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Non-Constant Learning Rates in Retrospective Experience Curve Analyses and their Correlation to Deployment Programs

A key challenge for policy-makers and technology market forecasters is to estimate future technology costs and in particular the rate of cost reduction versus production volume. A related, critical question is what role should state and federal governments have in advancing energy efficient and renewable energy technologies? This work provides retrospective experience curves and learning rates for several energy-related technologies, each of which have a known history of federal and state deployment programs. We derive learning rates for eight technologies including energy efficient lighting technologies, stationary fuel cell systems, and residential solar photovoltaics, and provide an overview and timeline of historical deployment programs such as state and federal standards and state and national incentive programs for each technology. Piecewise linear regimes are observed in a range of technology experience curves, and public investments or deployment programs are found to be strongly correlated to an increase in learning rate across multiple technologies. A downward bend in the experience curve is found in 5 out of the 8 energy-related technologies presented here (electronic ballasts, magnetic ballasts, compact fluorescent lighting, general service fluorescent lighting, and the installed cost of solar PV). In each of the five downward-bending experience curves, we believe that an increase in the learning rate can be linked to deployment programs to some degree. This work sheds light on the endogenous versus exogenous contributions to technological innovation and highlights the impact of exogenous government sponsored deployment programs. This work can inform future policy investment direction and can shed light on market transformation and technology learning behavior.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Fusion Materials Research at Oak Ridge National Laboratory in FY2019

This is the eighth Annual report of the Oak Ridge National Laboratory (ORNL) program aimed at developing and qualifying materials that can meet the application requirements of fusion power systems, especially the near-plasma regions of those systems. Earlier issues of these annual reports can be found at the Fusion Materials Program website; more detailed reporting for those and many earlier years are contained in the series of Semiannual Progress reports at the same website, and the detailed results of the program are contained in the published scientific literature.

36 MATERIALS SCIENCE↗

Development and Demonstration of Large-Scale Metal Additive Manufacturing for Military Vehicle Applications (Final Report)

Additive Manufacturing (AM) is a process by which complex parts can be manufactured directly from a basic feedstock without the need for tooling. From a defense perspective, this will enable direct manufacture of parts for legacy equipment. However, current AM technologies are limited to small part sizes (approximately 1 cubic foot), slow production rates (approximately 1 cubic inch per hour) and expensive feedstocks (approximately $100/lb). The objective of this project was to explore the impact large-scale, high production rate additive manufacturing can have on military applications. First, the program explored the use of existing composite large-scale (Big Area Additive Manufacturing – BAAM) systems for both tooling and direct part replacement with the use of the Army’s Low Velocity Airdrop Delivery (LVAD) system and Willy’s Jeep. We report on the cost and time required for the development of both tooling and parts. Second, the program focused on expanding the BAAM technology to directly manufacture aluminum and steel components. A robotic welding system was developed, test articles manufactured and with a final deliverable of a steel bumper manufactured and installed on the Jeep. This report covers the results of the program and describes follow-on efforts to commercialize the technology.

36 MATERIALS SCIENCE↗

Technical Specification Surveillance Interval Extension Using Self-Diagnostics

As part of the Light Water Reactor Sustainability program, an ongoing research effort is being conducted on technical specifications surveillance interval extension of digital equipment in nuclear power plants. The research team is led by Idaho National Laboratory and includes Pacific Northwest National Laboratory, Technology Resources, and Oak Ridge National Laboratory. This research focuses on developing methods for applying the U.S. Nuclear Regulatory Commission (NRC)–approved guidance to implement a licensee-controlled, risk-informed surveillance frequency change program in digital instrumentation and control (I&C) systems that include self-diagnostics and online monitoring (OLM) capabilities. Although approved methods exist for extending technical specifications (TS) surveillance test intervals (STIs) for general equipment, including analog I&C equipment, gaps remain in technology and guidance on crediting newer digital equipment’s internal self-diagnostics and OLM characteristics. Previous research described a general methodology for crediting internal self-diagnostics for extending surveillance test intervals. The methodology used self-diagnostics to detect—and credited recovery from—failure. Self-diagnostics were also applied for performance monitoring during the extended surveillance interval. This report discusses the status of recent activities to evaluate the previously developed methodology using a pilot study. Although both a utility partner for a pilot study and a specific digital asset were identified in FY2020, delays in obtaining proprietary information resulted in a limited ability to fully evaluate the methodology, and further interactions were complicated by the COVID pandemic. Therefore, at that time, the use of public-domain information—along with current processes for surveillance interval extension through a surveillance frequency control program—identified the need to fully assess diagnostic coverage as part of the pilot study. Furthermore, self-diagnostics were also identified as a potential option to replace the drift analyses conducted as part of current STI extension procedures. In FY2022, the project was reconstituted with the industry partner, and information and data were made available by the industry partner to the research team for review. The shared information included failure event descriptions and data for a digital I&C system since its implementation, as well as recent STI extension interval reports developed by the utility partner on that digital I&C system. This report presents an evaluation of this information and data and describes an application of the proposed methodology cited above. The methodology seeks to take advantage of the self-diagnostics and OLM capabilities to reduce risk or reduce the level of qualitative monitoring assessment needed to perform a risk-informed STI extension using existing NRC approved guidance or both. Addressing these issues of STI extension by crediting self-diagnostics is likely to result in benefits for current and future nuclear power plant (NPP) operations, including lowering the barriers to adoption of digital I&C systems and increasing cost savings by deferring or eliminating unneeded preventive maintenance (tasks or checks or activities). Specifically, self-diagnostic and OLM capabilities of newer digital equipment being installed in non-safety and safety applications are designed to detect failures, provide early warning of potential failures, and notify plant operators to take appropriate action to reduce out of service (OOS) time thus protecting safety margins. Moreover, the equipment is expected to provide information that time-related operational degradation is identified early to ensure timely and planned corrective actions instead of a reactive and unplanned approach ahead of an extended-surveillance interval.

42 ENGINEERING↗

Systems Level Fuel Cycle Modeling in TMAP8 - A Demonstration

The tritium migration analysis program (TMAP) has been used for tritium inventory tracking and analysis for several years, and the Multiphysics Object Oriented Simulation Environment (MOOSE)-based TMAP8 has several improvements over TMAP4 and TMAP7, such as support for multiple dimensions and non-cartesian coordinate systems, as well as interoperability with sub-apps at higher and lower length scales. We demonstrate that TMAP8 has the additional capacity to solve systems-level problems using zero-dimensional ordinary differential equations by reproducing a literature model which describes a systems-level fuel-cycle of tritium inventory in a hypothetical fusion power plant. The capacity to run several coupled multi-scale physics calculations as part of a single package will be necessary for accurate blanket and fuel-cycle design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Microbiome to Function: Next Generation Eco-Microbiology Workshop Report

The Biological and Environmental Research (BER) program champions the predictive understanding of complex biological systems to enable energy and infrastructure security. To support this challenging endeavor, BER began funding Science Focus Area (SFA) projects at the national labs a decade ago in order to foster scientific advances that are more easily achieved by sustained team research. BER has recently encouraged collaboration among SFAs as a means to accelerate innovation and scientific impact. To facilitate BER’s vision, the Bioscience Division at Los Alamos National Laboratory (LANL) proposed an annual pan-SFA workshop that would rotate among the National Labs. LANL hosted the first workshop in September 2019 to explore a common challenge—understanding how microbiomes function—and to promote collaboration opportunities among SFAs in BER’s Microbial Genomics Program. The workshop comprised overview presentations of the eight SFAs and two SFA pilots and discussions of near- and midterm opportunities to foster collaboration. These opportunities include 1) sharing isolates, 2) sharing data and storage, 3) establishing common standards and best practices, and 4) fostering scientific exchanges.

59 BASIC BIOLOGICAL SCIENCES↗

State Energy Offices’ Engagement in Electric Distribution Planning to Meet State Policy Goals

NASEO and Berkeley Lab released a new publication on State Energy Offices’ Engagement in Electric Distribution Planning to Meet State Policy Goals. State and Territory Energy Offices develop plans, programs, policies, and projects that have a substantial impact on electric distribution systems. They also can participate in distribution system planning (DSP) processes to help ensure that utilities – consumer- and investor-owned – meet the state’s future energy needs. This paper recognizes the wide spectrum of roles that State Energy Offices can play in DSP processes, including planning for distributed energy resources and grid modernization. It highlights various examples of non-regulatory activities by State Energy Offices including planning, conducting studies, convening stakeholder processes, and implementing programs that inform and contribute to distribution system planning. It also provides examples of State Energy Offices’ engagement in proceedings before their respective public utility commissions. As State Energy Offices face myriad challenges associated with meeting state policy goals, preparing for anticipated rates of distributed energy resource deployment, addressing concerns regarding grid reliability and resilience, and recommending and making long-term investment decisions, the examples provided through this guide can serve as a resource in navigating those challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cyber security evaluation of II&C technologies

The Light Water Reactor Sustainability (LWRS) Program is a research and development program sponsored by the Department of Energy, which is conducted in close collaboration with industry to provide the technical foundations for licensing and managing the long-term, safe and economical operation of current nuclear power plants The LWRS Program serves to help the US nuclear industry adopt new technologies and engineering solutions that facilitate the continued safe operation of the plants and extension of the current operating licenses. Within the LWRS Program, the Advanced Instrumentation, Information, and Control (II&C) Systems Technologies Pathway conducts targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. operating light water reactor (LWR) fleet. The II&C Pathway is conducted by Idaho National Laboratory (INL). Cyber security is a common concern among nuclear utilities and other nuclear industry stakeholders regarding the digital technologies that are being developed under this program. This concern extends to the point of calling into question whether these types of technologies could ever be deployed in nuclear plants given the possibility that the information in them can be compromised and the technologies themselves can potentially be exploited to serve as attack vectors for adversaries. To this end, a cyber security evaluation has been conducted of these technologies to determine whether they constitute a threat beyond what the nuclear plants already manage within their regulatory-required cyber security programs. Specifically, the evaluation is based on NEI 08-09, which is the industry’s template for cyber security programs and evaluations, accepted by the Nuclear Regulatory Commission (NRC) as responsive to the requirements of the nuclear power plant cyber security regulation found in 10 CFR 73.54. The evaluation was conducted by a cyber security team with expertise in nuclear utility cyber security programs and experience in conducting these evaluations. The evaluation has determined that, for the most part, cyber security will not be a limiting factor in the application of these technologies to nuclear power plant applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Office of Electricity Delivery and Energy Reliability (OE) National Energy Technology Laboratory (NETL) American Recovery and Reinvestment Act 2009 United States Department of Energy

The purpose of this project was for Seeo to deliver the first ever large-scale or grid-scale prototype of a new class of advanced lithium-ion rechargeable batteries. The technology combines unprecedented energy density, lifetime, safety, and cost. The goal was to demonstrate Seeo’s entirely new class of lithium-based batteries based on Seeo’s proprietary nanostructured polymer electrolyte. This technology can enable the widespread deployment in Smart Grid applications and was demonstrated through the development and testing of a 10 kilowatt-hour (kWh) prototype battery system. This development effort, supported by the United States Department of Energy (DOE) enabled Seeo to pursue and validate the transformational performance advantages of its technology for use in grid-tied energy storage applications. The focus of this project and Seeo’s goal as demonstrated through the efforts made under this project is to address the utility market needs for energy storage systems applications, especially for residential and commercial customers tied to solar photovoltaic installations. In addition to grid energy storage opportunities Seeo’s technology has been tested with automotive drive cycles and is seen as equally applicable for battery packs for electric vehicles. The goals of the project were outlined and achieved through a series of specific tasks, which encompassed materials development, scaling up of cells, demonstrating the performance of the cells, designing, building and demonstrating a pack prototype, and providing an economic and environmental assessment. Nearly all of the tasks were achieved over the duration of the program, with only the full demonstration of the battery system and a complete economic and environmental analysis not able to be fully completed. A timeline over the duration of the program is shown in figure 1.

25 ENERGY STORAGE↗

Business Models for Scaling Demand Flexibility Volume III – Stakeholder ecosystem management challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Business Models for Scaling Demand Flexibility Volume II – Customer relationship management strategies, challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program customer relationship management strategies, which provide information on value creation and focus on ensuring customers can navigate programs smoothly. This report discusses the role of customer relationship management strategies in demand flexibility programs, characterizes customer relationship management strategies that can be considered during program design and implementation, identifies existing challenges to customer relationship management strategies, and describes lessons learned. This report is part of a series that includes reports on value propositions, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Business Models for Scaling Demand Flexibility Volume I – Value proposition characteristics, challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility value propositions, which provide information on value creation and describe how programs deliver clear benefits that address customer and grid needs. This report discusses the role of value propositions in demand flexibility programs, provides an overview of value propositions for a range of demand flexibility stakeholders, identifies existing challenges to establishing an effective value proposition, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Business Models for Scaling Demand Flexibility Volume IV – Program life cycle challenges and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on the life cycle of demand flexibility programs, which provides information on value creation and describes the various deployment phases program implementers navigate from initial program conceptualization through to program expansion and replication to new customer segments and regions. This report characterizes the key phases of the demand flexibility program life cycle, identifies existing challenges across the program deployment phases, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Using Thermosyphon Hybrid Cooling System to Optimize Data Center Water Efficiency

Fact sheet describes a Federal Energy Management Program Federal Energy and Water Management Award-winning project that deployed an innovative thermosyphon cooler (TSC) as a test bed on the roof of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF). In combination with existing evaporative towers, the TSC forms an extremely water-and cost-efficient hybrid cooling system.

Data Center↗

Pneumatic System for the Neutron Generator Facility at SRNL

A neutron generator system equipped with a pneumatic sample transfer system, enables scientists to perform neutron activation analysis with increased efficiency and accuracy. The samples are placed in sealed containers called 'rabbits' and are passed through the system. Once sealed in the rabbit, the samples are loaded into the tubes via a hopper mechanism. The sample is launched into the generator with compressed air and captures neutrons, causing the sample to become radioactive. Next, the sample is sent to a detector, where the samples are counted for a set length of time on a Ge detector. Once the detection is completed, the sample is then sent to the trash. The system utilizes an interface to select which generator valve and detector location to send the sample too. This project is supports the future development of the neutron generator in the F wing of 773-A. A program is used to automate the passing of the rabbit through the system. The user is able to select the generator valve and detector to be utilized in the testing of the sample. Using the 'Add Rabbit' button the user can add as many sample rabbits to rabbit editor tab. Characteristics are then entered into the fields for each rabbit. The Pre-Det Cooldown and Post-Det Cooldown are intervals used to space out the time before the rabbit is sent into and out of the detector. After all characteristics are entered, the 'Submit to Hopper' button is selected thus preparing the program to run the rabbits in the inserted order. Small setup of the system using the available parts. Simulates the movement of the rabbit from the generator to the detector. Small valves are located before each manifold. To transport the rabbit from one side to the other, the valve of the sending side must be open and the receiving side must be closed. The manifold and valve in direct line with the compressor would extend to the hopper. Once the hopper is loaded, the valve would be open to send the rabbit to generator. Additional manifolds and other mechanical parts are necessary to complete the system design. To complete the construction of the system additional parts, such as the mechanical parts for the switching mechanism and the solenoid valves, are necessary. Further, to complete the automatization of the system, an Arduino is required to communicate with the valves and adjust the linear servos appropriately so that the rabbits are sent to the selected generator location and detector. This work is aimed to be completed in the next fiscal year.

42 ENGINEERING↗

FY20 LLNL Experimental Programs at Omega

In Fiscal Year 2020 (FY20), Lawrence Livermore National Laboratory’s (LLNL’s) High-Energy-Density Physics (HED) and Indirect Drive Inertial Confinement Fusion (ICF-ID) programs conducted numerous campaigns on the OMEGA and OMEGA-EP (EP) laser systems. This was the 22ndyear of National Lab collaborative experiments at OMEGA since the Nova Laser at LLNL shut down in 1999, building upon prior collaborations. In FY20overall,these LLNL programs led 375target shots, with 166shots using just the OMEGA laser system and209 shots using just the EP laser system. Approximately 34% of the total number of shots (35OMEGA shots and 93EP shots) supported the Indirect Drive Inertial Confinement Fusion Campaign. The remaining 66% (131OMEGA-only shots and 116EP-only shots) were dedicated to experiments for High-Energy-Density Physics.Highlights of the various HED and ICF-ID campaigns are summarized in the following reports.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗

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