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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Testing of Instrumentation and Control Sensors and Cables for Small Modular Reactors

As advanced reactor concepts like small modular reactors (SMRs) progress towards design maturity and commercial deployment, it is important to assess their instrumentation and control (I&C) system sensors and cables to assure their safe, reliable, and efficient operation throughout their service lifetimes. The I&C system of any nuclear power plant (NPP) is the central nervous system of the plant and is made up of field devices such as sensors which are connected via cables to analog and/or digital systems. These systems are responsible for control and/or protection of the plant. The performance of I&C sensors and cables is dependent on several factors, including plant operating conditions. In particular, the plant operation can affect the static (accuracy) and dynamic (response) performance of the sensor, whereas the surrounding ambient environment can impact the remaining useful life of the cables, connectors, and other associated components within I&C sensor circuits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Development of Advanced Ultra-Supercritical (AUSC) Pulverized Coal (PC) Plants

This report presents an independent assessment of pulverized coal (PC) power plants operating at advanced ultrasupercritical (AUSC) steam conditions. At AUSC conditions, PC plants generate electricity at higher efficiencies and with lower carbon footprints than PC plants operating at subcritical, supercritical (SC), and ultrasupercritical (USC) steam conditions, such as those examined in previous National Energy Technology Laboratory (NETL) reports. However, advanced materials are required for commercial operation under these AUSC steam conditions which impact plant economics. In 2001, the United States (U.S.) Department of Energy (DOE) with the Ohio Coal Development Office launched a research program carried out by a consortium of industry and research organizations (the AUSC Consortium) to develop the materials necessary to commercially demonstrate AUSC technology. The results contained in this report incorporate findings by the AUSC Consortium.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1983

Advanced coal gasifier-fuel cell power plant systems design

Two advanced, high efficiency coal-fired power plants were designed, one utilizing a phosphoric acid fuel cell and one utilizing a molten carbonate fuel cell. Both incorporate a TRW Catalytic Hydrogen Process gasifier and regenerator. Both plants operate without an oxygen plant and without requiring water feed; they, instead, require makeup dolomite. Neither plant requires a shift converter; neither plant has heat exchangers operating above 1250 F. Both plants have attractive efficiencies and costs. While the molten carbonate version has a higher (52%) efficiency than the phosphoric acid version (48%), it also has a higher ($0.078/kWh versus $0.072/kWh) ten-year levelized cost of electricity. The phosphoric acid fuel cell power plant is probably feasible to build in the near term: questions about the TRW process need to be answered experimentally, such as weather it can operate on caking coals, and how effective the catalyzed carbon-dioxide acceptor will be at pilot scale, both in removing carbon dioxide and in removing sulfur from the gasifier.

Heller, M. E.↗

DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Design and experimental testing of a 150 kWh thermal battery using thermosiphons embedded in a concrete matrix for power plant flexible operation

One of the options for achieving the global temperature limitation of 1.5 °C target, for the mitigation of global warming, is based on the better penetration of renewables into the electrical grid. This has imposed a burden to fossil fuel fired power plants since they are required to operate away from their baseload mode to compensate for the inherent intermittence of the renewable power. Integrating energy storage with fossil plants is an option to achieve their needed flexibility. A cost competitive energy storage option for the solution is based on storing sensible heat in concrete. Here, this paper reports research results and development of a thermal battery cell (TBC) capable of operating at temperatures up to 425 °C. A novel concept consisting of a concrete matrix for sensible heat storage, engineered to provide enhanced thermal and mechanical properties, and twenty-two thermosiphon elements, engineered for dual action were designed and fabricated into a single thermal energy storage (TES) module. Research for the development of the components for the TBC was performed in the laboratory. Efficient heat transfer, to/from the storage media, was demonstrated under several charging and discharging conditions with a thermal storage capacity of 150 kWh th and a rapid discharge, making the TBC suitable for fast ramping when integrated with a fossil fuel fired power plant. Efficient radial heat transfer to the concrete was observed due to the well designed spacing and location of thermosiphons in the radial direction. A minimal temperature difference of 2 °C, between the thermosiphons bottom and top was obtained, demonstrating the isothermicity of those elements. An overall end-to-end TBC energy-to-energy round trip efficiency of 70% was achieved.

25 ENERGY STORAGE↗

DOE OSTI2025

Using X-ray radiography to study oxygen flow in a proton exchange membrane electrolyzer operating under balanced pressure conditions

Of the various water electrolyzer technologies, the proton exchange membrane electrolyzer (PEMWE) is one of the best solutions for producing clean hydrogen without releasing CO 2 . In order to allow for widespread use of clean hydrogen, it is necessary to decrease its cost, which is intrinsically related to system operation. Current PEMWE plants operate in differential mode, directly pressurizing hydrogen and benefiting from thermodynamic compression, which increases overall system efficiency. However, high differential pressure above 30 bar can cause membrane stress, resulting in membrane creeping and failure. Pressurizing the water and operating at balanced pressure allows hydrogen to be produced at higher pressures while preserving the integrity of the membrane and porous layers. Nevertheless, the impact of pressurizing water on PEMWE performance must be better understood to maximize performance under balanced pressure conditions. Here, this study examined the impact of water pressure on two-phase flow. A high-pressure electrolyzer setup was developed to perform operando X-ray radiography and examine oxygen transport with high temporal resolution. The imaging segmentation process, developed to capture bubble properties in the channel, was applied to a specific experiment. The results clearly showed that as pressure increased up to 30 bars, the initial bubbly flow transitioned to slug flow, which led to channel saturation with oxygen. This work demonstrates that two-phase flow in an electrolyzer can be studied using X-ray radiography, which has the advantages of fast measurements and the ability to probe dense materials, such as those required for pressurized electrolyzers.

Balanced high pressure operation↗

DOE OSTI2022

Risk-Informed Initiating Events and Accident Response

Emerging nuclear technologies and advanced reactor developments have brought proposed changes to the regulatory framework which can be leveraged to improve safety implementation and regulatory oversight processes at existing operating nuclear power plants. Specifically, opportunities exist to make plants’ regulatory compliance processes more efficient. This can be done by decreasing reliance on purely deterministic and prescriptive approaches and expansion of the use of risk informed and performance-based approaches to demonstrate reactor safety while achieving economic gains and efficiencies. In this report, we researched possibilities of economic benefits potentially available from the application of concepts associated with a modernized regulatory framework developed for advanced reactors to the existing light water reactors. We used timing factors to demonstrate the complexity of the existing risk assessments and described how these complexities affect regulatory compliance activities at the plants. We also investigated regulatory framework for both existing and new reactors and provided an overview of potential improvements. Lastly, we evaluated various areas of existing plant operations where modernization of compliance activities can offer substantial benefits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2024

Designing Payload and Spaceflight Operations for Plants From Extreme 1 Terrestrial Environments

Terrestrial plants from the edges of the limits of life are likely to harbor genes that confer an advantage in deep space environments. These plants are seemingly capable of performing mission critical functions under prevailing deep space conditions while informing directed gene manipulation in target plant species. However, their adaptations to physiologically extreme habitats may hinder efficacy of routine laboratory techniques established for model plants. Here we present the development of Antarctic moss Ceratodon purpureus payload and flight operations for the ARTEMOSS experiment to the ISS astute of limited physical space and crew time. We demonstrate that the hydrophobic surface of Antarctic moss impedes chemical tissue fixation and precludes usage of RNAlater coupled with payload hardware deployed in standard plant spaceflight experiments. We show that deep-freezing the moss tissue on Petri plates provides adequate tissue fixation and allows for extraction of high-quality RNA suitable for gene expression profiling. We replaced hardware with stacks of Petri plates housing Antarctic moss and chemical fixation with deep-freeze in cryogenic GLACIER freezer. Our design can be translated to other plant species, expanding current techniques of experimentation with plants from extreme terrestrial environments aimed toward advancing human space exploration.

Agata K Zupanska↗

DOE OSTI2024

Wind and structural loads data measured on parabolic trough solar collectors at an operational power plant

Abstract Wind loading is a primary contributor to structural design costs of concentrating solar-thermal power collectors, such as heliostats and parabolic troughs. These structures must resist the mechanical forces generated by turbulent wind, while the reflector surfaces must maintain optimal optical performance. Studying wind-driven loads at a full-scale, operational concentrating solar-thermal power plant provides insights into the wind impact on the solar collector field beyond the capabilities of wind tunnel tests or state-of-the-art simulations. We conducted comprehensive field measurements of the atmospheric turbulent wind conditions and the resulting structural wind loads on parabolic troughs at the Nevada Solar One plant over a two-year period. The measurement setup included meteorological masts and structural load sensors on four trough rows. Additionally, a lidar scanned the horizontal plane above the trough field. In this study, we describe the high-resolution dataset characterizing the complex flow field and resulting structural loads. This first-of-its-kind dataset will enhance the understanding of wind loading on collector structures and will help in designing the next-generation solar collectors and photovoltaic trackers.

14 SOLAR ENERGY↗

DOE OSTI2020

High Temperature Gas Sensor for Coal Combustion System

Robust gas sensors that offer real-time feedback about combustion in coal-fired power plants promise greater energy efficiency with fewer harmful emissions while also improving the bottom line for power generators. Currently, no technology meets this objective. Therefore, the objectives of this project are to develop an accurate, robust, reliable, high temperature sensor offering near real-time feedback to plant operators using a novel catalytic/non-catalytic sensor design to detect target gases available for oxidization. Such a sensor would inform power plant operators how to adjust the amount of fuel and air for an optimal performance.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1973

Pollution monitoring in Lake Champlain using ERTS-1 imagery

The author has identified the following significant results. Band 4 imagery of April 7 and 25 show contrasting pollution effects due to seasonal and discharge variations. The pollution plume emanating from the International Paper Co. mill just north of Fort Ticonderoga was first detected on October 10 ERTS-1 imagery and now has been documented during spring high lake level conditions. The plume was observed extending further to the north and east than under low water conditions of October 10. This northward extension reflects a stronger northward current flow expected in the turbid southern leg of Lake Champlain. The extensive plume of April 25 represents full plant operation while the April 5 scene shows some plume traces directly over the submerged diffuser, discharge pipe representing minimal discharge during weekend plant operation. The ERTS-1 documentation will be used in developing a model of plume behavior under varying environmental conditions and will hopefully serve to assist in a major resource decision pending at U.S. Supreme Court level.

Lind, A. O.↗

DOE OSTI2022

LCOE reduction through proactively optimized monitoring of PV Systems (Final Technical Report)

The project demonstrates the value proposition for a high-resolution monitoring system (HRMS) with diagnostic-prognostic capability and determine its impact on LCOE. The HRMS differs from conventional monitoring systems in a number of ways. First it will include the capability to automatically measure IV curves at the string and module levels. This provides a much richer view into the DC performance of the PV system and allows classification of many typical failure and degradation modes. Second, it will incorporate software capable of quantifying power and energy losses in the field as well as define the location and mechanism of the power and energy loss. Moreover, we aimed to deliver a prognostic system that is capable of predicting certain failures before they occur and giving system operators the opportunity to more efficiently plan operations and maintenance (O&M) activity in order to lower costs and increase yield over the life of the system. The research identifies cost targets required for different monitoring stages, including at the string combiner, at the individual string, and at the module level, to lower the levelized cost of energy (LCOE). Finally, a comprehensive guide determines the value PV monitoring brings to PV field operations. The guide assists plant operators in maximizing value from existing plants and identify the trade-offs of different monitoring solutions for future plants depending on the size, location, and expected system lifetime.

14 SOLAR ENERGY↗

DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

NPP Simulators for Coupled Thermal and Electric Power Dispatch

The Light Water Reactor Sustainability (LWRS) program within the United States Department of Energy supports extending the operation of the U.S. commercial nuclear power plant (NPP) fleet. Within the LWRS program, the Flexible Plant Operation and Generation (FPOG) Pathway works to diversify the revenue streams of light water reactors (LWRs) by opening opportunities for the co-generation of non-electric products in addition to supplying electrical power to the grid. Recent events have added greater motivation to these efforts. For example, the recent Inflation Reduction Act (IRA) passed by the U.S. federal government offers substantial tax incentives for producing clean hydrogen, the technology readiness level of dispatchable and high-efficiency hydrogen production has dramatically increased in a short time, and societal response to world climate change is driving a transition away from fossil fuels. Producing hydrogen with maximum efficiency using nuclear power requires dispatching both electrical and thermal power from the nuclear plant to the hydrogen plant, so testing concepts of operations for combined electrical and thermal power dispatch (TPD) from an NNP to a hydrogen plant is of interest. This report documents achievement of the Light Water Reactor Sustainability (LWRS) program milestone “Install and demonstrate a vendor-developed simulator on the Human Systems Simulation Laboratory (HSS) for dispatch of LWR electrical power to a close-coupled electrolysis plant” with a due date of Dec. 22, 2022. Several factors provide motivation for this effort. Coupling the power generation deck of a nuclear power plant to a hydrogen production facility introduces new possibilities for operational transients that must be addressed. In particular, the performance of the integrated system during startup and shutdown of the hydrogen production facility, as well as offnormal conditions, need to be evaluated to ensure there are no adverse effects on the operation of the existing NPP. The concept of operations involving the NPP, the hydrogen plant, and the electric power grid must be tested using NPP simulators and operating procedures that have been modified for TPD operations. These tests must also include dynamic simulations of the coupled tertiary thermal and electric loads as well as coordinated activities with NPP operators, tertiary load operators and grid power coordinators. The report summarizes progress in developing and testing full-scope NPP simulators at the HSSL, including a generic BWR simulator from GSE Systems, Inc. and generic PWR simulator from Westinghouse. In the case of the TPD-GBWR Simulator from GSE Systems, Inc., a BWR is thermally coupled to a high temperature electrolysis (HTE) plant that produces hydrogen and oxygen from de-ionized water. The hydrogen plant is not explicitly simulated but only included as a transient heat sink. A thermal power dispatch (TPD) system transfers heat between the steam systems at the BWR and the hydrogen plant. Operational results from two versions of the modified simulator are presented. The first version uses synthetic oil as a heat transfer fluid in a closed delivery heat loop (DHL) that generates steam at the hydrogen plant. The second version uses steam as the heat transfer fluid in a delivery steam line (DSL) to provide steam to the hydrogen plant. For both versions, the estimated thermal power delivery distance is approximately one kilometer. The amount of thermal power dispatched in the simulators is 15% of the total reactor thermal power such that the simulators provide a tool to study the feasibility of coupling a BWR to industrial processes that benefit from a combination electrical and thermal power dispatch. Ongoing work within a CRADA is also developing a full-scope PWR simulator provided by Westinghouse for both thermal and electric power coupling. This simulator is based on a PWR plant with two three-loop Westinghouse reactors. Westinghouse PWRs are sufficiently similar that a simulator of a three-loop reactor is an appropriate representation for two-loop and four-loop PWR reactors. The three-loop simulator will initially be modified for close-coupling to a 100 MW HTE hydrogen production plant that will require approximately 25 MW of thermal power while operating at its maximum rated capacity. The simulator testing will include full coupling to dynamic simulations of a hydrogen production plant and a representative bulk electric grid. The simulator provided by Westinghouse is similar to the GPWR simulator that INL has already obtained from GSE Systems but has a few important added benefits. First, the Westinghouse simulator is based on digital controls and has additional screens that can be called up to show parameter trends to assist operators in decision-making. The Westinghouse simulator also has upgrades to the controls and hardware representations, such as valve actuators, that make it more realistic and flexible in terms of accurately sim

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗

NASA NTRS2016

Remote Sensing and Modeling for Improving Operational Aquatic Plant Management

The California Sacramento-San Joaquin River Delta is the hub for California’s water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, water quality changes, and expansion of invasive aquatic plants threatens ecosystems, impedes ecosystem restoration, and is economically, environmentally, and sociologically detrimental to the San Francisco Bay/California Delta complex. NASA Ames Research Center and the USDA-ARS partnered with the State of California and local governments to develop science-based, adaptive-management strategies for the Sacramento-San Joaquin Delta. The project combines science, operations, and economics related to integrated management scenarios for aquatic weeds to help land and waterway managers make science-informed decisions regarding management and outcomes. The team provides a comprehensive understanding of agricultural and urban land use in the Delta and the major water sheds (San Joaquin/Sacramento) supplying the Delta and interaction with drought and climate impacts on the environment, water quality, and weed growth. The team recommends conservation and modified land-use practices and aids local Delta stakeholders in developing management strategies. New remote sensing tools have been developed to enhance ability to assess conditions, inform decision support tools, and monitor management practices. Science gaps in understanding how native and invasive plants respond to altered environmental conditions are being filled and provide critical biological response parameters for Delta-SWAT simulation modeling. Operational agencies such as the California Department of Boating and Waterways provide testing and act as initial adopter of decision support tools. Methods developed by the project can become routine land and water management tools in complex river delta systems.

Agriculture↗

DOE OSTI2023

A2E2G (Atmosphere to Electrons to the Grid platform) [SWR-23-22]

A2E2G is a platform that integrates 1) forecasting tools to account for weather uncertainty, with 2) aerodynamic wind plant models to account for wake dynamics and wind plant operation, and 3) economic models to advise on operation for a wind power plant that offers grid services in addition to energy. The A2E2G platform can be used as a high-level controller for a wind plant for market participation and real-time wind plant control. The A2E2g platform is a holistic Python tool with modules that can be run to 1) advise on market participation and 2) control and operate a wind power plant in real time. The A2E2g framework assumes two stages: the first stage is in day-ahead and the second stage is in real-time. Managing uncertainty is key in the first stage and managing variability is key in the second stage. The different components have models written and developed in the Python programming language. The code is assembled into a Python package and can be easily downloaded and installed from the A2E2g repository (https://github.com/NREL/a2e2g).

Sinner, Michael↗