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DOE OSTI2021

FY2020 Technology Integration Annual Progress Report

The 2020 Technology Integration Annual Progress Report covers 46 multi-year projects funded by the Vehicle Technologies Office. The report includes information on competitively awarded projects, ranging from rural shared mobility demonstration projects to medium- and heavy-duty electric vehicle deployment to statewide alternative fuel resiliency planning. It also includes projects conducted by several of the Vehicle Technologies Office’s (VTO) National Laboratory partners, Argonne National Laboratory, Oak Ridge National Laboratory and the National Renewable Energy Laboratory. These projects range from a Technical Assistance project for business, industry, government and individuals, to the EcoCar Advanced Vehicle Technology Competition, and the Fuel Economy Information Project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Dynamometer Facts

The National Renewable Energy Laboratory's (NREL's) Flatirons Campus features two dynamometers in the Water Power Systems Laboratory that can perform research validation on marine and hydrokinetic energy devices as well as wind turbine systems from 100 watts to 15 kilowatts (kW) in size. These capabilities can also be combined with programmable loads, blue economy (nrel.gov/water/powering-blue- economy.html) application simulators, and microgrid emulators to test an extensive range of system operation and technology integration. The dynamometers replicate realistic operational conditions to assess power take-of systems and advance the technical readiness of innovations.

dynomometer↗

DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗

DOE OSTI2020

Distributed Energy Resource Cybersecurity Framework Best Practices

Current cybersecurity challenges for distributed energy resources (DERs) stem from the integration of various systems and the cyber-physical security concerns they bring. Although frameworks exist for industrial control systems and other energy systems, a simple guided tool that can prioritize recommended actions for controls specific to DERs is lacking. Additionally, the novelty of the field has made it difficult to create a standardized procedure for DERs with cybersecurity in mind. In response to this critical need to address cybersecurity risk management, the National Renewable Energy Laboratory (NREL) has developed a framework and accompanying web application known as the Distributed Energy Resources Cybersecurity Framework (DERCF) (Powell, 2019). A brief overview of the DERCF can be found in Section 1.2. This document provides a guide to cybersecurity best practices identified by technical research and site visits where the framework was used to assess the cybersecurity posture of DER systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Phase 1: Duke Energy Zero Emission Resource Integration Study (ZERIS); Phase 2: Carbon-Free Resource Integration Study for Duke Energy (Final Report)

Phase 1: This statement of work makes up Phase 1 of a larger effort. During this Phase 1 effort, NREL will work with Duke Energy to analyze the impacts of integrating significant amounts of new solar power into the Duke Energy power system under a variety of different penetrations scenarios, with a maximum of ten (10) full scenarios examined. The existing fleet, particularly the nuclear generation, will be considered in the quantitative assessments and discussions. Duke Energy is looking to quantify how much solar generation its system can handle. NREL will work with Duke Energy to quantify solar potential, identify likely integration challenges and possible opportunities for wind, storage, demand side resources and other technologies. Phase 2: This Statement of Work consists of a follow-up effort (Phase 2) to a recently completed Phase 1 modeling effort. During Phase 2, NREL will work closely with Duke Energy to analyze the impacts of integrating significant amounts of variable generation resources (wind and solar) and storage into Duke Energy's system in the Carolinas. The existing fleet, particularly nuclear generation, will be considered in the quantitative assessment and discussions. This Statement of Work also includes an extension to Phase II of the Carbon-Free Resource Integration Study for Duke Energy. In this extension, NREL will work closely with Duke Energy to extend the production cost analysis developed in Phase II to 2018 weather and load data for Duke Energy's territory. This extension leverages the modeling tools and datasets developed as part of Phase II. The analysis will compare results from Phase II (using 2012 weather and load) with 2018 results to assess system operations with increased penetration of renewables and storage. Simplifying assumptions will be made for modeling Duke Energy's neighbors in the production cost model.

14 SOLAR ENERGY↗

DOE OSTI2026

Green Methanol via an Integrated Direct Air Capture, CO 2 Electrolyzer, and Hydrogenation Reactor

This project pioneered a groundbreaking reactor design to produce green methanol by harnessing the electrochemical CO 2 reduction reaction (eCO 2 RR), a cornerstone of power-to-fuels technology. The effort integrated three innovative technologies to achieve carbon-neutral methanol production at a target cost of under $\$$800/ton: 1. Direct Air Capture (DAC): Using a cutting-edge sorbent material developed at Holocene, scalable models were developed to integrate captured atmospheric CO₂ into the reactor system. 2. Intermediate-Temperature CO 2 Electrolyzer: Developed by the University of Tennessee (UTK), this electrolyzer utilizes a cost-effective, proton-conducting solid acid electrolyte (CsH 2 PO 4 , CDP) and a mixed-metal oxide cathode. It achieves high faradaic efficiencies (>98%) by effectively suppressing hydrogen evolution at high current densities, converting CO 2 to CO with remarkable selectivity. 3. Catalysis and Reactor Engineering: Oak Ridge National Laboratory (ORNL) contributed world-class expertise in heterogeneous catalysis and reactor design. Their advanced ASPEN modeling drove systems integration and supported techno-economic and life cycle analyses. This effort was further bolstered by partnerships with industry leaders Air Company and Plug Power, who provided critical guidance on scaling, systems engineering, and the integration of water electrolyzers into large-scale operations. During Phase 1, the team focused on modeling and validating a lab-scale reactor demonstrating the feasibility of the integrated approach. Key accomplishments include a 52% increase in current density at 0.8 V while maintaining >98% CO faradaic efficiency, successful 10× scale-up of the electrolyzer with performance within 5% of coin-cell results, best-in-class durability (168-hour test at 0.6 V with 0.14 mA/cm 2 -h degradation), validated TEA confirming the $\$$800/ton methanol target, and completed preliminary LCA showing potential for net-negative GHG emissions under renewable energy scenarios.

10 SYNTHETIC FUELS↗

DOE OSTI2020

State-Constrained Grid-Forming Inverter Control for Robust Operation of AC Microgrids

The control of grid-forming inverters has become an important problem to achieve high efficiency, reliability, and resilience of the future power grid. The distributed control that utilizes a sparse communication network has been proposed for AC microgrids, in which each DG only has access to the information of a small number of its neighboring DGs, reducing the computational complexity and improving the resiliency to faults and unknown parameters. However, these approaches have not provided a clear analysis of the coupling between active/reactive power injections and the voltage magnitude/frequency or a distributed updating law with a solid theoretical foundation. Additionally, in the existing approaches, tracking the references from the secondary control may violate operational constraints.

Xu, Ying↗

DOE OSTI2025

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗

DOE OSTI2023

Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

The integration of large-scale uncertain and uncontrollable wind and solar power generation has brought new challenges to the operations of modern power systems. In a power system with abundant water resources, hydroelectric generation with high operational flexibility is a powerful tool to promote a higher penetration of wind and solar power generation. In this paper, we study the day-ahead scheduling of a thermal-hydro-wind-solar power system. The uncertainties of renewable energy generation, including uncertain natural water inflow and wind/solar power output, are taken into consideration. We explore how the operational flexibility of hydroelectric generation and the coordination of thermal-hydro power can be utilized to hedge against uncertain wind/solar power under a multistage robust optimization (MRO) framework. To address the computational issue, mixed decision rules are employed to reformulate the original MRO model with a multi-level structure into a bi-level one. Column-and-constraint generation (C &CG) algorithm is extended into the MRO case to solve the bi-level model. The proposed optimization approach is tested in three real-world cases. Furthermore, the computational results demonstrate the capability of hydroelectric generation to promote the accommodation of uncertain wind and solar power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Water Quality Effects of Economically Viable Land Use Change in the Mississippi River Basin under the Renewable Fuel Standard

Demand for biofuel production driven by the Renewable Fuel Standard (RFS2) has coincided with increased land in corn production and increasing nitrogen (N) loss to the Gulf of Mexico. Diversifying cropland with perennial energy crops (miscanthus and switchgrass) may reduce N loss and improve water quality. However, the extent of these benefits depends on the mix of biomass feedstocks (corn stover, perennials) incentivized by the RFS2 and the extent to which energy crops displace N-intensive row crops. We developed an integrated economic-biophysical model to quantify the water quality impacts of three potential policy scenarios that provided corn ethanol at levels before the RFS2 (RFS1 baseline); 15 billion gallons of corn ethanol (corn ethanol only); or 16 billion gallons of cellulosic ethanol in addition to corn ethanol (corn + cellulosic ethanol). Our results showed that economically optimal locations for perennial energy crop production were distributed across idle cropland with lower intrinsic N loss than active cropland. We found stover removal incentivized by the RFS2 offset N loss benefits of perennial energy crops. Furthermore, this finding suggests that targeted incentives for N loss reduction are needed to supplement the RFS2 to induce displacement of N-intensive row crops with energy crops to reduce N losses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Automating the Solar Interconnection Technical Evaluation Process: PREconfiguring and Controlling Inverter SEt-Points (PRECISE)

Utilities are receiving an increasing number of interconnection requests for distributed solar photovoltaic (PV) systems from their customers, requiring a solution to quickly and intelligently perform technical assessments of these requests and to manage these new renewable energy assets. In this paper, we present a stand-alone interconnection tool, PREconfiguring and Controlling Inverter SEt-points (PRECISETM), and how it overcomes the challenges of accelerating technical evaluations and leverages smart inverter functionality to provide local voltage support on an as-needed basis. The paper details the purpose and architecture of PRECISE, including tools, data source requirements, the model-based interconnection evaluation process, and technical results. PRECISE is an integrated software tool that customizes PV inverter settings for utilities by modeling and assessing impacts on local voltages and the need for advanced inverter functions (AIFs) (e.g., volt-VAR and volt-watt). PRECISE maximizes the use of readily available utility data sets to make fast online assessments of incoming PV interconnection requests and makes accept/reject recommendations along with custom settings for AIFs.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2023

Digital Twin for Hydropower System Object Modeling: Alder Dam (FY2023)

Hydropower is the world's largest source of renewable electricity, and hydropower plants are distributed all over the world. Typical major components of a hydropower plant are the governor, excitation, generator, thrust bearing, hydraulic turbine, transformer, the main lead, metering and control, tailwater depression, and dissolved oxygen. For each component, various measures are taken. The measurements are acquired by various heterogeneous systems, including standalone sensors, programmable logic controllers (PLC), Supervisory control and data acquisition (SCADA), Internet of Things (IoT), and data acquisition and integration platforms such as OSI/PI. The measured data are often archived within the plant by a data management platform, and many institutions have cloud-based archive systems, such as Hydropower Research Institution (HRI), U.S. Army Corps of Engineers (USACE), and Columbia River Data Access in Real Time (DART). Object Modeling is a general framework for designing information systems. It focuses on objects, the actions they perform, and the messages they send to one another to cause those actions to be taken. The major differences among object modeling, network modeling, data modeling, and process modeling are that in the first we focus on the actions in response to information, objects which form the system, the actions they perform, and how they pass information to one another, while in the second we concentrate on where, when and how much information is moved, while in the third we focus on what information is moved and where it is moved, while in the last we focus on how it is moved and when it is moved. Object modeling was developed basically as a method to develop object-oriented systems and to support object-oriented programming. It describes the static structure of the system. The object Modeling Technique is easy to draw and use. That is why we choose object modeling to connect physical hydropower plants to Digital Twin. It recognizes the objects and the relationship between them. It identifies the attributes and functions of each class. Dynamic Modeling: It explains how objects respond to events. Functional Modeling indicates the processes executed in an object and how data changes when it moves to objects. It has been used in many applications like telecommunication, transportation, etc.

13 HYDRO ENERGY↗

DOE OSTI2024

Data Acquisition and Control for Marine Energy Devices: Cost Considerations

This document discusses the process involved with developing a data acquisition system specifically in the context of applications for Marine Renewable Energy (MRE) technologies however, much of what is presented is applicable to applications requiring data acquisition in general. The detail on the process is provided to highlight the critical steps and needs for a successful measurement campaign and to understand what can impact the overall outcome, cost, and schedule. The process presented is an amalgamation of best practices, lessons learned, recommendations, and prudent technical project planning and management. Data acquisition systems may be tightly integrated with or into the device under measurement and it often has its own dependencies that must be met. Therefore, early consideration and planning for the data acquisition system are stressed throughout this document.

13 HYDRO ENERGY↗

DOE OSTI2026

Design of a SMART Valve Testbed for Nuclear Thermal Dispatch

By the year 2050, the United States aims to achieve net-zero carbon emissions. To achieve this target, the licensing of the Light Water Reactor (LWR) fleet has been extended for 20 more years. To stay economically competitive with other power sources such as renewable and fossil-fuel power plants, the U.S. Department of Energy has introduced a plan to modernize the existing LWR fleet and diversify the revenue stream. One of the plans is to dispatch thermal energy to endothermic industrial processes. SMART valves will play an important role in this initiative by efficiently balancing the load by regulating valves in a coordinated manner while monitoring the thermal-hydraulic systems to enhance safety and maintain the integrity of the power plant. This research aims to develop a facility to test the coordinated control algorithm and produce various test results for training the monitoring system. The constructed facility is capable of simulating various operational and accidental scenarios by coordinating all the valves (positions) and pump (flowrate). The facility is developed with an Internet of Things (IoT)-based custom system and a python-based valve position control and coordination mechanism. It has achieved stable sensor outputs, pump control, and coordinated valve regulation in all three valves with minimum obstruction in the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Geothermal Energy and Resilience in Arctic Countries

The eight Arctic countries - Iceland, Canada, Denmark (Greenland and the Faroe Islands) Norway, Sweden, Finland, Russia, and the United States (Alaska) - have diverse energy systems, but can be split into two distinct groups based on energy characteristics. The first group includes systems in Europe (Finland, Norway, Sweden, and Iceland), which are heavily grid-connected. The second group includes the United States (Alaska), Canada, Russia, and Greenland, which have grid-connected energy systems in their more densely populated southern regions, but are also defined by the prevalence of remote microgrids. Energy sources for heat and power vary across grid-connected communities in the Arctic nations. The primary energy source for remote communities, on the other hand, is almost exclusively diesel. This is true for both heat and power. Despite these and other key distinctions, Arctic countries share many commonalities with regard to their energy systems. One is a fundamental need for heat. Heat and electric energy are linked in most communities - remote, rural, and urban - and those linked systems are increasingly vulnerable to disruptions. Several of the Arctic countries use baseload renewable energy resources for heat and power. Iceland uses geothermal and hydroelectric; Canada, the United States, Sweden, Norway, and Finland use hydroelectric. Utilization of baseload renewable energy resources on-site for combined heat and power appears to enhance the resilience of communities in Arctic countries with high penetration of those resources. On the other hand, reliance on diesel by remote communities in other Arctic countries may be amplifying vulnerabilities. Although geothermal energy is currently used in all eight Arctic countries, resources are poorly mapped, and details can be difficult to come by. Despite this, geothermal energy provides heat and sometimes electricity at both utility scales and at the microgrid scale. Geothermal electricity is produced in Iceland, Russia, and the United States (Alaska). Direct use of geothermal heat is used in Iceland, Russia, United States, Canada, and Norway. Geo-exchange is used in Sweden, Finland, Norway, Canada, and the United States. In this paper, we reframe geothermal heat and power systems as integrated energy systems, asking the question: are integrated geothermal energy systems - where available and economic - resilient solutions for communities in Arctic countries? We identify resilience attributes of integrated geothermal energy systems, with a focus on microgrids and small-scale applications. Based on the high-level, qualitative analysis presented in this paper, the answer appears to be yes. Further work should prioritize refining our understanding of geothermal resources in Arctic countries, because development of the most economic geothermal resources in Arctic countries has the potential to enhance the energy resilience of its residents, whether in a grid-connected or remote off-grid context.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

U.S. national water and energy land dataset for integrated multisector dynamics research

Abstract Understanding resource demands and tradeoffs among energy, water, and land socioeconomic sectors requires an explicit consideration of spatial scale. However, incorporation of land dynamics within the energy-water nexus has been limited due inconsistent spatial units of observation from disparate data sources. Herein we describe the development of a National Water and Energy Land Dataset (NWELD) for the conterminous United States. NWELD is a 30-m, 86-layer rasterized dataset depicting the land use of mappable components of the United States energy sector life cycles (and related water used for energy), specifically the extraction, development, production, storage, distribution, and operation of eight renewable and non-renewable technologies. Through geospatial processing and programming, the final products were assembled using four different methodologies, each depending upon the nature and availability of raw data sources. For validation, NWELD provided a relatively accurate portrayal of the spatial extent of energy life cycles yet displayed low measures of association with mainstream land cover and land use datasets, indicating the provision of new land use information for the energy-water nexus.

58 GEOSCIENCES↗

DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Electrical Energy Storage Data Submission Guidelines

Energy storage technologies are positioned to play a substantial role in power delivery systems. They are being touted as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, due to their relative infancy, there is a lack of knowledge on how these resources truly operate over time. Data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verifying the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data the provider supplies for analysis. After review of energy storage data received from several providers, it has become clear that some of these data are inconsistent and incomplete, raising the question of their efficacy for robust analysis. This report reviews and proposes general guidelines such as sampling rates and data points that providers must supply for robust data analysis to take place. Consistent guidelines are the basis of the proper protocol and ensuing standards to (a) reduce the time it takes data to reach those who are providing analysis; (b) allow them to better understand the energy storage installations; and (c) enable them to provide high-quality analysis of the installations. This report is intended to serve as a starting point for what data points should be provided when monitoring. As battery technologies continue to advance and the industry expands, this report will be updated to remain current.

25 ENERGY STORAGE↗