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DOE OSTI2026

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Quantifying market volume sensitivity to material property modifications in polyhydroxybutyrate: A parametric analysis approach

Polyhydroxybutyrate (PHB), a biodegradable biopolymer, represents a promising alternative to petroleum-based thermoplastics. However, despite consistent market growth, PHB faces persistent commercialization challenges that limit widespread adoption. Existing research has focused predominantly on optimizing PHB production processes, leaving a critical gap in understanding which material property modifications would most effectively enhance market competitiveness. This study addresses this gap by systematically analyzing the relationship between polymer material properties and market performance using U.S. market data from 2008 to 2021 for 21 thermoplastic polymers across 19 material properties. We employed principal component regression to identify property modifications that could maximize market volume while reducing CO 2 emissions. Our parametric analysis revealed that two specific material properties – Hardness Shore A and Sheet Extrusion Temperature – significantly influence PHB marketability across different price points. Market simulations demonstrated that a 10% increase in Hardness Shore A could increase PHB market volume by 431.5 million kg while reducing emissions by 188.7 kg CO 2 . A similar 10% increase to Sheet Extrusion Temperature could yield a 297.5 million kg volume increase and a 99.2 kg CO 2 reduction in emissions. Critically, this approach is agnostic to the specific methods required to achieve these property changes, instead providing material scientists with quantitative, data-driven targets for R&D prioritization. Here, this framework offers a novel methodology for evaluating biopolymer competitiveness and supporting strategic decisions to accelerate PHB market adoption and contribute to decarbonization of the plastics industry.

09 BIOMASS FUELS↗

DOE OSTI2026

A Systematic Review and Integrated Approach to Modeling of Aging Utility Scale PV Systems

The growing deployment of utility-scale photovoltaic (PV) systems has increased the importance of techno-economic modeling operational photovoltaic (PV) systems for predicting energy yield, optimizing asset management, and informing financial decisions. Through a systematic review of literature and current industry practices, we review the different common modeling practices of a system's configuration and age, performance and degradation, operation and maintenance (O&M), while also focusing on specific considerations for repowering, revamping, and decommissioning. Building on the synthesis, we develop a structured framework for techno-economic modeling of operating PV systems that integrate performance and degradation analysis, a decommissioning and repowering cost model that estimates the system's end-of-life costs to reduce uncertainty quantifications and improve consistency across the sector. This research contributes to improved modeling methodologies and potentially to reduced financial performance requirements by providing practitioners with input resources and practical approaches to estimate performance, degradation, and costs associated with continued operation, revamping, repowering, or decommissioning decisions.

14 SOLAR ENERGY↗

DOE OSTI2014

Regional Analysis of Building Distributed Energy Costs and CO2 Abatement: A U.S. - China Comparison

The following paper conducts a regional analysis of the U.S. and Chinese buildings? potential for adopting Distributed Energy Resources (DER). The expected economics of DER in 2020-2025 is modeled for a commercial and a multi-family residential building in different climate zones. The optimal building energy economic performance is calculated using the Distributed Energy Resources Customer Adoption Model (DER CAM) which minimizes building energy costs for a typical reference year of operation. Several DER such as combined heat and power (CHP) units, photovoltaics, and battery storage are considered. The results indicate DER have economic and environmental competitiveness potential, especially for commercial buildings in hot and cold climates of both countries. In the U.S., the average expected energy cost savings in commercial buildings from DER CAM?s suggested investments is 17percent, while in Chinese buildings is 12percent. The electricity tariffs structure and prices along with the cost of natural gas, represent important factors in determining adoption of DER, more so than climate. High energy pricing spark spreads lead to increased economic attractiveness of DER. The average emissions reduction in commercial buildings is 19percent in the U.S. as a result of significant investments in PV, whereas in China, it is 20percent and driven by investments in CHP. Keywords: Building Modeling and Simulation, Distributed Energy Resources (DER), Energy Efficiency, Combined Heat and Power (CHP), CO2 emissions 1. Introduction The transition from a centralized and fossil-based energy paradigm towards the decentralization of energy supply and distribution has been a major subject of research over the past two decades. Various concerns have brought the traditional model into question; namely its environmental footprint, its structural inflexibility and inefficiency, and more recently, its inability to maintain acceptable reliability of supply. Under such a troubled setting, distributed energy resources (DER) comprising of small, modular, electrical renewable or fossil-based electricity generation units placed at or near the point of energy consumption, has gained much attention as a viable alternative or addition to the current energy system. In 2010, China consumed about 30percent of its primary energy in the buildings sector, leading the country to pay great attention to DER development and its applications in buildings. During the 11th Five Year Plan (FYP), China has implemented 371 renewable energy building demonstration projects, and 210 photovoltaics (PV) building integration projects. At the end of the 12th FYP, China is targeting renewable energy to provide 10percent of total building energy, and to save 30 metric tons of CO2 equivalents (mtce) of energy with building integrated renewables. China is also planning to implement one thousand natural gas-based distributed cogeneration demonstration projects with energy utilization rates over 70percent in the 12th FYP. All these policy targets require significant DER systems development for building applications. China?s fast urbanization makes building energy efficiency a crucial economic issue; however, only limited studies have been done that examine how to design and select suitable building energy technologies in its different regions. In the U.S., buildings consumed 40percent of the total primary energy in 2010 [1] and it is estimated that about 14 billion m2 of floor space of the existing building stock will be remodeled over the next 30 years. Most building?s renovation work has been on building envelope, lighting and HVAC systems. Although interest has emerged, less attention is being paid to DER for buildings. This context has created opportunities for research, development and progressive deployment of DER, due to its potential to combine the production of power and heat (CHP) near the point of consumption and delivering multiple benefits to customers, such as cost

25 ENERGY STORAGE↗

DOE OSTI2016

Heart of the Hearth: Making the Popular Clean, Not the Clean Popular - Technology Research, Development, and Tools for Clean Biomass Cookstoves

This technical report summarizes the work completed by BioLite in fulfilment of the US DOE EERE award. The work plan focused on three key objectives: developing an optimized combustion system that demonstrates high combustion efficiency and low PM 2.5 and CO emissions, integrate the system into popular stove phenotypes – side-fed rocket stove architecture like the BioLite HomeStove, and the Patsari chimney stove in Mexico such that they maintain their important phenotypical characteristics, independently evaluate quantitative fuel and emissions performance of the integrated ‘Turbo-Patsari’ in Mexican households. The project activities were organized into six major tasks: A. Develop, fabricate, and test proof-of-concept prototypes B. Develop field prototypes, assess user feedback and field performance C. Define revised stove design for pre-production model, Identify manufacturing requirements and estimated cost to build, Conduct reliability, emissions, and performance testing of pre-production Turbo-Patsari D. Build pre-production Turbo-Patsari stove combustion cores E. Conduct pre-production field trials F. Summarize field trial results and evaluate Turbo-Patsari for potential volume production. A two-pronged approach was adopted for the above tasks. The first involved building a modular test platform that allowed parametric variation of multiple stove design parameters that directly affect its performance – heat output, thermal efficiency, and emissions. The second part of the approach comprised of building a surrogate Patsari based on GIRA’s specifications that could then be modified or retrofitted for optimum performance based on the learnings from the modular test platform. The following sections of the report will describe the findings of tests on these platform, the subsequent development, design, and installation of the Turbo-Patsari, and finally the in-home field trial.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach

Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables that are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Developing a predictive model for the chemical composition of soot nanoparticles

In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed a series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Evaluation of Rail Decarbonization Alternatives: Framework and Application

The Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework is a comprehensive industry-oriented tool for simulating the deployment of new energy technologies including biofuels, e-fuels, battery-electric, and hydrogen locomotives. By classifying fuel types into two categories based on deployment requirements, the associated optimal charging/fueling facility location and sizing problem are solved with a five-step framework. Life-cycle analysis (LCA) and techno-economic analysis (TEA) are used to estimate carbon reduction, capital investments, cost of carbon reduction, and operational impacts, enabling sensitivity analysis with operational and technological parameters. Here, the framework is illustrated on lower-carbon drop-in fuels as well as battery-electric technology deployments for the US Eastern and Western Class I railroad networks. Drop-in fuel deployments are modeled as admixtures with diesel in existing locomotives, while battery-electric deployments are shown for varying technology penetration levels and locomotive ranges. When mixed in a 50% ratio with diesel, results show biodiesel’s capacity to reduce emissions at 36% with a cost of $\$$ 0.13 per kilogram of CO 2 reduced, while e-fuels offer a potential reduction of 50% of emissions at a cost of $\$$ 0.22 per kilogram of CO 2 reduced. Battery-electric results for 50% deployment over all ton-miles highlight the value of future innovations in battery energy densities as scenarios assuming 800-mi range locomotives show an estimated emissions reduction of 46% with a cost of $\$$ 0.06 per kilogram of CO 2 reduced, compared with 16% emissions reduction at a cost of $\$$ 0.11 per kilogram of CO 2 reduced for 400-mi range locomotives. The NUFRIEND framework provides a systematic method for comparing different alternative energy technologies and identifying potential challenges and benefits in their future deployments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Using Dynamic Line Rating to Enhance Flexibility of Wind-involved Hybrid Energy System

By providing real-time overhead line ampacity, Dynamic Line Rating (DLR) is a potential technique to solve the transmission congestion issues that are threatening NPPs, especially when a nuclear plant is coupled with wind turbine generators. First, DLR can utilize the concurrent cooling effect and dynamic monitors on transmission lines to increase the line carry capacity and reduce congestion. Second, more frequent interactions between transmission lines and operators can increase the flexibility and create a more dynamic and responsive hybrid system. In general, DLR can increase the flexibility and optimize the efficiency of a hybrid system utilizing wind energy. DLR helps meet continuously fluctuating electricity demands with real-time observation of transmission lines for more accurate estimation of line carry capacity. For the fuel energy, DLR can contribute greatly in saving fuels and reducing carbon emission by providing timely information for adjusting generation levels. For HES's including NPPs, transmission congestion could be more serious and needs attention. Therefore, by providing additional transmission capacity, DLR can contribute to alleviate the congestion and congestion pricing impact that is threatening the current NPPs and prevent potential congestion that may constrain the operation of future hybrid system involved with a nuclear plant. However, either a fuel plant or nuclear plant is better coupled with a wind turbine to take advantage of the concurrent cooling effect and dynamic monitoring. The research team at Idaho National Laboratory, currently focused on the benefit of DLR technology to assist wind generation, is now exploring the feasibility of implementing the same technology into such HES and NPP. Future work will focus on the analysis of technique feasibility, challenges and benefit-cost analysis of integrating DLR into HES. Detailed studies are anticipated to evaluate the benefits of DLR on HES and NPP with transmission constraints. This paper explores a potential utilization of DLR in hybrid energy systems, especially those involving traditional fossil fuels or nuclear power coupled with wind energy. The goal is to increase the efficiency and flexibility of these types of hybrid systems in order to provide more secure, reliable and low-carbon energy.

17 WIND ENERGY↗

DOE OSTI2023

Improving the Quality of Geothermal Data Through Data Standards and Pipelines Within the Geothermal Data Repository: Preprint

For machine learning outputs to be applicable to real world problems, high quality data are needed to ensure high quality results. With the more recent emphasis on machine learning in geothermal, there is an increasing need for greater focus on the quality of the data available for use in these projects. For example, Geothermal Operational Optimization Using Machine Learning (GOOML) utilized large quantities of geothermal power plant operational data to inform power plant operational configurations to maximize power generation. High quality datasets result from dependable sensors or devices collecting data, high frequency of measurements, sufficient data points, adequate metadata, reliable storage of data, and sufficient data curation. Another component that contributes to high quality data is reusability, which can be enhanced through data standardization. Data Standardization creates consistency in formatting and contents of like datasets, lessening preprocessing requirements and ensuring adequate information provided by a given dataset. The Geothermal Data Repository (GDR) aims to help improve data quality through automated data standardization for high-value datasets through the implementation of data pipelines alongside reliable and accessible long-term storage for datasets. As such, the GDR has decided to shift away from recommending the use of Excel-based content models and towards the implementation of automated data pipelines. This takes the burden of data standardization off the user and project team and will increase the availability of standardized geothermal data available through the GDR. A set of recommendations, or a data standard for each data type will exist with each data pipeline in order to advise data collection for maximum usability for future research. This paper serves to describe the GDR's proposed transition towards data standardization through automated data pipelines, to discuss the need for and value of such a shift, and to call for suggestions from the community regarding the most useful data standards and pipelines.

data↗

DOE OSTI2016

Optimizing the Costs of Solid Sorbent-Based CO 2 Capture Process Through Heat Integration

The focus of this project was the ADAsorb™ CO 2 Capture Process, a temperature-swing adsorption process that incorporates a three-stage fluidized bed as the adsorber and a single-stage fluidized bed as the regenerator. ADAsorb™ system was designed, fabricated, and tested under DOE award DEFE0004343. Two amine-based sorbents were evaluated in conjunction with the ADAsorb™ process: “BN”, an ion-exchange resin; and “OJ”, a metal organic framework (MOF) sorbent. Two cross heat exchanger designs were evaluated for use between the adsorber and regenerator: moving bed and fluidized bed. The fluidized bed approach was rejected fairly early in the project because the additional electrical load to power blowers or fans to overcome the pressure drop required for fluidization was estimated to be nominally three times the electrical power that could be generated from the steam saved through the use of the cross heat exchanger. The Energy Research Center at Lehigh University built and utilized a process model of the ADAsorb™ capture process and integrated this model into an existing model of a supercritical PC power plant. The Lehigh models verified that, for the ADAsorb™ system, the largest contributor to parasitic power was lost electrical generation, which was primarily electric power which the host plant could not generate due to the extraction of low pressure (LP) steam for sorbent heating, followed by power for the CO 2 compressor and the blower or fan power required to fluidize the adsorber and regenerator. Sorbent characteristics such as the impacts of moisture uptake, optimized adsorption and regeneration temperature, and sensitivity to changes in pressure were also included in the modeling study. Results indicate that sorbents which adsorb more than 1-2% moisture by weight are unlikely to be cost competitive unless they have an extremely high CO 2 working capacity that well exceeds 15% by weight. Modeling also revealed that reductions in adsorber pressure drop could negatively affect the CO 2 adsorption characteristics for sorbents with certain isobar adsorption characteristics like sorbent BN. Thus, reductions in pressure drop do not provide the efficiency benefits expected. A techno-economic assessment conducted during the project revealed that without heat integration, the a metal organic framework (MOF) sorbent used in conjunction with the ADAsorb™ process provided the opportunity for improved performance over the benchmark MEA process. While the addition of a cross heat exchanger and heat integration was found to significantly improve net unit heat rate, the additional equipment costs required to realize these improvements almost always outweighed the improvement in performance. The exception to this was for a supported amine sorbent and the addition of a moving bed cross heat exchanger alone or in conjunction with waste heat from the compressor used for supplemental regenerator heating. Perhaps one of the most important points to be drawn from the work conducted during this project is the significant influence of sorbent characteristics alone on the projected COE and LCOE associated with the ADAsorb™ process, and the implications associated with future improvements to solid sorbent CO 2 capture. The results from this project suggest that solid sorbent CO 2 capture will continue to see performance gains and lower system costs as further sorbent improvements are realized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Global Value Chain and Manufacturing Analysis on Geothermal Power Plant Turbines

The global geothermal electricity market has significantly grown over the last decade and is expected to reach a total installed capacity of 18.4 GWe in 2021 (GEA, 2016). Currently, geothermal project developers customize the size of the power plant to fit the resource being developed. In particular, the turbine is designed and sized to optimize efficiency and resource utilization for electricity production; most often, other power plant components are then chosen to complement the turbine design. These custom turbine designs demand one-off manufacturing processes, which result in higher manufacturing setup costs, longer lead-times, and higher capital costs overall in comparison to larger-volume line manufacturing processes. In contrast, turbines produced in standard increments, manufactured in larger volumes, could result in lower costs per turbine. This study focuses on analysis of the global supply chain and manufacturing costs for Organic Rankine Cycle (ORC) turboexpanders and steam turbines used in geothermal power plants. In this study, we developed a manufacturing cost model to identify requirements for equipment, facilities, raw materials, and labor. We analyzed three different cases 1) 1 MWe geothermal ORC turboexpander 2) 5 MWe ORC turboexpander and 3) 20 MWe geothermal steam turbine, and calculated the cost of manufacturing the major components, such as the impellers/blades, shaft/rotor, nozzles, inlet guide lanes, disks, and casings. Then we used discounted cash flow (DCF) analysis to calculate the minimum sustainable price (MSP). MSP is the minimum price that a company must sell its product for in order to pay back the capital and operating expenses during the plant lifetime (CEMAC, 2017). The results showed that MSP could highly vary between 893 dollar/kW and 30 dollar/kW based on turbine size, standardization and volume of manufacturing. The analysis also showed that the economy of scale applies both to the size of the turbine and the number manufactured in a single run. Sensitivity analysis indicated these savings come largely from reduced labor costs for design and engineering and manufacturing setup.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Improvements in SMR Modular Construction through Supply Chain Optimization and Lessons Learned

Affordable energy is a critical societal need. Capital construction cost is a significant portion of nuclear energy cost. By controlling and reducing cost, companies can build more competitive nuclear power plants and hence provide access to more affordable energy. Modular construction provides an opportunity to reduce the cost of construction, and as projects scale up in number, the cost of each unit can be further reduced. The objective of this project was to advance design and construction methods for manufacturing Small Modular Reactors (SMRs), and in particular to improve modular construction techniques and develop best practices for designing and operating supply chains that take advantage of these techniques. The overarching objectives were to accelerate the construction schedule and reduce its variability, reduce the cost of construction, reduce interest costs accrued during construction (IDC), and thus enhance the economic attractiveness of SMRs. Our fundamental measure of merit was total capital investment cost (TCIC). To achieve these objectives, this project developed a decision support system, EVAL, to support identifying, addressing, and resolving or ameliorating challenges and deficiencies in the current modular construction approach. The results of this effort were consistent with the facts that the cost of a construction activity is often smallest when accomplished in the factory, greatest when accomplished at the construction site, and at an intermediate level when accomplished at an assembly area close to the construction site. Further, EVAL can aid in providing insight into ways to reduce waste, improve quality, efficiency, and throughput and reflects the fact that the more done early in the construction process, i.e., in the factory, the more upfront funding is required and hence the more IDC will be accrued. The analysis has lead to a better understanding of circumstances under which modular construction performed mainly in the factory will result in lower expected total cost, relative to more traditional, on-site construction procedures. Further, we anticipate that EVAL can be used to gain insight regarding what role standardization can play in order for modularization to be most effectively defined. Such results would ultimately benefit all (small and large) new nuclear construction.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Identification of Potential Locations for Placement of Strategic Transformers Reserve in Puerto Rico

Extreme weather events such as tropical storms cause extensive damage to energy infrastructures including generator stations, substations, poles, transformers, power transmission lines, etc. due to their presence in hazardous locations. Often these damages lead to long term power outage as was seen in Puerto Rico (PR) following the landfall of Hurricane Maria (2017). A potential solution to long-term power outage is strategic placement of reserve generators and transformers that would ensure continued power supply to enable restoration and recovery activities. The strategic placement of energy storage devices requires identification of potential sites such that (i) the damage from extreme events to energy infrastructures is minimized, (ii) power supply is optimized, and (iii) installation of these infrastructures is cost effective. To meet this goal, in this research, a Multi-Criteria Evaluation (MCE) technique-based site suitability analysis was implemented to identify potential sites for reserve generators and transformers in PR. To determine suitable sites and existing government facilities, the following steps were implemented: (i) identify physical, environmental, technical and socio-economic factors contributing to site suitability; (ii) determine siting constraints based on guidelines and standards used for energy sector (i.e., siting of nuclear plants, solar or wind farms), (iii) implement a raster data model using 30m x 30m spatial resolution to create suitability layers for each factor, (iv) combine suitability and exclusionary layers to create the final suitability layer. Results show that six hospitals, four colleges and universities, and five correctional facilities are in very high suitability areas and could be used for to store reserve generators. These facilities are in the eastern, south-eastern, south-western, and southern part of the island except for one correctional facility that is in the northern part of PR. Not surprisingly, coastal areas were found to be unsuitable for siting any energy facility. More than half (55.9%) of the island was also found to be unsuitable while only 14% of the island was found to be very highly suitable. Further assessment of current power plants and sub-stations indicated that only 16% and 42% of existing power plants and sub-stations respectively are in suitable areas. Given that northern part of the island lacks any facility that could be used for placement of reserve generators, suitable sites identified in this region could be used for placement of storage devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2013

Distributed Smart Grid Asset Control Strategies for Providing Ancillary Services

With large-scale plans to integrate renewable generation driven mainly by state-level renewable portfolio requirements, more resources will be needed to compensate for the uncertainty and variability associated with intermittent generation resources. Distributed assets can be used to mitigate the concerns associated with renewable energy resources and to keep costs down. Under such conditions, performing primary frequency control using only supply-side resources becomes not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in primary frequency control to maintain the stability of the system at an acceptable cost. The main objective of this project is to develop a novel hierarchical distributed framework for frequency based load control. The framework involves two decision layers. The top decision layer determines the optimal gain for aggregated loads for each load bus. The gains are computed using decentralized robust control methods, and will be broadcast to the corresponding participating loads every control period. The second layer consists of a large number of heterogeneous devices, which switch probabilistically during contingencies so that aggregated power change matches the desired amount according to the most recently received gains. The simulation results show great potential to enable systematic design of demand-side primary frequency control with stability guarantees on the overall power system. The proposed design systematically accounts for the interactions between the total load response and bulk power system frequency dynamics. It also guarantees frequency stability under a wide range of time varying operating conditions. The local device-level load response rules fully respect the device constraints (such as temperature setpoint, compressor time delays of HVACs, or arrival and departure of the deferrable loads), which are crucial for implementing real load control programs. The promise of autonomous, Grid Friendly™ response by smart appliances in the form of under-frequency load shedding was demonstrated in the GridWise Olympic Peninsula Demonstration in 2006. Each controller monitored the power grid voltage signal and requested that electrical load be shed by its appliance whenever electric power-grid frequency fell below 59.95 Hz. The controllers and their appliances responded reliably to each shallow under-frequency event, which was an average of one event per day and shed their loads for the durations of these events. Another objective of this project was to perform extensive simulation studies to investigate the impact of a population of Grid Friendly™ Appliances (GFAs) on the bulk power system frequency stability. The GFAs considered in this report are represented as demonstration units with water heaters individually modeled.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Decentralized control of units in smart grids for the support of renewable energy supply

Due to the significant environmental impact of power production from fossil fuels and nuclear fission, future energy systems will increasingly rely on distributed and renewable energy sources (RES). The electrical feed-in from photovoltaic (PV) systems and wind energy converters (WEC) varies greatly both over short and long time periods (from minutes to seasons), and (not only) by this effect the supply of electrical power from RES and the demand for electrical power are not per se matching. In addition, with a growing share of generation capacity especially in distribution grids, the top-down paradigm of electricity distribution is gradually replaced by a bottom-up power supply. This altogether leads to new problems regarding the safe and reliable operation of power grids. In order to address these challenges, the notion of Smart Grids has been introduced. The inherent flexibilities, i.e. the set of feasible power schedules, of distributed power units have to be controlled in order to support demand–supply matching as well as stable grid operation. Controllable power units are e.g. combined heat and power plants, power storage systems such as batteries, and flexible power consumers such as heat pumps. By controlling the flexibilities of these units we are particularly able to optimize the local utilization of RES feed-in in a given power grid by integrating both supply and demand management measures with special respect to the electrical infrastructure. In this context, decentralized systems, autonomous agents and the concept of self-organizing systems will become key elements of the ICT based control of power units. In this contribution, we first show how a decentralized load management system for battery charging/discharging of electrical vehicles (EVs) can increase the locally used share of supply from PV systems in a low voltage grid. For a reliable demand side management of large sets of appliances, dynamic clustering of these appliances into uniformly controlled appliance sets is necessary. We introduce a method for self-organized clustering for this purpose and show how control of such clusters can affect load peaks in distribution grids. Subsequently, we give a short overview on how we are going to expand the idea of self-organized clusters of units into creating a virtual control center for dynamic virtual power plants (DVPP) offering products at a power market. For an efficient organization of DVPPs, the flexibilities of units have to be represented in a compact and easy to use manner. We give an introduction how the problem of representing a set of possibly 10{sup 100} feasible schedules can be solved by a machine-learning approach. In summary, this article provides an overall impression how we use agent based control techniques and methods of self-organization to support the further integration of distributed and renewable energy sources into power grids and energy markets. - Highlights: • Distributed load management for electrical vehicles supports local supply from PV. • Appliances can self-organize into so called virtual appliances for load control. • Dynamic VPPs can be controlled by extensively decentralized control centers. • Flexibilities of units can efficiently be represented by support-vector descriptions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Opportunities and challenges for the expansion of LFP battery supply chains

Global markets for energy storage are growing rapidly, with some applications transitioning from traditional LiNi x Mn y Co 1−x−y O 2 (NMC) toward LiFePO 4 (LFP) due to cost, safety, and performance advantages. Battery growth has seeded interest in critical material supplies such as high-purity lithium precursors. In contrast, challenges in securing high-purity iron and phosphorus, historically not considered critical materials, are often overlooked. Precursors must remain inexpensive to maintain LFP's current cost advantage, which leverages the low-cost (∼$\$100$ per t) FeSO 4 byproduct from titanium dioxide manufacturing and will not be available as LFP manufacturing expands. As an alternative, iron is mined primarily for steel manufacturing, for which the existing supply chain and beneficiation process is optimized. LFP batteries require small iron volumes compared to steel (0.11%), but profit margins associated with existing low-cost iron ores and high costs (∼$27 000 per t) associated with low volume high-purity iron oxides may limit interest in manufacturing small volumes of high-purity, specialized iron battery precursors. The complementary LFP precursor, phosphoric acid (H 3 PO 4 ), is primarily utilized in fertilizers. Of the current phosphate ore demand for fertilizers, 4–22.8% would be required to meet projected 2045 LFP H 3 PO 4 demand, suggesting significant supply chain planning is needed to achieve projected demand. If only higher-grade material is considered, demand jumps to 15–77% of current world production. While lithium precursor purity requirements have been evaluated (Li 2 CO 3 is commonly defined as ≥99.5%), “battery grade” iron and phosphorus precursors remain poorly defined, with no internationally accessible and widely adopted standard, further challenging expanding industry by creating manufacturing uncertainty and increasing potential costs.

25 ENERGY STORAGE↗