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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Novel Charfuel® Coal Refining Process 18 TPD Pilot Plant Project for Co- Producing an Upgraded Coal Product, and Commercially Valuable Co- Products: Area of Interest #3 – Coal Beneficiation Pilot Plant Testing (Final Report)

Operation of Carbon Fuels, LLC’s (“CF”) existing, permitted 18 TPD pilot plant located in Golden, Colorado using two individually ranked (ASTM D 388) coal types (two campaigns), employing the novel Charfuel® coal refining process to produce an upgraded coal product and a number of high-valued organic and inorganic coproducts (for which there presently exists large commercial markets) in order to produce engineering and product data which will then be utilized toward the design of a commercial scale integrated facility (pre-feed document). Carbon Fuels, LLC has developed the Charfuel® Coal Refining Process which refines domestically abundant, raw coal (in the same manner as crude oil is refined) to produce the identical, high value co-products that are refined from crude oil. Thus, gasoline, jet fuel, “green diesel”, fuel oil, and marine fuels, as well as petrochemicals such as benzene, toluene, xylene, and methanol are refined from raw coal using this process. The Charfuel® Coal Refining Process is not a coal conversion process, like pyrolysis, or indirect liquefaction. Nor is it an alternative energy system. Rather it is a coal refining process that has the ability to economically produce products traditionally associated with the refining of crude oil but using only abundant, raw coal as the refinery feed stock. The Charfuel® Coal Refining Process is more economical than crude oil refining and is environmentally benign. Therefore, this value added process yields a return on investment well above 50% for a commercial facility. Furthermore, the Charfuel® process, unlike alternatives such as ethanol and hydrogen, can utilize the existing transportation, delivery, and other petroleum based systems. Hence, there is no need for new engines, pipelines, tankers, or product acceptance. As a result, the profitability of the process is increased. Objectives: (1) Operation of the integrated 18 tpd pilot plant, using two coal types (ranks); (2) Demonstration of process flexibility in being able to produce different products (gas, liquid, and char), as well as determination of operating parameters for identifying scale up criteria for two coal types (ranks); (3) Generation of engineering and design information (process specifications) for use in designing a commercial scale plant (scale-up); (4) Determination of important environmental issues surrounding the process and the products such as fate of trace elements (mercury and other heavy metals) and distributions of SO2, NOx, and CO 2 by analysis of effluent streams; (5) Production of sufficient product to allow reliable commercial economic evaluation of both the refined coal product and the coproducts; and, (6) Assessment of longer-term reliability of unit operations. Period 1: reconfiguration of the 18 TPD plant to meet specific FOA requirements and to qualify the facility for operation; and, Period 2: operation of the 18 TPD plant for two campaigns using two coals types (ranks) which are widely commercially used and abundant - the first being a subbituminous (Powder River Basin (“PRB”)) coal, and the second a bituminous (Illinois #6) coal.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

13 HYDRO ENERGY↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2023

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

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

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A Market Feedback Framework for Improved Estimates of the Arbitrage Value of Energy Storage Using Price-Taker Models

Price-taker (PT) models are often used to assess the potential value or revenue of energy arbitrage opportunities for energy storage in wholesale markets. But as greater amounts of energy storage are deployed on the grid, current PT models fail to predict the effects that energy storage itself can have on market prices. This can lead to an overestimation of the economic value of storage and an inability to capture price suppression. In this paper, we propose the use of a modified PT model to simulate the impact of increased storage deployment on energy prices and the resulting impact on revenue. Our method uses a gradient-boosting regressor to estimate the impact on prices, and we apply our method on historical price data from the PJM and California Independent System Operator wholesale markets. We use this approach to explore possible causes of electricity price suppression that occur from storage capacity additions, which is generally not possible with PT models.

arbitrage↗
DOE OSTI2020

Energy Savings Potential of SSL in Agricultural Applications

A report on the energy-savings potential of solid-state lighting in agricultural applications, which updates a prior edition that focused on horticultural lighting only, with an expanded focus that includes animal lighting. The analysis considered three indoor horticultural lighting categories: lighting supplemented greenhouses, high intensity sole-source farms, and sole-source indoor vertical farms typically growing leafy greens. To characterize animal lighting, the analysis investigated the three largest markets of livestock in the U.S. that use lighting – poultry, swine, and dairy farming. To estimate energy consumption of agricultural lighting, the analysis utilized interviews with lighting manufacturers, growers, utility companies, academic professionals, and industry consultants, as well as various data sources. Each of these resources enabled the determination of total market size and illuminated area, typical lighting configuration, lighting power density for LED and incumbent technologies, operating hours, and the installed lighting technology mix. Among the major findings: 1. If all indoor horticultural lighting today was converted to LED technology, annual horticultural lighting consumption would be reduced to 6.3 TWh of site electricity, or 60 tBtu of source energy, which represents lighting energy savings of 34% or $350 million. 2. If all animal lighting today was converted to LED technology, annual lighting energy consumption for animal production would be reduced to 2.8 TWh site electricity, or 26 tBtu of source energy, which represents lighting energy savings of 25% or $96 million.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

2024 Bioenergy Industry Status Report

This report provides a snapshot of the bioenergy industry status at the end of 2024. The report compliments other annual market reports from the Department of Energy's (DOE's) Office of Energy Efficiency and Renewable Energy (EERE) offices and is supported by DOE'sOffice of Critical Minerals and Energy Innovation's Alternative Fuels and Feedstocks Office. The 2024 Bioenergy Industry Status Report focuses on past year data covering multiple dimensions of the bioenergy industry and does not attempt to make future market projections. There report covers production, consumption, plants, trade, and end-use for all biofuels, biopower, and biobased products where data is available. The report provides a balanced and unbiased assessment of the industry and associated markets. It is openly available to the public and is intended to complement other industry reports with a focus on DOE stakeholder needs.

09 BIOMASS FUELS↗
DOE OSTI2021

Case Study: Field Evaluation of a Low-Cost Circuit-Level Electrical Submetering System

Circuit-level metering technologies provide the ability to monitor individual circuits within an electrical panel in a building, providing detailed power and energy consumption data at a much more granular level than was previously achievable in a cost-effective manner. While the fundamental hardware components of circuit-level—split-core current transformers (CTs) and power monitoring meters—have existed for some time, the new offerings in the market have tightly integrated these components, lower costs, and have streamlined data organization, transport, and access via software solutions accessible through web and application programming interfaces (API). NREL evaluated Meazon’s single-circuit metering system. The evaluation of the equipment under test (EUT) focused on three aspects: (1) accuracy of the data provided by the system, (2) ease of installation of this technology and ease of data integration into existing U.S. General Services Administration (GSA) analytics platforms, and (3) total cost of ownership and cost-effectiveness of the technology. The metering technology was tested in a field deployment where it was installed in low and high-voltage commercial electrical panels in the César E. Chávez Memorial Building in Denver, Colorado. The goals were to assess the metering accuracy, the ease of installation and data retrieval, and the total cost of ownership and cost-effectiveness. This technology proved easy to install by a certified electrician who completed the install of 6 meters in 2 separate panels (120/208 V and 277/480 V) and 2 circuit disconnects (along with associated commissioning) at the César E. Chávez Memorial Building in less than one day. The technology was installed in high- and low-voltage panels and with limited space in the electrical room, demonstrating the applicability of this technology to almost all commercial buildings in the GSA portfolio. Primary considerations for this technology include appropriate sizing of CTs for each circuit, selection of loads/circuits/panels that are of high value for detailed submetering (e.g., tailored for high-load devices), and how GSA would like to integrate these data with its existing energy management infrastructure. Through the field evaluation, we demonstrated data integration from the vendor system into the primary software component on the GSA enterprise-level energy management and information system, GSA Link. Data from the circuit-level metering were integrated into this platform and used to perform FDD. This demonstrated the ability of the EUT to augment existing energy management and information systems in buildings where GSA Link is deployed and provides a pathway for delivering FDD at other buildings throughout the portfolio.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Classical versus quantum models in machine learning: insights from a finance application

Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from real-world applications is one of the key milestones towards a quantum ready era. Here, we take a first step towards addressing this challenge by performing a comparison of the widely used classical ML models known as restricted Boltzmann machines (RBMs), against a recently proposed quantum model, now known as quantum circuit Born machines (QCBMs). Both models address the same hard tasks in unsupervised generative modeling, with QCBMs exploiting the probabilistic nature of quantum mechanics and a candidate for near-term quantum computers, as experimentally demonstrated in three different quantum hardware architectures to date. To address the question of the performance of the quantum model on real-world classical data sets, we construct scenarios from a probabilistic version out of the well-known portfolio optimization problem in finance, by using time-series pricing data from asset subsets of the S&P500 stock market index. It is remarkable to find that, under the same number of resources in terms of parameters for both classical and quantum models, the quantum models seem to have superior performance on typical instances when compared with the canonical training of the RBMs. Our simulations are grounded on a hardware efficient realization of the QCBMs on ion-trap quantum computers, by using their native gate sets, and therefore readily implementable in near-term quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.

Peng, Hongwu↗
DOE OSTI2021

CO2ROpt (Feedstock Allocation Optimization Model) [SWR-22-13]

Reducing greenhouse gas emissions remains a critical challenge for the transportation and chemical sectors. The electrochemical reduction of carbon dioxide is a potential pathway for production of fuels and chemicals that uses atmospheric carbon dioxide as a feedstock. Here we present an analysis of the potential for carbon dioxide captured from point sources and via direct air capture to be utilized in electrochemical reduction under different market scenarios. Using a numerical optimization framework and national data sets, scenarios for nationwide production of ethylene, formate, and carbon monoxide are evaluated at scales comparable to current production of these molecules in the United States. We show that developing a network for production of these products at scale requires capture and utilization of significant portions of the carbon dioxide that is currently emitted from large stationary point sources. Because carbon dioxide point sources are spatially and compositionally variable, their use for carbon dioxide reduction depends on electricity prices, capture cost, and location. If the power sector in the United States is decarbonized, carbon dioxide supply decreases significantly, increasing the importance of utilizing other carbon dioxide streams, and increasing the likelihood that direct air capture plays a role in supplying carbon dioxide feedstocks. These findings provide a first insight into the feedstock and energy constraints for carbon dioxide reduction.

Badgett, Alex↗
DOE OSTI2026

Hybrid Power Purchase Agreements for Flexible 24/7 Energy Delivery – A Comprehensive Review of Current Practices and Research Pathways

Power Purchase Agreements (PPAs) are becoming increasingly preferred among large energy consumers, such as data centers, to secure cost-effective energy and meet accelerating demand growth. Traditionally, variable renewable energy (VRE)-based PPAs operate on a pay-as-produced basis, balancing supply and demand for a relatively longer duration (e.g., annually). However, the focus is shifting toward matching supply and demand on an hourly basis to fully meet energy needs. This shift requires the integration of flexible energy resources, such as hydropower, thermal generation, and energy storage, to complement VRE sources like wind and solar, forming the foundation for 24/7 PPA. This work contributes by: (i) reviewing emerging market trends and current practices in PPA procurement, supported by data on PPA prices and technology portfolios; (ii) synthesizing the existing literature on modeling approaches for contract pricing, quantities, hybrid resource procurement, and risk management in 24/7 PPA design, while identifying key research gaps; and (iii) proposing an integrated 24/7 PPA design framework along with two contracting mechanisms from the perspectives of both PPA providers and consumers. The proposed framework highlights critical modeling challenges, risk-allocation issues, and future research opportunities for 24/7 PPA design.

24/7↗
DOE OSTI2021

Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems (Final Report)

This is the end of project report for the DOE Project DE-FE-0027844 “Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems”. The objectives of the project were to develop several technologies required for the mass adoption of Solid Oxide Fuel Cell systems for medium to large power generation applications. The key achievements of this project were: 1) Development of fuel cells with complete internal fuel reforming capability. 2) Fuel cell power scale up with production of cells with larger active areas. 3) Assembly of an integrated 5 kW modular stack platform capable of installation into medium to large power systems. 4) Demonstration of a 10 kW Fuel Cell Power System utilizing the integrated 5 kW modular stack platform. 5) Cost modelling to show system cost of $1,500/kW are achievable at production volumes. 6) Development of cathode air poison getters for reduced degradation and extended fuel cell life. 7) Assessment of fuel poison on anode degradation, particularly poisons frequently produced from the process of gasification of coal. The Cummins led Voice of Customer (VOC) work identified several significant markets for natural gas powered fuel cell systems, where the end customer is interested in reducing energy consumption and emissions, or increasing reliability of electrical supply. Data centers and truck Auxiliary Power Units (APU) were highlighted as 2 key future markets for fuel cell systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Streamlining Energy Sprawl: Assessment of Geothermal Impacts on Public Lands

Renewable energy power generation, including geothermal, solar photovoltaics (PV), and wind, can have significant land use impacts depending on the technology and the size of the facility. At the same time, innovations across these renewable technologies are working to improve plant efficiency per acre of disturbance. Through a survey of satellite imagery and other sources, this analysis reevaluates the impact of geothermal power generation and examines the variations in land use for different geothermal technologies (binary, flash, and dry steam). Geothermal operations on Bureau of Land Management (BLM) sites serve as the focus for this analysis - i.e., only active sites with federal mineral ownership for which the BLM receives royalties from power production. The results show significant variation in direct land disturbance across the operations sites and across these three geothermal technologies. Although there is variation across plants within the same geothermal technology category, dry steam plants (The Geysers) were shown to have the lowest impact, in acres per megawatt capacity and acres per gigawatt-hour generation, followed by flash steam and binary plants. Overall, direct land impacts relative to capacity and generation have declined 16-27% for facilities constructed since 2000 as compared to those constructed prior, even though most new facilities are binary plants (which traditionally had higher land disturbance). Additionally, capacity factors based on actual net production data were found to average only 50% across all operations sites, suggesting that previous estimates based on nameplate capacity only may lead to an undercounting of generation-based land disturbance metrics. Should the geothermal industry capture additional market share, improving land disturbance metrics through accurate net production and capacity data and further minimizing impacts will be increasingly important as competition over land use intensifies.

GEOTHERMAL ENERGY↗
DOE OSTI2021

U.S. Hydropower Market Report (January 2021 edition)

This is the third complete edition of the U.S. Hydropower Market Report (the first two were the 2014 and 2017 Hydropower Market Report published in 2015 and 2018, respectively). In intervening years between publishing the full report, updated data are also summarized and released, and can be found at the Oak Ridge National Lab (ORNL) HydroSource website. This report combines data from public and commercial sources as well as research findings from other Department of Energy (DOE) R&D projects in order to provide a comprehensive picture of developments in the U.S. hydropower and PSH fleet and industry trends. Prior to the first Market Report being published, there was a noted lack of publicly available and easily accessible information about hydropower in the United States and other important trends affecting this important sector of the energy industry. New and valuable types of information are constantly being developed in the course of DOE research activities and, in a rapidly evolving energy industry, it is important that these data be made available in a predictable and consistent manner for use by all different types of stakeholders and decision-makers.The report highlights developments in 2017–2019 (the years for which new data has become available since the publication of the 2017 Hydropower Market Report), and contextualizes this information compared to evolving high-level trends over the past 10–20 years. Apart from presenting trends over time, the report discusses differences in those trends by region, plant size, owner type, or other attributes.

13 HYDRO ENERGY↗
DOE OSTI2021

Practice Consolidation Among U.S. Radiation Oncologists Over Time

Health care practices across the United States have been consolidating in response to various market forces. The degree of practice consolidation varies widely across specialties but has not been well studied within radiation oncology. This study used Medicare data to characterize the extent of practice consolidation among radiation oncologists and to investigate associated market factors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Case Study: Hybrid Carbon Conversion Using Low-Carbon Energy Sources in Coal-Producing States

The demand for more carbon efficient power sources and a decrease in natural gas prices has decreased the desire for coal power. This decrease in demand has led to massive job losses in coal mining regions over the past decade. The purpose of this project is to develop a hybrid energy system utilizing both a coal power plant and advanced reactor, which is competitive with natural gas by improving on profitability and decreasing carbon emissions. This report details the problem with a summary of the impact on the coal industry and the availability of renewable energy sources in the Appalachian region. Because of the geography of the region, variable renewable energy sources are not available without significant size and siting restrictions. However, biomass in the form of wood waste is abundant and can be used as a carbon neutral energy source. Combining biomass and coal processing, in addition to thermal power plants, can increase system profits and efficiency by providing peaking power and conversion opportunities for secondary markets. The electric load is based on publicly available demand data from Appalachian Power, which services the western Virginia and southern West Virginia in the Appalachian region. The demand information is combined by service, normalized, and scaled to an average demand of 1000 kW, which will be the basis for sizing the hybrid energy system. A traditional screening curve analysis for a coal plant and advanced reactor shows that the least cost design varies significantly based on the assumed discount rate and capital recovery period. An optimization program to size the design in TEAL based on the load curve gives 10 optimal designs, all with a negative resulting net present value (NPV) and a coal plant capacity of less than 15%. Including profits from selling captured carbon at a flat rate results in a positive NPV; however, the coal capacity factor only increases to about 40%. There are limitations with this optimization as well since the price of CO2 is likely to decrease as more is sold to the conversion market. The suggested design will combine coal power, an advanced reactor, and coal and biomass coprocessing to produce a variety of products that can be sold to the conversion market while increasing system efficiency. The analysis of conversion pathways for coal and biomass reveals that multiple options will need to be included in the analysis to produce the optimal system design. Three systems will be optimized and compared to determine the best design based on the figures of merit of total NPV and cost of carbon avoided. The first system will include a coal power plant and an advanced reactor that will sell electricity to the grid to meet demand and sell captured carbon to the conversion market. The second system adds a high-temperature steam electrolysis plant, which will utilize electricity during times of low demand to produce hydrogen and sell it to the conversion market. The third system adds biomass and coal processing with options for hydrocarbon oils, syngas to be produced for the conversion market, and electricity generation to power components within the system or provide peaking power. This analysis will be based on a new approach that combines traditional screening curve methods with a dispatch algorithm that optimizes the system based on the opportunity cost of different production options. The resulting optimization algorithm should provide results with less processing time than HERON’s decision tree method. The results from this analysis will determine an optimal design and reinforce the benefits of coal power when used in a hybrid energy system. The initial results show that the addition of a secondary market for carbon sales could result in a positive NPV and increases the capacity factor of the coal plant as compared to a design with only sales to the electricity market. The addition of more markets and additional coal consumption from biomass coprocessing could increase NPV further, replace carbon in other markets through the sale of biomass-derived hydrocarbons, and demonstrate the value of coal power technology.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Community Solar Program Design Considerations & Modeling Inputs

Designing and modeling a community solar (CS) program is a complex process with numerous variable inputs that are interconnected. Modeling a CS program can be useful to inform the programs design itself while also providing stakeholders of all types with information. Accurate data inputs and assumptions are key to ensuring that a model is informative and as representative of real market conditions as possible. This report is an exploration of community solar program modeling considerations, especially as it relates to data inputs such as capital costs, administrative fees, and subscription size, using a CS program in North Carolina as a case study.

14 SOLAR ENERGY↗