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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Developing high performance RF heating scenarios on the WEST tokamak

High power experiments, up to 9.2 MW with LHCD and ICRH, have been carried out in the full tungsten tokamak WEST. Quasi non inductive discharges have been achieved allowing to extend the plasma duration to 53s with stationary conditions in particular with respect to tungsten contamination. Transitions in H mode are obtained lasting up to 4s with weak energy increment at the power crossing the separatrix is close to the threshold. Hot L mode plasmas (Te(0)>3keV) with a confinement time following the ITER L96 scaling are routinely obtained. The weak aspect ratio dependence of this scaling law is confirmed. Tungsten accumulation is generally not an operational issue on WEST. Difficulty of burning through tungsten can prevent from accessing to a hot core plasma in the ramp-up phase or can lead to rapid collapse of the central temperature when radiation is enhanced by a slight decrease of the temperature. Apart few pulses post-boronization, the plasma radiation is rather high (Prad/Ptot~50%) and is dominated by tungsten. This fraction does not vary as the RF power is ramped up and is quite similar in ICRH and/or LHCD heated plasmas. An estimate of the contribution of the RF antennas to the plasma contamination in tungsten is given.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Hierarchical Resilience Planning for Networked Microgrids: A Case Study of Puerto Rico

Microgrids can be designed to enhance the energy resilience of communities and critical infrastructures, such as hospitals, data centers, and communication networks, which are vulnerable to frequent weather-related disruption. Coordinating multiple microgrids in a network can leverage the geographical diversity of load and generation resources while enabling resilient and cost-effective planning of the distribution system. Designing a networked microgrid is complex, involving intricate technical assessment, cost-benefit analysis, site-specific requirements, and the evaluation of existing resources. Therefore, this paper proposes a hierarchical resilience planning framework and performs an extensive techno-economic analysis for the design of a networked microgrid. Hierarchical resilience planning involves technology sizing at an individual community level to meet the critical load and satisfy resilience criteria, and resource optimization at networked microgrid level to provide a higher level of resilience and energy adequacy. A real-world case of Puerto Rico's cooperative microgrid “Microrred de la Montaña” is investigated considering localized electricity tariffs, site-specific demand profiles, solar generation, and existing hydro resources. Multiple optimization scenarios are developed based on the resiliency requirement to estimate the capacity of solar photovoltaic and battery energy storage (BES) to be installed at each substation. The results provide the optimal sizing for individual community and networked microgrid to withstand 1day and 3-day outages along with the criteria for critical load.

13 - HYDRO ENERGY↗
DOE OSTI2021

A heuristic tool to assess regional impacts of renewable energy infrastructure on conservation areas

Wind and hydropower are important renewable components of national energy portfolios, but their infrastructure negatively affects biodiversity. Regional development requires identification of scenarios that minimize the cumulative impacts of multiple facilities. We introduce the cumulative impact plot (CIP) to quantify cumulative impacts of renewable energy development within a region. Summed impacts of facilities are plotted as a function of the number of facilities, with facilities ranked by increasing (the best-case scenario) or decreasing (worst-case scenario) individual impact. These curves represent lower and upper bounds to which alternative development scenarios (e.g., facilities ranked by generating capacity) can be compared. We used CIPs to assess overlap of potential wind and hydropower facilities with two types of conservation area in the United States: (1) federally protected lands and (2) critical habitats of federally threatened and endangered species. Here, we evaluated two alternative scenarios: facilities ranked by (A) decreasing generating capacity and (B) increasing distance from urban centers. Differences between the best-case and worst-case scenarios were large, thus revealing opportunities to develop facilities with limited impact on conservation areas. Alternative scenarios maximizing energy density generally resulted in conservation area overlap intermediate to best-case and worst-case scenarios. CIPs can also identify the proportion of wind versus hydropower favored under alternative scenarios. Build-out scenarios aimed at minimizing conservation area overlap favor hydropower, whereas alternative scenarios favor wind power. We conclude that CIPs can (1) complement—but should not replace—project-level environmental impact assessment, (2) integrate strategic, cumulative, and scenario-based assessments, and (3) harness rapidly growing geospatial data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Forming a database to study reversed magnetic shear from the National Spherical Torus eXperiment using machine learning

Achieving a long-lived reversed magnetic shear (RMS) target plasma in the National Spherical Torus eXperiment Upgrade will require developing various sustainment scenarios. To help with the ongoing plasma control efforts, the development of a new analysis for the motional Stark effect (MSE) diagnostic using a machine learning algorithm, namely, MSE-ML, is described. MSE-ML will be used to identify patterns during RMS discharges, some of which suffer magnetohydrodynamic (MHD) events resulting in current redistribution and monotonic q-profiles. A database consisting of q and magnetic shear profiles is being constructed primarily based on the existing National Spherical Torus eXperiment data with equilibrium reconstructions constrained by the magnetic field pitch angle profile measured using the multi-channel MSE diagnostic. An unsupervised k-means clustering of the data is developed to study the RMS formation as a function of time. The initial clustering from the q-profiles shows significant differences in both amplitude and the duration of the RMS period. As a goal, the clustering results that detect and distinguish shots with substantial and sustained RMS are to be used as a preprocessing step in a supervised algorithm to identify the underlying conditions that lead to long-lasting improved confinement with RMS. Another aim of the MSE-ML study is to identify precursors of RMS-destroying MHD events in either derived data such as the q-profile or directly measured data such as the magnetic field pitch angle profile.

Uzun-Kaymak, I. U. (ORCID:0000000276251493)↗
DOE OSTI2023

Alfvén eigenmode stability in a JET afterglow deuterium plasma and projections to deuterium–tritium plasmas

Abstract The performance of fusion devices relies strongly on the good confinement of energetic particles (EPs). Therefore, the investigation of EP transport by magnetohydrodynamic instabilities is one of the key aspects in the development of plasma scenarios. Alfvénic instabilities in particular can lead to significant losses of alpha particles that are essential for plasma self-heating. A so-called afterglow scheme has been developed to study the destabilization of Alfvén eigenmodes (AEs) by alpha particles and associated EP transport in the JET tokamak. In this work, the linear stability of AEs is discussed for the partial afterglow phase in a JET deuterium plasma discharge and for the full afterglow phase in a projected deuterium–tritium (DT) plasma. Thanks to recent upgrades in the tokamak transport code TRANSP, one can account for the contributions of different EP species to mode stability. Analysis of deuterium plasmas shows that AE growth rates are extremely sensitive to the energy and distribution of fast ions. An increase in fast ion energy can lead to more unstable AEs. In the afterglow phase of projected DT plasmas, it is EPs that mostly drive the AEs. However, the drive by alpha particles is comparable to that by beam ions and their contribution to the net growth rate might be hard to separate. According to the discussed projections, the destabilization of AEs might be ineffective because the background plasma damping significantly exceeds the EP drive. In this case, the development of an alternative plasma scenario that allows us to overcome such damping would be required in future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Development of an integrated core–edge scenario using the super H-mode

An optimized pedestal regime called the super-H (SH) mode is leveraged to couple a fusion relevant core plasma with a high density scrape-off layer appropriate for realistic reactor power exhaust solutions. Recent DIII-D experiments have expanded the operating space of the SH regime using advanced control algorithms and investigated optimization of impurity seeding, deuterium gas puffing, and 3D magnetic perturbations. Simultaneous real-time control of the pedestal density and radiated power with in-vessel coils and nitrogen seeding enable optimal coupled divertor and pedestal conditions. Four case studies are analysed with varied levels of radiated power in the divertor volume ranging from 0 (no seeding) to 8.5 MW radiated from carbon and nitrogen emission. Plasmas with a 4.5 MW radiated power target establish a radiative mantle, leading to divertor temperatures of ~16 eV while maintaining SH-mode, and with only marginal impact on the pedestal and core performance. Increased levels of N 2 seeding with a 7.5 MW radiated power target facilitate detachment onset and divertor temperatures <5 eV, with no degradation in stored energy and the operational point remaining inside the SH-mode channel for $ > 2.5{\tau }_{\text{E}}$. Finally, a 8.5 MW radiated power target leads to partial detachment, which is so far associated with the loss of access to SH-mode pedestal conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗
DOE OSTI2024

Biosphere Futures: a database of social-ecological scenarios

T. Biosphere Futures (https://biospherefutures.net/) is a new online database to collect and discover scenario studies from across the world, with a specific focus on scenarios that explicitly incorporate interdependencies between humans and their supporting ecosystems. It provides access to a globally diverse collection of case studies that includes most ecosystems and regions, enabling exploration of the multifaceted ways in which the future might unfold. Together, the case studies illuminate the diversity and plurality of people’s expectations and aspirations for the future. The objective of Biosphere Futures is to promote the use of scenarios for sustainable development of the biosphere and to foster a community of practice around social-ecological scenarios. We do so by facilitating the assessment, synthesis, and comparative analysis of scenario case studies, pointing to relevant resources, and by helping practitioners and researchers to disseminate and showcase their own work. This article begins by outlining the rationale behind the creation of the database, followed by an introduction to its functionality and the criteria employed for selecting case studies. Subsequently, we present a synthesis of the first 100 case studies included in the scenarios database, highlighting emerging patterns and identifying potential avenues for further research. Finally, given that broader utilization and contributions to the database will enhance the achievement of Biosphere Futures’ objectives, we invite the creators of social-ecological scenarios to contribute additional case studies. By expanding the database’s breadth and depth, we can collectively foster a more nuanced understanding of the possible trajectories of our biosphere and enable better decision making for sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems. All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Developing an Active Learning algorithm for learning Bayesian classifiers under the Multiple Instance Learning scenario

In the Multiple Instance Learning scenario, the training data consists of instances grouped into bags, and each bag is labelled with whether it is positive, i.e. contains at least one positive instance. First, Active Learning, in which additional labels can be iteratively requested, has the potential to allow more accurate classifiers to be learned with less labels. Active Learning has been applied to the Multiple Instance Learning under two settings: when bag labels of unlabelled bags can be requested, and when instance labels within bags known to be positive can be requested. Second, Bayesian Active learning methods have the potential to learn accurate classifiers with few labels, because they explicitly track the classifier uncertainty and can thus address its knowledge gaps. Yet, there does not exist any Bayesian Active Learning method for the Multiple Instance Learning Scenario. In this work, we develop the first such method. We develop a Bayesian classifier for the Multiple Instance Learning scenario, show how it can be efficiently used for Bayesian Active Learning, and perform experiments assessing its performance. While its performance exceeds that when no Active Learning is used, it is sometimes better, sometimes worse than the naive baseline of uncertainty sampling, depending on the situation. This suggests future work: building more customizable Bayesian Active Learning methods for the Multiple Instance Scenario, customizable to whether bag or instance label accuracy is targeted, and the labeling budget.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Development of wind turbine blade recycling baselines in the United States

Over the past several years, the wind energy industry has received scrutiny regarding wind turbine blade (WTB) recycling due to the landfilling of WTBs caused by a lack of industrially viable recycling solutions. The amount of WTBs that will need to be recycled is set to increase in the United States as the deployment of wind energy is expected to rapidly grow to meet the nation's energy goals by 2035. While significant progress has been made worldwide, it is still unclear which WTB recycling solutions would be the most cost and energy effective within the United States for the existing fleet of wind turbines. To guide researchers and industry with a clear path forward, a range of options for WTB recycling in the United States are modeled through development of baseline scenarios and the use of formal life cycle assessment (LCA). Model data have been collected through literature review, industry engagement, and expert opinion regarding current end of life practices and considerations surrounding equipment, labor, and logistics. A detailed baseline for WTB decommissioning processes has been developed and used to assess alternative approaches, such as on-site shredding to compare the impacts on greenhouse gas (GHG) emissions. The developed LCA model and baseline scenarios for WTB recycling is used to assess the current WTB decommissioning practices in the United States along with emerging recycling pathways, including cement kiln co-processing and pyrolysis. Initial findings indicate that there are different approaches to decommissioning WTBs in the United States, each of which has unique implications for recycling. In light of this finding, additional results from the modeling will be used to better understand decommissioning practices and assist in making educated decisions on recycling pathways for the future. Throughout the analysis, focus was given to where international efforts might differ from the United States. WTB recycling is occurring worldwide, and different countries have different drivers for creating markets for recycled WTB materials. The contrasts and similarities between the United States and other countries offer insight to areas of opportunity that the United States could investigate and areas that can be readily transferred from existing solutions. By modeling and characterizing the current decommissioning practices and potential recycling solutions for the United States, a clearer vision will be created for pathways forward as to how to handle end of life WTBs to enable more efficient and cost-effective opportunities for material recovery from end-of-life WTBs.

Korey, Matt↗
DOE OSTI2025

Critically assessing sodium-ion technology roadmaps and scenarios for techno-economic competitiveness against lithium-ion batteries

Sodium-ion batteries have garnered notable attention as a potentially low-cost alternative to lithium-ion batteries, which have experienced supply shortages and price volatility for key minerals. Here we assess their techno-economic competitiveness against incumbent lithium-ion batteries using a modelling framework incorporating componential learning curves constrained by minerals prices and engineering design floors. We compare projected sodium-ion and lithium-ion price trends across over 6,000 scenarios while varying Na-ion technology development roadmaps, supply chain scenarios, market penetration and learning rates. Assuming that substantial progress can be made along technology roadmaps via targeted research and development, we identify several sodium-ion pathways that might reach cost-competitiveness with low-cost lithium-ion variants in the 2030s. In addition, we show that timelines are highly sensitive to movements in critical minerals supply chains—namely that of lithium, graphite and nickel. Our modelled outcomes suggest that being price advantageous against low-cost lithium-ion variants in the near term is challenging and increasing sodium-ion energy densities to decrease materials intensity is among the most impactful ways to improve competitiveness.

25 ENERGY STORAGE↗
DOE OSTI2020

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios

Abstract. Future trends in air pollution and greenhouse gas (GHG)emissions for China are of great concern to the community. A set of globalscenarios regarding future socio-economic and climate developments, combiningshared socio-economic pathways (SSPs) with climate forcing outcomes asdescribed by the Representative Concentration Pathways (RCPs), was createdby the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China'sfuture anthropogenic emission pathways. The DPEC is designed tointegrate the energy system model, emission inventory model, dynamicprojection model, and parameterized scheme of Chinese policies. The modelcontains two main modules, an energy-model-driven activity rate projectionmodule and a sector-based emission projection module. The activity rateprojection module provides the standardized and unified future energyscenarios after reorganizing and refining the outputs from the energy systemmodel. Here we use a new China-focused version of the Global ChangeAssessment Model (GCAM-China) to project future energy demand and supply inChina under different SSP–RCP scenarios at the provincial level. Theemission projection module links a bottom-up emission inventory model, theMulti-resolution Emission Inventory for China (MEIC), to GCAM-China andaccurately tracks the evolution of future combustion and production technologiesand control measures under different environmental policies. We developedtechnology-based turnover models for several key emitting sectors (e.g.coal-fired power plants, key industries, and on-road transportationsectors), which can simulate the dynamic changes in the unit/vehicle fleetturnover process by tracking the lifespan of each unit/vehicle on an annualbasis. With the integrated modelling framework, we connected five SSP scenarios(SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and threepollution control scenarios (business as usual, BAU; enhanced controlpolicy, ECP; and best health effect, BHE) to produce six combined emissionscenarios. With those scenarios, we presented a wide range of China's futureemissions to 2050 under different development and policy pathways. We foundthat, with a combination of strong low-carbon policy and air pollutioncontrol policy (i.e. SSP1-26-BHE scenario), emissions of major airpollutants (i.e. SO 2 , NO x , PM 2.5 , and non-methane volatile organic compounds – NMVOCs) in China willbe reduced by 34%–66% in 2030 and 58%–87% in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important rolein continuous emission reduction until 2050. In contrast, China's emissionswill remain at a high level until 2050 under a reference scenario without activeactions (i.e. SSP3-70-BAU). Compared to similar scenarios set from theCMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates ofemission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in2020–2030, but their emission ranges become similar in the year 2050.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) have ranked as the deadliest and most financially crippling natural disasters in the United States. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study we apply a fixed-constraint storyline approach that holds storm tracks and initial conditions constant to probe future TC intensity in the North Atlantic Basin. First, we simulate 618 historical TC events using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep-learning intensity model. Next, we apply warming signals derived from eight CMIP6 climate scenarios and rerun each event to explore how intensities respond across scenarios. Finally, we develop an interactive dashboard that allows users to explore individual storm simulations and the scenario-modified environmental drivers. Together, this dataset and tool provide a clear, illustrative way to investigate how TC intensity responds to changes in air-sea state.

Climate Change↗
DOE OSTI2022

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Visual‐Analytics Bridge Complexity and Accessibility for Robust Urban Water Planning

Urban water resources planning is complicated by unprecedented uncertainty in supply and demand. Real‐world planning often simplifies the full range of uncertainty faced by a system into a limited set of deterministic scenarios to enhance accessibility for decision‐makers and the public. However, overlooking uncertainty can expose the system to failures. On the other end of the spectrum, academically developed tools for scenario analysis rigorously quantify the combined effects of multiple sources of uncertainty, but the practical application of these models is limited by the challenges of information visualization and communication of results. In short, municipal water supply planners lack access to planning frameworks that effectively integrate a rigorous treatment of uncertainty with accessible, user‐friendly visual and interactive tools to enhance user accessibility. In this work, we fill this gap by proposing Visual‐Robust Decision Making, and demonstrate an application for the city of Santa Barbara (SB), CA. Santa Barbara faces multiple uncertainties from pending state and federal regulations to changing hydrology and water demand. The city seeks to increase its water portfolio robustness by expanding its seawater desalination plant, but must decide how much capacity to add. We introduce computational tools that assess uncertainty across nine uncertain drivers identified with the help of water planners in SB. To allow public participation in the desalination expansion decision, we develop interactive visual‐analytics to aid decision‐makers and stakeholders in navigating complex scenario analysis outcomes. Our results quantify the tradeoffs between increased capacity and system robustness and aim to enhance participation and uncertainty characterization of urban water planning efforts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

BSM (Bioenergy Scenario Model) 2023 FKA: Biomass Scenario Model [SWR-09-09]

The U.S. Department of Energy's (DOE's) Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.

Bush, Brian↗