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DOE OSTI2024

Clocking out and letting go to unleash green biotech applications in a photosynthetic host

Cyanobacteria are photosynthetic bacteria whose gene expression patterns are globally regulated by their circadian (daily) clocks. Due to their ability to use sunlight as their energy source, they are also attractive hosts for “green” production of pharmaceuticals, renewable fuels, and chemicals. However, despite the application of traditional genetic tools such as the identification of strong promoters to enhance the expression of heterologous genes, cyanobacteria have lagged behind other microorganisms such as Escherichia coli and yeast as economically efficient cell factories. The previous approaches have ignored large-scale constraints within cyanobacterial metabolic networks on transcription, predominantly the pervasive control of gene expression by the circadian (daily) clock. Here, we show that reprogramming gene expression by releasing circadian repressor elements in the transcriptional regulatory pathways coupled with inactivation of the central oscillating mechanism enables a dramatic enhancement of expression in cyanobacteria of heterologous genes encoding both catalytically active enzymes and polypeptides of biomedical significance.

Science & Technology - Other Topics↗
DOE OSTI2025

Enhancing Time Synchronization in Smart Grid With White Rabbit: Theory, Architecture, and Challenges

The smart grid aims to provide economically efficient and sustainable power to consumers with high quality and security. However, the increasing integration of distributed renewable energy sources presents challenges for smart grid protection and control systems. Here, to enhance smart grid operations, this article introduces White Rabbit (WR) as a more precise and accurate time synchronization technique. To explore White Rabbit’s potential applications in the smart grid, this study first explains existing time synchronization techniques and their limitations, followed by an analysis of the role and importance of time synchronization in smart grids. A comprehensive survey is then conducted, covering the theories, principles, implementations, performances, and existing application cases of White Rabbit. The findings suggest that White Rabbit is a promising technique for smart grid deployment. However, several challenges remain on the path to large-scale implementation. These challenges are analyzed in detail, highlighting key areas for future research.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2022

Welcome to the Special Issue on Grid-Edge Computing With Behind-the-Meter Resources [Guest Editorial]

The integration of distributed energy resources (DERs), such as solar photovoltaic systems, as well as other synergistic assets, including electric vehicles, energy storage, and smart appliances, in electric power systems has been dramatically increasing in the past few years. These assets have the capability to provide much-needed flexibility to electric power systems for improved grid reliability, resilience, and economic efficiency; however, most of these resources are located behind the meter (BTM) on customer premises, and their flexibility is not fully used in current grid operations. Grid-edge computing plays an important role in unlocking the great benefits and potential that BTM resources could provide to electric power systems by enhancing visibility and controllability at the grid edge.

behind-the-meter resources↗
DOE OSTI2024

Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗
DOE OSTI2025

A Novel Secondary Frequency Regulation with Optimal Priority Selection of AGC Contributions

Automatic generation control (AGC) is used to maintain acceptable frequencies during operation owing to fluctuations in load and variable resources. In conventional industry applications, the AGC signal is allocated to each generator according to the predispatched frequency regulation capacity or the order of economic efficiency. However, with the increasing integration of inverter-based resources (IBRs), the retirement of conventional synchronous generators (SGs) has posed new challenges to frequency control schemes because fewer of them are optional for AGC regulation. In this paper, we propose a novel model predictive control (MPC)-based frequency regulation model to reduce control cost and ensure stability, by considering different critical dynamic factors when optimally selecting the AGC units. The proposed control model – developed in a general form – comprehensively embeds characteristics such as generator ramping rates, reserve capacity, and operation cost. The model predictive control–based two-timescale AGC scheme enhances the capability of immunizing the power disturbance from types of resources by coordinating the control signals between faster IBRs and slower SGs. The case study’s proposed model is verified to be effective in synergistically enforcing different dynamic properties of AGC units into the frequency regulation scheme.

Jiang, Sufan [The University of North Carolina at ↗
DOE OSTI2020

Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

active distribution networks↗
DOE OSTI2021

Survey of Advanced Generation IV Reactor Parameters for Integrated Energy System Modeling Capabilities

This paper is to categorize and characterize advanced Generation IV nuclear reactors that are not water based according to the key parameters for the Integrated Energy Systems (IES) project[1]. A key goal of the IES project is to assess the economic viability of including an advanced nuclear reactor into an IES. The landscape of advanced reactors is heterogeneous: several unique designs with different heat profiles are currently being studied and designed. This report provides a high-level perspective of advanced reactor concepts, including primary loop nuclear output temperatures and operation schemes that could affect secondary side power production or storage. For example, this report addresses the type of thermodynamic system (Brayton, supercritical CO 2 [S-CO 2 ], etc.) and the various pressures and temperatures on the secondary side that are expected to be provided by the various primary side reactor types. Based on these characteristics, it may be possible to determine which type of nuclear power plant could be the most economical for analysis in the IES framework for power production coupled with energy storage and waste heat utilization. The results of this effort will serve as input for model development of various secondary side components, concentrating on off-the-shelf capabilities for current and near future secondary side power generation and heat rejection as simulated in TRANSFORM. Generic models can be created which are agnostic of the nuclear energy supply without violating intellectual property rights of the developer. These models can be used to make informed decisions on the economics of integrating various energy system components with nuclear plant designs. Once complete, conclusions may be drawn regarding (1) the economics, efficiency, and reliability of certain nuclear reactors, (2) certain industry processes and energy storage systems, and (3) prioritization of certain reactor types with the studied industry types.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

High Performance Solution for Security Constrained Optimal Power Flow

Solving the Alternating Current Optimal Power Flow (ACOPF) problem is key to economically efficient and reliable power networks with a good solution potentially saving utilities tens of billions of dollars annually (according to FERC). The Grid Optimization (GO) Competition set up by ARPA-E saw several promising solutions in Challenge 1. While team GOT-TJU-OPF placed top 10 in Division 3 and 4, this was not a satisfactory performance, and the team has identified specific areas to improve and will be adding more members to round out the necessary skills and expertise needed to be more competitive. The team set out for redemption during GO Challenge 2 with an improved High-Performance Solution for Security Constrained Optimal Power Flow. The (renamed) BSI-GOT-OPF Team ended up finishing top 2 overall in the competition. The algorithms developed have potential impacts for the electric power markets that are enormous. Optimal Power Flow technology can be an enabling technology to achieve energy-efficient power grids while enhancing renewable energy penetration, among others.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. The plant reload optimization framework development project aims to build an artificial intelligence, i.e., Genetic Algorithm (GA), based reactor core designing tool taking into account reactor safety and fuel performance analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Anti-Biofouling Surface Treatments for Improved Condenser Performance for Coal-Based Power Plants

Condenser performance is a critical area for improving the economic efficiency of coal-based power facilities, since more than 50% of generated heat is lost during condenser operation. Decline in condenser performance due to fouling of condenser heat transfer tubes greatly has a significant and detrimental effect on power plant efficiency and controlling biofilm formation remains a major operational challenge. Biofilm formation in condenser tubes decreases the effective diameter for cooling water and increases the surface roughness, which increases the hydraulic resistance of each tube. In addition, biofouling can result in the degradation of the condenser tubing by facilitating corrosion. Therefore, this project aims to develop novel surface treatments and secondarily applied remediation components to mitigate biofilm growth on condenser tube surfaces used in coal-fueled power plants.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Development of Genetic Algorithm Based Multi-Objective Plant Reload Optimization Platform

The U.S. nuclear industry is facing a challenge in maintaining required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects of light-water reactor nuclear power plant operations. Safety can become more economical by using a risk-informed ecosystem, such as the one being developed in the Risk-Informed Systems Analysis Pathway under the U.S. Department of Energy Light Water Reactor Sustainability Program. The Light Water Reactor Sustainability Program promotes a wide range of research and development activities to maximize both the safety and economic efficiency of nuclear power plants through improved scientific understanding, especially given that many plants are now considering second license renewals. The Risk-Informed Systems Analysis Pathway has two main goals: Deploy methodologies and technologies that better represent safety margins and cost and safety factors; Develop advanced applications that enable cost-effective plant operations. The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. This report summarizes genetic-algorithm-based multi-objective fuel reload optimization activities, specifically: Developing the non-dominated sorting genetic algorithm II optimizer in the Risk Analysis and Virtual ENviroment (RAVEN); Demonstrating and validating the developed non-dominated sorting genetic algorithm II optimizer using benchmark optimization problems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Assessment of Modeling and Simulation Technical Gaps in Safety Analysis of High Burnup Accident-Tolerant Fuels

The United States nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs), and it can be achieved more economically by using a risk-informed ecosystem, such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development activities to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: The deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety, and; The development of advanced applications that enable cost-effective plant operation. As part of the RISA Pathway, the Enhanced Resilient Plant (ERP) project refers to an NPP where safety is improved by implementing various measures, such as accident-tolerant fuels (ATF), diverse and flexible coping strategy (FLEX), enhancements to plant components and systems, incorporation of augmented or new passive cooling systems, and utilization of advanced battery technologies. The objective of the ERP research is to use novel methods and computational tools to enhance existing reactors’ safety while reducing operational costs. Many U.S. utilities are targeting implementation of ATFs instead of traditional fuel in the near future since ATFs offer benefits in terms of improved performance and cost savings. The robust properties of ATF make it possible to extend the refueling cycle from 18 to 24 months in addition to the opportunity to use less of fuel. Extensive safety assessments are required to support regulatory requirements and obtain the approvals to use ATFs and the ERP project support the industry by developing novel effective methodologies for safety evaluations. In this project, the technical gaps in the modeling and simulation of the high burnup (HBU) ATF were assessed in terms of the fuel cladding behavior during the postulated accident events. The issues were identified in modeling the cladding deformation, the hydrodynamic change due to cladding deformation and the critical heat flux (CHF). The RELAP5-3D cladding deformation model was assessed by multiple verification tests and validation with the instrumented fuel assembly (IFA) experiment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Development of Plant Reload Optimization Framework Capabilities for Core Design and Fuel Performance Analysis

The United States (U.S.) nuclear industry faces a challenge in maintaining required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects of light water reactor (LWR) nuclear power plant (NPP) operations. Safety can become more economical by using a risk-informed ecosystem, such as the one being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program promotes a wide range of research and development activities with the goal of maximizing both the safety and economic efficiency of NPPs through improved scientific understanding, especially given many plants are now considering second license renewals. The RISA Pathway has two main goals: (1) deploy methodologies and technologies that better represent safety margins and cost and safety factors and (2) develop advanced applications that enable cost-effective plant operation. The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses, and also uses artificial intelligence to support optimization of core design solutions. This report summarizes Fiscal Year 2022 (FY-22) activity in platform capability developments in RAVEN. This platform performs simulations using industry codes for core design (i.e., PARCS) and fuel performance (i.e., TRANSURANUS) which will allow expansion of the capabilities to include advanced fuel designs such as accident-tolerant fuel (ATF)s with high burnup.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Safety Analysis of FeCrAl Accident-Tolerant Fuels with Increased Enrichment and Extended Burnup

The U.S. nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor nuclear power plants (NPPs), and it can be achieved more economically by using a risk-informed ecosystem, such as that being developed by the Risk-Informed Systems Analysis Pathway under the U.S. Department of Energy Light Water Reactor Sustainability Program. This program is promoting a wide range of research and development activities to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering a second license renewal. The Risk-Informed Systems Analysis Pathway has two main goals: (1) The deployment of methodologies and technologies that enable a better representation of the safety margins and factors that contribute to cost and safety, and (2) The development of advanced applications that enable cost-effective plant operation. As part of this pathway, the Enhanced Resilient Plant project refers to an NPP where safety is improved by implementing various measures, such as accident-tolerant fuels, diverse and flexible coping strategies, enhancements to plant components and systems, incorporation of augmented or new passive cooling systems, and utilization of advanced battery technologies. The objective of the Enhanced Resilient Plant project is to use novel methods and computational tools to enhance existing reactors’ safety while reducing operational costs. This report documents research and development conducted in support of deployment of accident-tolerant fuels. This project performed safety analyses for the steady-state normal operation, anticipated operational occurrences, and design-basis accidents of a representative four-loop pressurized water reactor model with Zr and FeCrAl accident-tolerant fuel clad with higher enrichment and burnup supporting plant refueling cycles of 18 and 24 months. The source terms and environmental impacts were studied for a large-break loss of coolant accident, including uncertainty analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Demonstration of the Plant Fuel Reload Process Optimization for an Operating PWR

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. This report summarizes the research outcomes in FY-2021, which the project progressed from the planning and methodology development phase to the early demonstration phase. The highlights of these activities are: (1) the development of a multi-objective optimization process using Genetic Algorithms (GAs); (2) the development and test of an approach for optimization process acceleration using artificial intelligence (AI) that significantly reduces the computational burden; (3) the demonstration of the fuel reload optimization framework for a generic pressurized water reactor (PWR); and (4) the demonstration of limiting design basis accident (DBA) scenarios for evaluation of the transition from deterministic to risk-informed approach for fuel reload optimization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Innovations in Direct Air Capture: Unveiling a Simple and Robust Synthesized Fibrous Amine-functionalized Matrix (FAM) Sorbent for Commercial Scale-up

The escalating challenge of climate change necessitates innovative solutions in the realm of carbon management, particularly in mitigating the impact of fossil fuel emissions. Direct Air Capture (DAC) technology emerged as a critical component within the spectrum of Carbon Capture and Sequestration (CCS) solutions, offering the distinct advantage of directly removing CO2 from the atmosphere irrespective of the source. This attribute grants DAC systems unparalleled flexibility in deployment locations and the potential to make substantial contributions to lowing atmospheric CO2 levels. The success of DAC technologies significantly depends on the development of an efficient, economical sorbent capable of selective and durable CO2 capture from ambient air. Recent advancements in material science have led to the exploration of amine-functionalized sorbents, hollow fiber sorbents and membranes, and other novel materials designed to meet these criteria. This study explores a novel Fibrous Amine-functionalized Matrix (FAM) sorbent. The FAM sorbent distinguished itself through its mechanical robustness, a streamlined synthesis process, and the capability for low-temperature regeneration (is 90 oC really low temperature?). FAM’s exceptional adsorption-desorption kinetics enable swift CO2 capture and release, crucial for the viability of DAC on a commercial scale. The synthesis process involves a simple dip-coating technique, allowing crosslinked amines to coat glass substrates.

Wang, Qiuming↗
DOE OSTI2024

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application

This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗