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DOE OSTI2023

A subcellular biochemical model for T6SS dynamics reveals winning competitive strategies

Abstract The type VI secretion system (T6SS) is a broadly distributed interbacterial weapon that can be used to eliminate competing bacterial populations. Although unarmed target populations are typically used to study T6SS function in vitro, bacteria most likely encounter other T6SS-armed competitors in nature. However, the connection between subcellular details of the T6SS and the outcomes of such mutually lethal battles is not well understood. Here, we incorporate biological data derived from natural competitors of Vibrio fischeri light organ symbionts to build a biochemical model for T6SS at the single-cell level, which we then integrate into an agent-based model (ABM). Using the ABM, we isolate and experiment with strain-specific physiological differences between competitors in ways not possible with biological samples to identify winning strategies for T6SS-armed populations. Through in vitro experiments, we discover that strain-specific differences exist in T6SS activation speed. ABM simulations corroborate that faster activation is dominant in determining survival during competition. Once competitors are fully activated, the energy required for T6SS creates a tipping point where increased weapon building and firing becomes too costly to be advantageous. Through ABM simulations, we identify the threshold where this transition occurs in the T6SS parameter space. We also find that competitive outcomes depend on the geometry of the battlefield: unarmed target cells survive at the edges of a range expansion where unlimited territory can be claimed. Alternatively, competitions within a confined space, much like the light organ crypts where natural V. fischeri compete, result in the rapid elimination of the unarmed population.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A mesoscopic link-transmission-model able to track individual vehicles

Macroscopic traffic flow is a common choice for large-scale traffic simulations. These models do not provide individual-specific metrics as outputs. However, this treatment is necessary in agent-based-models, as in, for example, assigning routes based on personal characteristics. Here, in this paper, we propose an extension of the link-transmission-model, an efficient and yet accurate discretization of the Lighthill-Whitham-Richards (LWR) model, which allow vehicles to be tracked individually while keeping the main features of the underlying model. The extension comprises modifying the link and node models to ensure that the flow between links is always at discrete levels. Therefore, every unit of flow is associated with one individual vehicle moving from its current to its next link. An upper bound of the discretization error is provided. We show that the proposed model resembles its continuous counterpart on lane drop, merge, and diverge cases. In addition, we apply the model into three different networks to validate its applicability in large networks. Finally, we also confirm the parameter transferability between continuous and discrete models and that both can well reproduce field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Simulating policy interventions in the interfirm diffusion of low-carbon technologies: An agent-based evolutionary game model

Policy interventions are frequently used by authorities around the world to mitigate carbon emissions. However, economic systems are essentially complex adaptive systems, which often exhibit unexpected responses to exogenous interventions and leave intervenors in a dilemma, even if the interventions are elaborately designed. To explore the behaviors of the diffusion system regarding low-carbon technologies, this study builds an agent-based model (ABM) to simulate enterprises' reactions to multiple policy interventions aimed at spurring low-carbon technology diffusion. The simulated enterprises are in a complex network where they play evolutionary games with their neighbors, which enables the model to possess two critical features of economic systems: adaptiveness and equilibrium. The model reveals a dilemma of policy interventions: intuitively, carbon taxes, asymmetric penalties (only imposed on un-low-carbon enterprises) and subsidies can improve the diffusion, which is consistent with previous research; counter-intuitively, all these policies turn out to be inefficient or even harmful to low-carbon enterprises because of the diffusion system's high adaptiveness. Specifically, when carbon taxes and penalties increase, both the low-carbon and un-low-carbon enterprises end up with the equilibrium of equivalent but lower profits. In contrast, all the enterprises earn equivalent but more profits even though subsidies are only given to the low-carbon enterprises, which implies that the un-low-carbon enterprises (indirectly) grab a portion of the subsidies and accordingly weaken the incentive effect of subsidies. These system behaviors are summarized as "equalizing effect" because the system tends to equalize the impact of both positive and negative interventions among all enterprises even though the policy interventions are asymmetrically imposed on one type of the enterprises. The findings also indicate that policies implemented to enlarge green market sizes can help policymakers bypass the dilemma.

Agent-based modeling↗

DOE OSTI2020

An agent-based approach to study the diffusion rate and the effect of policies on joint placement of photovoltaic panels and green roof under climate change uncertainty

As two of the highest trending green technologies, photovoltaic panels and green roofs are proven to be effective practices for energy generation and energy saving. The achievable impact from the widespread installation of such technologies is, however, not clearly established. This is mainly because the degree of this impact highly depends on the inherently uncertain environmental and climate factors, as well as the unknown adoption rates of these technologies, which in turn depend on different characteristics of decision makers and interactions among them. To that end, this study aims to investigate the diffusion rate of these green technologies under uncertainties caused by climate change, characteristics of adopters, and their interactions. An integrated framework is developed to capture the interplay between financial and attitudinal aspects, as well as the uncertainties due to both the stochastic nature of system parameters and the interactions among agents involving human beings. Specifically, this framework consists of a integer programming model to optimize the green roof and/or photovoltaic panel installation settings for a given building under climate change uncertainty, and an agent-based model to factor in the role of human behavior and interactions. A case study for the city of Knoxville, TN, is presented to evaluate the effects of different policies on the diffusion rate of the green technologies of interest. The results show that the affordability of green technologies and public awareness are the key drivers of the adoption of these technologies, which highlight the important role of the decision makers in impacting the diffusion rate.

14 SOLAR ENERGY↗

DOE OSTI2024

National Modeling of Geothermal District Energy Systems with Ambient-Temperature Loops Using dGeo: Preprint

Geothermal district energy systems (DES) with ambient-temperature loops, also known as thermal energy networks, are one option for decarbonizing space heating and cooling loads. Geothermal fifth-generation DES include an "ambient" temperature thermal loop that connects heat pumps at each building with thermal balancing sources such as geothermal borehole fields. Heating and cooling are provided via a water-source heat pump at each end-user. This project seeks to analyze the nationwide potential for ambient-temperature loop districts by creating a new module within the Distributed Geothermal Market Demand Model (dGeo). dGeo is an agent-based modeling tool for distributed geothermal resources; it can investigate potential on a nationwide or statewide scale using geospatial data for all 50 states and thermal demands for existing buildings. This process allows for high-level estimates of technical and economic potential for ambient-temperature loop districts across the United States. Using GHEDesigner, a lookup table was created to size borehole fields for different thermal loads and ground conditions experienced across the country. A cost and financing structure, along with incentives, were applied. Cost estimates include costs for the distribution network, borehole field installation and operation, and circulation pump operation, while savings are calculated based on agent energy bills. This newly developed module can be used for assessing which areas of the country have the highest potential for agent benefits from ambient-temperature loop installation and assess the impact of different costing and pricing future scenarios. While the code is still under development and nationwide simulations are ongoing, initial results for two states are provided. Future work includes expanding the module to consider mixed residential and commercial districts and considering multiple costing scenarios.

ambient temperature loop↗

DOE OSTI2025

National Modeling of Geothermal District Energy Systems with Ambient-Temperature Loops Using dGeo

Geothermal district energy systems (DES) with ambient-temperature loops, also known as thermal energy networks, are one option for decarbonizing space heating and cooling loads. Geothermal fifth-generation DES include an "ambient" temperature thermal loop that connects heat pumps at each building with thermal balancing sources such as geothermal borehole fields. Heating and cooling are provided via a water-source heat pump at each end-user. This project seeks to analyze the nationwide potential for ambient-temperature loop districts by creating a new module within the Distributed Geothermal Market Demand Model (dGeo). dGeo is an agent-based modeling tool for distributed geothermal resources; it can investigate potential on a nationwide or statewide scale using geospatial data for all 50 states and thermal demands for existing buildings. This process allows for high-level estimates of technical and economic potential for ambient-temperature loop districts across the United States. A lookup table was created using GHEDesigner to size borehole fields for different thermal loads and ground conditions experienced across the country. A cost and financing structure, along with incentives, were applied. Cost estimates include costs for the distribution network, borehole field installation and operation, and circulation pump operation, while savings are calculated based on energy bills for building owners (agents). This newly developed module can be used for assessing which areas of the country have the highest potential for agent benefits from ambient-temperature loop installation and assess the impact of future cost and price scenarios. Initial results for statewide analysis (for Vermont) and nationwide (for United States) are provided. Future work includes expanding the module to consider mixed residential and commercial districts as well as evaluating multiple cost scenarios.

ambient-temperature loop↗

DOE OSTI2021

Distributed Solar Adoption in Orlando: A Household-Level Model for Distribution Resource Planning

Potential for rooftop solar in Florida is massive (47% of retail sales, 3rd overall nationally), yet adoption lags (12th nationally). A 2018 Florida Public Service Commission ruling authorizing solar third-party ownership (leasing) has substantially increased attention on distributed solar in the state. The city of Orlando has committed to a 100% clean-energy target by 2050 and deployment of solar and storage are expected to contribute significantly to reaching the goal. Deployment of customer-adopted solar, unlike utility-procured solar, is uncertain, but known to be spatially correlated with demographic factors and existing adoption. We develop a new method to adapt NREL's dGen model in order to represent building-level agents in adoption forecasts for the Orlando Utility Commission (OUC) service territory. Using the agent-based model we develop projections of solar adoption, subject to scenarios varying future solar costs and valuation, and aggregate adoption predictions by OUC distribution feeder. We find substantial spatial heterogeneity in the projected level of adoption by OUC distribution feeder. For instance, 25% of all projected adoption through 2050 would be concentrated on just 5% of feeders and 88% of projected adoption on 50% of feeders. Because of the uncertainty in adoption, bottoms-up solar adoption forecasting methods at the household-level are integral to long-term resource planning by anticipating system needs as customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources.

14 SOLAR ENERGY↗

DOE OSTI2021

Farmer characteristics and decision-making: A model for bioenergy crop adoption

We report the commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

09 BIOMASS FUELS↗

DOE OSTI2023

Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets

This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.

Trinko, David A.↗

DOE OSTI2020

The synergy between stakeholders for cellulosic biofuel development: Perspectives, opportunities, and barriers

While understanding individual stakeholders' perspectives on the adoption and conversion to a biofuel-based landscape has been a subject of many previous studies on biofuels, there has been relatively little attention given to understanding how the interaction between multiple stakeholders involved in biofuel development could influence the widespread adoption of biofuel production. Here, this paper analyzes the key stakeholder interactions utilizing various data sources including survey results, social media posts, and empirical and theoretical analyses. An intensive review is conducted for a number of surveys and research papers on different aspects of biofuel development such as land use choices, biorefinery and transportation, infrastructure development, consumer priorities, environmental impacts, etc. Following that, a stakeholder synergy approach is applied to synthesizing typical responses of stakeholders, such as producers, consumers, biorefineries, rural communities, and the government, and discussing how their responses influence each other's decisions and the overall system performance. Based on the findings of inadequate stakeholder synergy, it is recommended that new surveys and further research should be conducted to understand why synergy between stakeholders in biofuel development is absent. Additionally, this paper provides research perspectives, including (1) applying cutting-edge text-mining techniques to conduct sentiment analysis, and research and public attention analysis; (2) using an agent-based model to simulate stakeholder interactions and understand the factors that influence stakeholder synergy and the emergence of a bioeconomy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Regional Representation of Wind Stakeholders' End-of-Life Behaviors and Their Impact on Wind Blade Circularity

Wind plant power has seen tremendous growth in the US and worldwide, representing the most significant renewable energy installed capacity besides hydropower. While wind power enables decarbonizing the electricity grid, the rising amount of end-of-life (EOL) wind blades - which are arduous to recycle - present a challenge for landfills if disposed of whole and a missed opportunity to recover valuable composite materials. The circular economy (CE) concept proposes strategies to rethink, reuse and recover products, components, and materials. However, transitioning to a CE implies changing how business models, supply chains, and behaviors deal with products and waste; changes arduously captured with traditional methods used to assess circularity such as life cycle assessment or material flow analysis (MFA). Here we present an agent-based model (ABM) that captures behavioral aspects impacting wind blade circularity in the US. The ABM also accounts for wind plant projects and landfills heterogeneity - a characteristic not easily included in top-down approaches such as MFA, input-output analysis, or system dynamics. Results show that recycling is divided as most recycling facilities are on the eastern side of the country, a challenge that could be alleviated by shredding blades before transportation. Recycling programs from the wind industry could also seed recycling behaviors within wind plant owners. Better yet, new blade designs could increase circularity if original equipment manufacturers accept the risks involved with the investments needed to adapt the production lines.

17 WIND ENERGY↗

DOE OSTI2021

PastoralScape: An Environment-Driven Model of Vaccination Decision Making Within Pastoralist Groups in East Africa

Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change, contagious diseases spread and evolving national and international trade. A key factor in the resilience of livestock that communities depend on is human decision making regarding vaccination against prevalent diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. This paper describes an agent-based model that couples models of disease propagation, animal health, human decision making, and external GIS data sources capturing measures of foraging condition. We describe the design of the sub-models, their coupling, and demonstrate the sensitivity of the model to parameters that relate to controllable factors such as government and NGO information sources that can influence human decision making patterns. This model is intended to form the basis upon which richer economic and human factor models can be built.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Characterizing Binding Interactions That Are Essential for Selective Transport through the Nuclear Pore Complex

Specific macromolecules are rapidly transported across the nuclear envelope via the nuclear pore complex (NPC). The selective transport process is facilitated when nuclear transport receptors (NTRs) weakly and transiently bind to intrinsically disordered constituents of the NPC, FG Nups. These two types of proteins help maintain the selective NPC barrier. To interrogate their binding interactions in vitro, we deployed an NPC barrier mimic. We created the stationary phase by covalently attaching fragments of a yeast FG Nup called Nsp1 to glass coverslips. We used a tunable mobile phase containing NTR, nuclear transport factor 2 (NTF2). In the stationary phase, three main factors affected binding: the number of FG repeats, the charge of fragments, and the fragment density. We also identified three main factors affecting binding in the mobile phase: the avidity of the NTF2 variant for Nsp1, the presence of nonspecific proteins, and the presence of additional NTRs. We used both experimentally determined binding parameters and molecular dynamics simulations of Nsp1FG fragments to create an agent-based model. The results suggest that NTF2 binding is negatively cooperative and dependent on the density of Nsp1FG molecules. Our results demonstrate the strengths of combining experimental and physical modeling approaches to study NPC-mediated transport.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Modeling the effects of carbon payments and forest owner cooperatives on carbon storage and revenue in Pacific Northwest forestlands

The forestlands of the Pacific Northwest store a significant amount of carbon, provide wildlife habitat, generate revenue for forestland owners, and provide for sustainable generation of forest products. Promoting forest carbon storage is increasingly recognized as a key management objective to mitigate climate change. We developed an agent-based model to examine the effectiveness of current land management practices in sustaining forest carbon storage and revenue flows, and the utility of market-based carbon payments in improving carbon storage while also improving revenue generation in the Pacific Northwest. We find that while market based incentives can improve both forest carbon storage and revenue, they are limited in their benefit to small forest owners. Our results highlight the crucial need for additional policy interventions to reduce barriers to market access for small private owners and ensure sustainable forest management in this region. We further show that a forest owners’ cooperative, which aggregates small owners to reduce the costs of participating in a carbon market, can help overcome these barriers and result in both greater carbon storage and a more equitable benefit from carbon payments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty

The installed data storage capacity in the U.S. will reach 2.2 Zettabytes by 2025, generating about 50 million units of end-of-life (EOL) hard-disk drives (HDDs) per year. Due to data security concerns, most EOL HDDs are currently shredded (even when still functioning), representing an economic loss. Moreover, raw material extraction linked to the increased demand for storage causes environmental impacts. Besides mitigating the threat posed by sudden restrictions of raw materials, the circular economy (CE) offers to maximize value retention in the economy and reduce the environmental impacts of human activities. Common CE strategies are reusing and recycling products. However, the reuse of hard disk drives is currently burdened by the lack of trust HDD end-users have toward other non-physical means of data removal than shredding. Here, an agent-based modeling (ABM) approach is proposed to explore how techno-economic and social factors affect end-users' decisions to adopt EOL management practices other than shredding. The proposed method also accounts for data uncertainty by applying a semi-quantitative approach. Results demonstrate how increased green procurement and more robust standards could spur end-users' trust toward data-wiping technologies. Even when accounting for uncertainty, HDDs' reuse brings better environmental and economic benefits than HDD shredding followed by material recovery. The semi-quantitative approach proposed in this study could be more universally applied in future ABM, especially given the often-stochastic nature of such models. The developed ABM is also the first to represent several HDD industry stakeholders and demonstrate how the HDD shredding lock-in situation could be resolved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) simulate activity and travel decisions at the disaggregate level of households, and individuals. To do this, ABMs require detailed information pertaining to socioeconomic and demographic characteristics of individuals. Various synthetic population generators (SPGs) have been proposed to address this need. However, most of the SPGs currently in practice are cross-sectional in nature, and do not account for the interrelationships among household's or individual's life progression. This is a major shortcoming of SPGs as literature has shown that transportation decisions are impacted by lifecycle events that unfold over a span of time. While some demographic evolution simulators have been proposed to address this shortcoming, they: i) are developed using cross-sectional data, ii) do not capture the full spectrum of lifecycle events and their interdependency. Overcoming these drawbacks, this paper proposes a Demographic Microsimulator (DEMOS) which captures the 'continuum of life' by accounting for a range of household-, and individual-level lifecycle events. DEMOS is developed using the Panel Survey of Income Dynamics, which is one of the world's longest running longitudinal surveys. DEMOS sub-models consider key lifecycle events which are influenced by a host of demographic variables. The whole framework is applied to evolve the population of San Francisco Bay Area over a 9-year horizon. Results indicate that the household and individual evolution are tightly connected, and that the structural framework (i.e., model sequencing) is a key element in capturing the population trend accurately.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2023

Assessing the Performance of a Circular Economy for Wind Energy Technologies: A Summary of Three Analytical Tools

A circular economy emphasizes the efficient use of all resources and presents opportunities for addressing series of economic and environmental objectives at local, regional, and national levels. Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. As a result, it is important to evaluate the performance and tradeoffs associated with circular economy transitions. This poster summaries three previously published analytical tools that were used to assess the performance of developing a circular economy for wind energy technologies: the Renewable Energy Materials Properties Database (REMPD), a circular economy agent-based model for wind blades (CE Wind ABM), and the Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework. The REMPD is a comprehensive database of materials used in wind and solar plants, including material quantities and physical materials availability. The CE Wind ABM allows us to understand how wind stakeholders' end-of-life behaviors influence wind blade circularity and evaluate the impact of regional variables (e.g., logistics and transportation). And, the CELAVI framework is a modular framework that can be used to evaluate the impacts associated with circular economy transitions. These three analytical tools have been applied to evaluate circular economy transitions for wind energy technologies and they could be expanded to other technologies and products.

agent-based modeling↗

DOE OSTI2021

Role of the social factors in success of solar photovoltaic reuse and recycle programmes

By 2050, the cumulative mass of end-of-life photovoltaic (PV) modules may reach 80 Mt globally. The impacts could be mitigated by module recycling, repair and reuse; however, previous studies of PV circularity omit the consideration of critical social factors. Here we used an agent-based model to integrate social aspects with techno-economic factors, which provides a more realistic assessment of the circularity potential for previously studied interventions that assesses additional interventions that cannot be analysed using techno-economic analysis alone. We also performed a global sensitivity analysis using a machine-learning metamodel. We show that to exclude social factors underestimates the effect of lower recycling prices on PV material circularity, which highlights the relevance of considering social factors in future studies. Interventions aimed at changing customer attitudes about used PV boost the reuse of modules, although used modules can only satisfy one-third of the US demand during 2020–2050, which suggests that reuse should be complemented by recycling.

14 SOLAR ENERGY↗