storage↗

DOE OSTI2020

Will Consumers Really Pay for Green Electricity? Comparing Stated and Revealed Preferences for Residential Programs in the United States

Public support is growing for policy initiatives to spur a transition from a fossil to renewable energy portfolio in the electricity sector. Some utilities in the United States offer programs that allow consumers to voluntarily pay premiums (0.1-7.0 cents/kWh) for electricity from renewable sources. However, it is unclear whether public support translates to paying for green electricity if given the option. Our analysis employs data from two national, longitudinal surveys on energy attitudes and willingness to pay for renewables to investigate whether environmental concerns and stated preferences for renewable energy translate to consumer behavior as measured through ratepayer participation in voluntary utility renewable energy programs known as utility green pricing. We find higher green pricing program participation rates in areas where consumers have stronger feelings about the environmental impacts of energy. Consumers in high-participation areas also have a higher stated willingness to pay for renewable energy, on average, than consumers in low-participation areas. We also find income, homeownership, and home value explain some of the difference between high- and low-participation programs. Further, program participation is lower in areas where utilities charge higher green pricing program premiums. These findings suggest that green power programs - such as utility green pricing - offer a market-based mechanism for consumers to realize their desire to purchase renewable energy. Policymakers may use these results to support further expansion of green power programs in areas where customers currently lack accessible and affordable options to act on their environmental beliefs and concerns.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Discrete versus continuous: Enhancing battery optimization in capacity expansion models

This study compares two battery modeling approaches for capacity expansion models: discrete-duration and continuous-duration formulations. In the discrete approach, battery duration is fixed, and power capacity is optimized. In the continuous approach, both power and energy capacities are decision variables, allowing storage duration to be optimized endogenously. Although both discrete-duration and continuous-duration battery formulations are used in long-term power system planning models, the literature has provided limited direct, systematic comparisons of their implications within a common modeling framework. To address this gap, this study implements both approaches in the Regional Energy Deployment System (ReEDS TM ) capacity expansion model using two resource adequacy methods, across a range of future system conditions, and with varying battery cost projections. Results show continuous-duration and high-resolution discrete approaches produce similar capacity expansion outcomes. The continuous formulation achieves faster runtimes compared to discrete-duration runs with many discrete-duration options. However, the discrete-duration approach allows users to choose to have limited fidelity for storage duration options, which in some cases can outperform the continuous formulation. The continuous formulation has the lowest overall system costs, indicating its ability to fine-tune storage duration to better meet specific system needs. This study's findings provide a side-by-side evaluation of discrete and continuous battery modeling approaches and offer guidance for improving the representation of real-world systems, flexibility, and computational efficiency for representing energy storage in long-term power system planning models.

25 ENERGY STORAGE↗

DOE OSTI2020

Optimization of energy requirements for CO 2 post-combustion capture process through advanced thermal integration

The energy optimization modeling work described here was performed to determine efficiency improvements that could be achieved for existing coal-fired power plants to retrofit a partial CO 2 capture from the post-combustion flue gas for carbon sequestration through thermal integration. The work presented includes optimization of the mono-ethanol amine (MEA)-based post-combustion CO 2 capture to reduce energy requirements that could be achieved at existing power plants by thermal integration of the steam turbine cycle, boiler, CO 2 compression train and post-combustion CO 2 capture process to offset efficiency and capacity losses that would be incurred by retrofit or implementation of post-combustion CO 2 capture. Furthermore, partial CO 2 capture, involving treatment of less than 100% of the flue gas leaving the plant and modular design of the CO 2 scrubbing system, was also investigated. Thermal integration of the steam turbine cycle with boiler and CO 2 compression train improved cycle and plant performance and offset, in part, the negative effects of post-combustion CO 2 capture. The best-analyzed integration options improved gross power output by 5% and net unit efficiency by 1.57%, relative to the conventional MEA process. Operating with 40% CO 2 capture increased gross power output by 11.6–14% (depending on the MEA thermal integration option), relative to the conventional MEA integration and 90% CO 2 capture. The improvement in net unit performance is larger compared to the improvement in turbine cycle performance because of the CO 2 compression work, which is also reduced by partial CO 2 capture.

42 ENGINEERING↗

DOE OSTI2023

Virtual prototyping of liquid metal blanket performance in fusion pilot plant

Liquid metal blanket is a dominant design option for the next step fusion devices responsible for harvesting energy from fusion reaction, and simultaneously producing fuel for the same reaction through tritium breeding. Liquid metal blankets introduce additional complexity to the design due to fluid motion, fluid structure interaction, and magnetohydrodynamic (MHD) effects arising from the motion of the conducting fluid through the magnetic field. They are also directly affected by the plasma heat flux and neutronic fluence. PPPL is currently developing a virtual prototyping system for numerical analysis of the liquid metal blankets for future fusion devices. The system has a customized 3D computational fluid dynamics (CFD) code in its core, allowing MHD flow and conjugate heat transfer analysis in blankets fluids and solids. The code was successfully used before for dual coolant blanket analysis [A. Khodak et al., Fusion Eng. and Des. 137 (2018)]. Recently the same code was modified to allow verified simulation of MHD flows at high Hartmann numbers of several thousand typical for blanket applications. CFD code receives volumetric heat source distribution from the neutronic analysis based on MCNP code. In addition, direct tritium breeding simulation will be performed allowing optimization of the blanket performance. 2D axisymmetric version of neutronics code will be used for rapid optimization, with 3D version employed for detailed analysis. The surface heat distribution on the plasma facing wall will be defined by the software HEAT allowing 3D modeling of the heat flux based on the magnetic field distribution including gyro-orbit effects. Results of thermal analysis are imported into structural analysis code also included in the system. Finally, direct import of CAD geometry will be used for analyzing all components and as a result design option can be efficiently optimized.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV

High penetration of grid-edge, inverter-based photovoltaic (PV) can cause significant voltage fluctuations not only at the distribution but also at the sub-transmission levels due to PV output intermittency. Traditional reactive power planning approaches do not consider intermittency, nor the possibility of coordinating the control of existing and future volt-ampere reactive resources. This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high PV penetration and intermittency with a minimum investment cost. The planning tool coordinates with an optimization-based volt-var operational tool for: a) modeling the coordination of all existing var assets in both sub-transmission and distribution systems to reduce the need of new equipment, and b)selecting a set of scenarios with voltage violations, derived from PV intermittency c) testing the final investment decision. The tool obtains an investment need for each intermittency scenario with a proposed optimal power-flow framework with efficient techniques to handle a high number of discrete variables. Two options are provided for final planning decision: i) a conservative direct combination of investment need solutions and ii) a machine learning-based selection of representative investment needs at most time steps. The final investment decision options are verified using a realistic large-scale sub-transmission system and 5-minute PV and load data. The results show a significant voltage performance improvement with a lower investment cost for additional var equipment compared to conventional approaches.

14 SOLAR ENERGY↗

DOE OSTI2021

Influence of Geography on Prostate Cancer Treatment

Several definitive treatment options are available for prostate cancer, but geographic access to those options is not uniform. We created maps illustrating provider practice patterns relation to patients and assessed the influence of distance to treatment receipt.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Understanding the trade-offs of national municipal solid waste estimation methods for circular economy policy

Policies embracing circular economy concepts have taken hold in national legislation around the world. As the number of governments and organizations adopting circular economy policies increases, so does the need for accurate and timely measurement of material resource flows. Since many countries do not have access to centrally reported municipal solid waste (MSW) data, estimation and modeling are critical in evaluating circular economy policy effectiveness. The purpose of this paper is to examine three modeling approaches estimating national MSW data in the United States, including industry-based material flow analysis, waste-extended input-output modeling, and aggregated regional waste reporting. We establish five criteria to guide the analysis through the context of policy monitoring (data quality, flow totality, update frequency, sensitivity to disruption, and product granularity) and use these criteria to analyze and score each model. We then use a literature search to identify five, internationally-implemented options for circular economy policy and determine the data and modeling components that are most helpful in evaluating policy effectiveness. Finally, we provide a crosswalk of the model scores and policy needs to inform the suitability of model selection by policy type. We found that data quality and update frequency are identified as critical components for evaluating circular economy policies within the models evaluated, and can both be fulfilled by aggregated regional waste reporting. Flow totality, sensitivity to disruption, and product granularity requirements vary by both model and policy types. While none of the evaluated models satisfy the combination of requirements for any of the five policies, industry-based material flow analysis offers flow totality for extended producer responsibility, landfill bans, and recycling rate target policies that typically require it. Here, the waste-extended input-output model can provide disruption sensitivity and product granularity as needed for policies like minimum recycled content and market restrictions. Policy developers in areas where strong centralized data collection is not an option should design policy action(s) with modeling tradeoffs in mind, including the potential hybridization of modeling approaches that may provide the most accurate national MSW estimates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗