Results for “Power Plant” — Page 38

DOE OSTI2022

Development of novel dynamic machine learning-based optimization of a coal-fired power plant

The increasing fraction of intermittent renewable energy in the electrical grid is resulting in coal-fired boilers now routinely ramp up and down. The current state-of-the-art operation for such boilers is to apply steady-state, neural network-based optimization to make control decisions in real-time, and this report demonstrates the feasibility of extending this to dynamic, neural network-based optimization using a long short-term memory neural network. A simplified numerical simulation of a t-fired coal boiler and supporting equipment is used to represent a real plant subjected to both steady-state, neural network-based optimization and dynamic, neural network-based optimization. Using the same intervals and a particle swarm optimization algorithm, the dynamic optimization outperforms the steady-state optimization and realizes up to 4.58% improvement in thermal efficiency. Dynamic optimization with a long short-term memory neural network is shown to both be feasible and beneficial for operation of a coal-fired boiler under changing load.

42 ENGINEERING↗

DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1981

The application of simulation modeling to the cost and performance ranking of solar thermal power plants

A computer simulation code was employed to evaluate several generic types of solar power systems (up to 10 MWe). Details of the simulation methodology, and the solar plant concepts are given along with cost and performance results. The Solar Energy Simulation computer code (SESII) was used, which optimizes the size of the collector field and energy storage subsystem for given engine-generator and energy-transport characteristics. Nine plant types were examined which employed combinations of different technology options, such as: distributed or central receivers with one- or two-axis tracking or no tracking; point- or line-focusing concentrator; central or distributed power conversion; Rankin, Brayton, or Stirling thermodynamic cycles; and thermal or electrical storage. Optimal cost curves were plotted as a function of levelized busbar energy cost and annualized plant capacity. Point-focusing distributed receiver systems were found to be most efficient (17-26 percent).

Rosenberg, L. S.↗

DOE OSTI2021

Dynamic Human-in-the-Loop Simulated Nuclear Power Plant Thermal Dispatch System Demonstration and Evaluation Study

An Idaho National Laboratory research team performed a human-in-the-loop study with two formerly licensed retired operators to evaluate thermal power dispatch operations supported by the modified GSE Systems GPWR plant simulator. Virtual representations of the analog control panels were presented on touch-screen bays configured to mimic the control room layout in the newly renovated Human Systems Simulation Laboratory. The operators performed 15 scenarios covering normal evolutions to transition the plant from full turbine operation to joint turbine and thermal power dispatch operations in addition to transient response scenarios induced with simulated faults to evaluate the impact of thermal power dispatch system on operator and plant responses. A prototype human-system interface (HSI) was developed and displayed in tandem with the virtual analog panels to support the operators executing the procedurally drive evolutions and transient responses. An interdisciplinary team of operations experts, nuclear engineers, and human factors experts observed the operators performing the scenarios to evaluate the operations. Only a preliminary analysis of the results has been performed. The full analysis will be shared in a milestone report scheduled for release at the end of September; however, some high-level conclusions were readily available. Two high level findings for the system design were captured in the study. The manual control supported by the HSI to transition from standard operations to thermal power dispatch operation imposed a considerable amount of workload on the operators due to tedious manual valve manipulations and system monitoring required to verify their intended effect. An additional operator would be required in the control room to support the daily evolution. Automatic control for the transition was deemed a requirement for plant adoption without imposing additional staffing costs. The second finding was the necessity for an automatic thermal power dispatch system trip isolation function linked to a turbine and reactor trip signal. The operators completed scenarios with automatic isolation functionality and manually required actuation of the thermal power dispatch system. The operator response was sufficiently slower in the manual trip condition, such that operators were unable to manually actuate key post trip safety functions an indicate a degraded control capability that should be avoided. With an automatic trip signal little to no impact of the thermal power dispatch system was identified on the primary plant response and therefore the system could be readily and safely adopted. Together these two findings represent the need to support the adoption of thermal power dispatch capability into existing operations by leveraging automation to augment any additional operator tasking required to control and monitor an additional system beyond existing operations.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

USE OF COGNITIVE WORK ANALYSIS IN DEVELOPING A NUCLEAR POWER PLANT NEW STATE VISION

The United States nuclear industry is in need of identifying and implementing a new strategy that will lower operating and maintenance costs while maintaining safety for existing plants. The industry must also have a clear and strategic vision of their transformative new state that focuses on ways in which technologies can be integrated to maximize the benefits of technology and people. While there are ongoing efforts in this area, this work discusses how the use of cognitive work analysis may further support these current efforts. This paper provides an overview of cognitive work analysis, as well as the state of current new state development efforts. The use of cognitive work analysis, as a tool to enhance existing practices, is presented in ensuring that the development of a new state vision is systematic and complete.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Dynamic security assessment of systems powered only by grid-forming power plants with uncertain dispatch using polynomial vectors

A modern challenge in power engineering is to perform the dynamic security assessment (DSA) of grids that are 100% powered by inverter-based resources (IBRs). Addressing this challenge is difficult because: (i) the dispatch of IBRs can be uncertain as a result of the variability of renewable resources and (ii) they have hard current control limits that cannot be neglected, contrasting synchronous machines. To address this problem, this paper sets forth a framework to conduct DSA of bulk power systems that are 100% powered by grid-forming IBRs. Furthermore, the framework considers that IBR operational conditions are unknown but bounded by a zonotope which is also expressed as a polynomial vector for uncertainty propagation via Dormand–Prince integration. The framework is applied to modified versions of the WSCC 9-bus and IEEE 39-bus grids.

14 SOLAR ENERGY↗

DOE OSTI2024

FIDVR Capability of Hybrid Grid-Forming PV Power Plants During Feeder Restoration

Grid-forming inverters must optimally transfer power from dc-coupled photovoltaic arrays and batteries into an ac grid. Further, they must be able to restore single-phase induction motors (SPIMs) and withstand fault-induced delayed voltage-recovery (FIDVR) events. These resilience and reliability challenges are addressed here by: (i) engineering a controller to optimally operate dc-coupled hybrid resources; (ii) modeling residential air-conditioning compressors for restoration/FIDVR studies; and (iii) analyzing SPIM thermal-relay performance under limited inverter currents and designing an electronic protection for stalled SPIMs. Furthermore, these contributions are demonstrated via electromagnetic-transient simulations and can be helpful to understand recommendations by the North American Electric Reliability Corporation.

14 SOLAR ENERGY↗

DOE OSTI2020

Fossil Power Plant Cyber Security Life-Cycle Risk Reduction: A Practical Framework for Implementation

Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Fossil Power Plant Cyber Security Life-Cycle Risk Reduction: A Practical Framework for Implementation

Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Treatment of brackish water for fossil power plant cooling

In this study, we evaluated the technical, economic and environmental impacts of retrofitting brackish groundwater treatment systems at existing coal- and gas-fired electric generating units (EGUs) to reduce freshwater consumption in wet cooling towers. Based on fleet averages, retrofitting brackish water treatment systems decreases unit freshwater consumption by 94–100%, while increasing the cost of electricity generation by 8–10%. The unit capacity shortfalls are less than 1.1%. The resulting cost of freshwater consumption savings by brackish water treatment is US$1.7 m -3 and US$2.9 m -3 on average for coal- and gas-fired EGUs, respectively. However, these trade-offs are highly affected by the brine disposal method. The use of thermal zero liquid discharge for brine disposal can roughly double the average cost of freshwater consumption savings. The cost-effectiveness of brackish water treatment compared with dry cooling deployment depends on how concentrated brines are managed. In conclusion, the identified trade-offs and their dependence fill knowledge gaps to better inform water management.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Fossil Power Plant Cyber Security Life-Cycle Risk Reduction, A Practical Framework for Implementation

Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Mid-height seismic isolation of equipment in nuclear power plants: Numerical simulations and design recommendations

Seismic isolation can mitigate earthquake demands on safety-class equipment and is being considered for application to some advanced nuclear reactors. For tall, slender vessels that could represent an advanced reactor, a steam generator or a heat exchanger, mid-height seismic isolation has been shown to be beneficial and practical. Here, this paper focuses on numerical modeling of a mid-height isolated tall, slender vessel with two primary goals 1) investigate and quantify the benefits of mid-height seismic isolation for a range of support structure stiffnesses, isolation systems, and seismic inputs, and 2) provide analysis and design recommendations for isolation of safety-class equipment. Results of response-history analysis were in good agreement with experimental measurements and demonstrated that mid-height isolation can substantially reduce seismic demands on tall, slender vessels for a range of support structure stiffnesses, isolation systems, and seismic inputs. Importantly, the reductions in horizontal spectral accelerations in the mid-height isolated vessel from the non-isolated condition were not affected by the stiffness of the support structure. Recommendations are made for analysis and design of isolated equipment and testing of isolators used for equipment protection in nuclear facilities, which are also applicable to other industries.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Exploring the Feasibility of INCONEL® ALLOY 740H® for Power Plant Headers: Integrating Machine Learning with Computational Fluid Dynamics (CFD)

This keynote presentation explores the behavior of headers—essential components of pipeline systems—using ANSYS simulation software and machine learning techniques. The study aims to predict the thermal and mechanical performance of headers under diverse conditions through both steady-state and transient simulations. We investigate critical parameters such as heat transfer coefficient, fluid velocity, and temperature to optimize header design. Conducted as part of a DOE project led by NCAT in collaboration with UNC Charlotte, this research encompasses multiple key topics. The initial section focuses on the behavior of header systems under steady-state conditions using ANSYS simulation. It underscores the importance of headers in industrial infrastructure, especially in the energy sector, and examines the implications of material selection and flow direction on heat transfer dynamics. Methodologically, we employ Computational Fluid Dynamics (CFD) analysis through ANSYS, detailing the development of models, material properties, geometry specifications, boundary conditions, and meshing strategies. Our simulations explore various operational parameters, including temperature and mass flow rates, crucial for predicting heat transfer coefficients and enhancing header design. Results from the study include parametric investigations into mesh sensitivity, viscosity model evaluations, and the effects of heat transfer locations, all validated against theoretical calculations. We conclude with insights on mesh optimization, the suitability of viscosity models, and recommendations for future research aimed at improving header system efficiency and sustainability in industrial applications.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Hybrid power plant design for low-carbon hydrogen in the United States

In this study, we provide a nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. We leverage the open-source Hybrid Optimization Performance Platform (HOPP) tool to simulate the hourly performance of an off-grid wind-solar plant integrated with a 1-GW polymer exchange membrane electrolyzer system. The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to explore cost sensitivities independent of plant design, performance, and site selection. Our findings suggest that strategies for cost reduction include selecting sites with abundant wind resources, complementary wind and solar resources, and optimizing the sizing of wind and solar assets to maximize the hybrid plant capacity factor. These strategies are linked to increased hydrogen production and reduced electrolyzer stack replacements, thereby lowering the overall cost of hydrogen.

08 HYDROGEN↗

DOE OSTI2020

CO 2 to Bioplastics: Beneficial Re-use of Carbon Emissions from Coal-fired Power Plants using Microalgae

This project sought to address the technical and economic barriers to carbon dioxide (CO 2 ) capture and utilization using microalgae. Specifically, a dual photobioreactor (PBR)/open raceway pond (ORP) cultivation system was evaluated with respect to capital and operational costs, productivity, and culture health, and compared to an ORP-only cultivation system. In the dual (or “hybrid”) system, a cyclic flow PBR was used to provide inoculum for two open raceway ponds after each harvest, with the hypothesis that this PBR + pond system should result in improved performance compared to traditional raceway ponds. Two other raceway ponds were operated conventionally as a control, the experiments being performed at Duke Energy’s East Bend Station in northern KY using coal-derived flue gas as the CO 2 source. Although algae productivity achieved was mediocre due to the fact that the experiments had to be performed late in the growing season when climatic conditions were not optimal for high growth, the hybrid cultivation system showed a higher level of algae productivity (statistically significant) compared to the traditional ponds. In order to realize the maximum value of the algal biomass produced, fractionation of the biomass was examined. Bioplastic compounding and material characterization was subsequently performed using three feed stocks: whole biomass, lipid-extracted biomass and the proteinaceous residue from full fractionation. In general, the whole and lipid-extracted biomass gave similar results in terms of the properties of the resulting bioplastics, while the proteinaceous residue showed promise for the production of a polybutylene adipate terephthalate (PBAT) blend. Finally, sustainability assessment of a putative biorefinery system was conducted, including techno-economic and life cycle impact assessment. Nine different production scenarios were considered, comprising combinations of the three different biomass growth architectures (ORP, PBR and dual PBR-ORP systems) coupled with three different algae biomass processing pathways (drying only, lipid extraction and fractionation). Results show that the minimum selling price of the bioplastic feed stock (BPFS) is within the realm of economic competition with prices as low as $970 USD tonne -1 . Additionally, life cycle impact assessment results indicate drastic improvements in performance of the produced BPFS, with reductions in greenhouse gas emissions ranging between 67 and 116% compared to a petroleum based plastic feedstock.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Evaluation of thermal energy storage for central receiver concentrating solar power plants under charging cycles through structural assessment

Electricity production by concentrated solar power (CSP) systems has stood out among energy transition alternatives due to their large generation capacity (between 30 MW and 400 MW) and high capacity factor (80%) when incorporating thermal storage systems (TES). Although TES tanks in operation worldwide are made of austenitic steels with remarkable mechanical and anticorrosive properties, operational failures have been reported due to low-cycle fatigue, creep, and the abrupt release of residual stresses generated during tank manufacturing. A critical operation for salt tanks is the initial daily charging operation, given the low fluid level and the thermal gradients between the inventory and the salt entering the tank. This study presents a structural simulation of a 39.6 m-diameter molten salt tank for central receiver CSP plants, accounting for temperature and pressure variations during charging and different sparger ring inlet configurations. The initial pre-stressed condition of the floor, resulting from the manufacture of welded plates, is used as a boundary condition. The fatigue and creep life analyses were performed in accordance with the ASME BPVC and API standards. It was found that the tank's floor is in a less favourable mechanical condition than the wall, due to its initial manufacturing condition and the maximum temperature differences during charging. A critical zone was identified in an area affected by residual stress on the floor and under the sparger ring. The baseline sparger ring configuration, with 52 2-inch-diameter orifices and a flow direction of 90 degrees , results in an average floor stress of approximately 25 MPa and creep damage after 1.2 years of tank operation.

14 SOLAR ENERGY↗

DOE OSTI2022

AOI.1 Application of Artificial Intelligence techniques enabling coal fired power plants the ability to achieve higher efficiency, improved availability, and increased reliability of their operations (Final Report)

During this effort, SparkCognition with support from the Electric Power Research Institute (EPRI) was tasked with applying artificial intelligence (AI) to improve the reliability, efficiency, and safety of operations at a coal-fired plant. By implementing AI techniques, like machine learning (ML), it is believed that operators can leverage existing data sources to gain more insights such as advanced warning of machine degradation. With enough lead time, a reliability engineer can take action to minimize, or even avoid, impact to production. To complete this work effort, SparkCognition developed and refined an ML-based model using sensor data for a Steam Turbine unit at a host site. The models were deployed in an online, web-based solution that allows users to visualize model outputs and supporting data. The final solution, based on SparkCognition’s proprietary software platform called SparkPredict®, was shared with EPRI who completed an online evaluation of results to determine the solution’s ability to detect actionable events.

20 FOSSIL-FUELED POWER PLANTS↗