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DOE OSTI2023

Marine Alternative Fuel Pricing, Supply, and Demand

The International Maritime Organization low sulfur fuel rules and greenhouse gas-reduction strategy may create a significant perturbation to global refinery operations, impacting volumes and prices of marine fuel globally and within the United States and incentivizing the transition to alternative fuel use. The goals of this project are to (1) enhance understanding of how very low-sulfur fuel-primarily fuel oil and diesel-and low carbon fuel requirements and promising biofuel processes will affect the marine fuel supply chain; and (2) explore how these perturbations interact with and impact indicators such as pricing, number of trips, and demand behavior, along with the potential to meet low sulfur and carbon fuel demand with biofuel supply chains, (3) merge innovative thinking in the area of marine fuels within research centers of DOE (NREL) and the U.S. Department of Transportation (Volpe). These goals will be accomplished through the combination of detailed refinery, marine fuel burn, system dynamics, and geospatially explicit linear programming models.

biofuel↗
DOE OSTI2023

Enabling Floating Solar Photovoltaic (FPV) Deployment: FPV Technical Potential Assessment for Southeast Asia

Southeast Asia (SE Asia) is a region with growing energy demand and increasing development of floating solar photovoltaic (FPV) systems, which can help meet countries' renewable energy and energy security goals. This study uses a high-level geospatial assessment methodology to estimate the technical potential for monofacial and bifacial FPV on reservoirs and natural waterbodies in the ten countries within the Association of Southeast Asian Nations (ASEAN). Technical potential consists of the suitable waterbody area for FPV development (km2), the capacity of FPV that could be installed on this suitable area (MW), and the annual energy that could be generated from these installations (GWh/year). This first-of-its-kind FPV technical potential assessment for SE Asia can help policymakers and planners better understand the role that FPV could play in meeting regional energy demand and can ultimately guide investment decisions. Although this work focuses on SE Asia, the methodology may also be applicable for countries in other regions, with adaptations. The FPV technical potential results are also integrated into the Renewable Energy (RE) Data Explorer online tool (https://www.re-explorer.org/).

bifacial↗
DOE OSTI2023

Industry Facing PV Degradation Prediction Tool and Database to Enable a 50-Year Life Module

The Photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 years. Furthermore, research efforts aim for up to 50 years of service life while keeping performance degradation at a minimum for decades of outdoor weathering - putting additional pressure on improving the accuracy of long-term durability and reliability assessments. Here there is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. A graphical user interface will aid in the understanding of the results. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database to perform geospatial degradation analysis in the US and worldwide. Our repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry.

degradation↗
DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Multi-scale, Geo-data Science Method for Assessing Unconventional Critical Mineral Resources

Critical minerals (CM) supply raw materials that constitute many of our essential infrastructure, defense, technology, and electrification needs. Currently, production and refinement of these materials from conventional sources is limited to few regions globally, which makes supply of these resources particularly venerable to disruption. To help overcome these risks and meet growing demand, attention has focused on identifying and developing resource potential of unconventional geologic CM sources, such as rare-earth elements in sedimentary systems. However, the unconventional nature of these types of sources means they are often poorly characterized and/or under-explored with respect to conventional counterparts. We present a regional case study for an Unconventional Rare-earth and Critical minerals (URC) assessment method for predicting and identifying REE resource potential and occurrence in unconventional systems in the Central Appalachian Basin (CAB). The method utilizes a geologic and geospatial data-driven approach, informed and guided by knowledge of REE enrichment processes, to systematically predict and identify areas of higher enrichment. Results from the test case indicate locations with potential for different types of coal-REE deposits, demonstrating its utility for reducing the area of exploration and identifying sites for more detailed investigation. Building upon the regional scale assessment capability, ongoing science-based enhancements to the method will allow for finer-scale (e.g., mine-scale) predictions required to support technical and economic assessments.

Creason, Christopher↗
DOE OSTI2023

Utilizing GEOPHIRES-X Beyond Electricity

The GEOPHIRES tool is a techno-economic simulator for evaluating the thermal performance and cost-competitiveness of geothermal plants for electricity, heating, and/or cooling. The tool combines reservoir, wellbore, and surface plant cost and performance models to estimate overall techno-economic metrics such as net present value or levelized cost of electricity, heating, or cooling. We recently upgraded the tool to an object-oriented Python framework, presented in an accompanying paper. As part of the upgrade, we enhanced the capability to simulate the performance of geothermal plants for heating and cooling, which is the topic of this paper. Specifically, we (1) integrated absorption chillers to investigate the performance of utilizing geothermal heat for cooling, (2) integrated a heat pump module to boost the geothermal temperature and thermal output, (3) integrated a district heating module to estimate heating demand for a district based on local weather data, and simulated heat supply with geothermal energy and peaking boilers, and (4) integrated GEOPHIRES as an engine in the dGeo simulator to perform a geospatial analysis of geothermal district heating feasibility across a large region (e.g., a state or the entire United States) utilizing resource and thermal demand maps. This paper presents background information and case studies for several of these heating and cooling end-use options in GEOPHIRES.

absorption chiller↗
DOE OSTI2020

Status Update on TRAC: A DOE-EM Tool for Tracking Groundwater Cleanup and Progress Toward Site Closure - 20245

The U.S. Department of Energy (DOE) Office of Environmental Management (EM) uses a customized, web-based mapping tool called TRAC (Tracking Restoration and Closure) to communicate information on plume sizes, remedial approaches, regulatory drivers, exit strategies, and long-term stewardship requirements at all sites within the DOE-EM complex. The web-based GIS story maps provide robust geospatial visualization of plumes at DOE sites using an intuitive interface that allows users to explore the plume maps, explanatory text, photographs, and video. This collection of story maps not only communicates information for each individual site, but also summarizes pertinent metrics on cleanup and remaining contaminants across all EM sites. This paper describes a new design within TRAC for communicating metrics on plume status, regulatory cleanup progress, and technology implementation. A principal benefit of TRAC is the ability to view individual pieces of data at a time, permitting targeted questions to be addressed, such as the status of regulatory decisions, site cleanup priorities, and site closure needs for each site within the DOE-EM complex. TRAC manages communication and supports decision-making through knowledge access, data and information transparency and traceability, and inclusive participation. It serves as a common resource that provides consistent information for DOE managers, site personnel, regulators and stakeholders. Because long-term stewardship of legacy waste sites requires ongoing coordination and communication among DOE, regulators, and stakeholders, TRAC can also be used to help transition EM sites to the Office of Legacy Management at site closure. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗
DOE OSTI2023

Progress in the Verification and Validation Efforts for START: A Spent Fuel Routing Tool

The U.S. Department of Energy’s (DOE) Office of Nuclear Energy (DOE-NE) is planning for an integrated waste management approach to transport, store, and dispose of spent nuclear fuel (SNF) and other high-level radioactive waste (HLW) as part of the Integrated Waste Management (IWM) program [1]. In support of this effort, the Stakeholder Tool for Assessing Radioactive Transportation (START) is being developed within the IWM program [2, 3]. This is a web-based decision support tool that can be used to analyze geospatial data related to the transportation of SNF and HLW. START is designed as a web-based application using an ArcGIS server through which the user can select the origin and destination of the route [4]. This is followed by selection of the mode of transportation of choice based on user preference. Some of the modes available to the user include rail, heavy haul truck, and barge. The option of utilizing more than one mode of transportation (intermodal transportation) is also implemented in START. A few examples of intermodal transportation include barge to rail, and heavy haul truck to rail. It must be noted that some routes might not have access to all modes of transportation, depending on the infrastructure availability at the origin and destination sites. Users can also select any stops or barriers they would like to introduce in the routes. This is followed by selection of the routing criteria of interest. Three primary routing options available to the user include minimum population, minimum distance, and minimum time. Apart from that, a few other options include accounting for a weighted average of the three aforementioned routing options. The next step involves the selection of the buffer distance of interest which includes the two choices available of 800 m and 2500 m, respectively. Finally, an option to select any prohibited rail carriers that the user does not wish to use is available. After making these selections, a route is ready to be created.

Gadey, Harish Reddy↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗
DOE OSTI2024

The "PVLib" of Degradation: PVDeg

The Photovoltaic (PV) industry constantly aims for lower costs through higher-efficiency cells, improved module designs, and improvements in durability. This leads to the use of new materials, designs, and manufacturing processes, and not always with a sufficient amount of durability testing. To help drive down costs there is a desire to create modules that will last for up to 50 years of service life. To accomplish this, every degradation mode and mechanism must be identified and either eliminated or otherwise mitigated. This involves the extrapolation of laboratory results to the field conditions. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. While the basic equations used to model degradation are sometimes very simple, the full analysis involves calculations are cumbersome but ubiquitous for many degradation processes. A simplified, modeling framework to accomplish these repetitive processes will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this talk, we will describe our progress creating the open-source tool PVDeg. This tool can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. PVDeg simplifies many of the common foundational computational operations for obtaining meteorological data and using it to generate a model of the PV deployment. This prediction tool repository also contains various degradation models as well as a library of material parameters suitable for estimating the durability assessment of materials and components. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database, and other weather sources, to perform geospatial degradation analysis in the US and worldwide. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry. During the talk, we will provide the PVPMC attendees the opportunity to interact with the tool via a Google Collab tutorial they can run on their phones or laptops.

durability↗
DOE OSTI2024

OpenPATH - Leveraging Technology to Measure Travel Behavior

Shifting transportation to more sustainable modes is a key piece of the decarbonization puzzle. However, mobility behavior and travel patterns are difficult to influence because they are difficult to measure. OpenPATH provides a tool to capture longitudinal behaviors through a smartphone application. Agencies interested in gathering data about a population's travel behavior can set up a deployment of the app customized to the needs of their community. Partners can choose between simple mode and purpose labels or surveys for each trip to balance the level of user engagement with the associated burden. The labels, trip surveys, and an initial demographic survey can all be tailored to the specific context of the deployment. The OpenPATH tool is unique in its open-source nature, ability to gather detailed longitudinal travel data, and design allowing direct engagement with travelers. A valuable technological advancement, this tool enables partners to measure the way changes in the transportation landscape impact their community. The suite of tools includes both public and administrator dashboards. The public dashboard supports continuous data analysis through charts presenting trip information updated daily. The administrator dashboard displays geospatial data and supports data export. Example applications have included e-bike programs; gathering valuable metrics on increased access to opportunities and reduction in VMT, and studies aimed at understanding existing mobility behavior to see where advancements such as electric vehicles could fit into these habits. OpenPATH collects travel data in association with an initial demographic survey, enabling detailed insight into the behavior patterns or impact of a certain program on different populations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗
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 OSTI2024

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop: Preprint

Geothermal cost and performance evaluation implemented via technoeconomic assessment (TEA) modeling is critical for the Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for the Annual Technology Baseline (ATB). The ATB data are inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL’s reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the US generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on technoeconomic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next generation technologies such as closed loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

Annual Technology Baseline↗
DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗
DOE OSTI2025

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop

Geothermal cost and performance evaluation implemented via techno-economic assessment (TEA) modeling is critical for the U.S. Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for NREL's Annual Technology Baseline (ATB), which provides inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL's reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the U.S. generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on techno-economic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next-generation technologies such as closed-loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

annual technology baseline↗