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The Advanced Research on Integrated Energy Systems (ARIES) platform is one of the most powerful research environments anywhere for addressing the speed and scale of change on power systems. This research plan details ARIES capabilities, current and upcoming, and how they relate to objectives for advanced energy systems.
This poster highlights uncertainty and technical risk reduction capabilities in CCSI2, with a focus on robust optimization. It presents recent advances of the two-stage robust optimization (RO) solver PyROS and applications to advanced energy systems optimization. To demonstrate the computational performance and reliability of PyROS, a benchmarking study on a library of over 8,500 small-scale RO problems is presented. Further, PyROS is used to obtain robust system designs of a MEA-based CO2 absorber under uncertainty in the thermodynamic property models for a variety of CO2 capture rate threshold requirements. Overall, the results demonstrate that the PyROS solver, including recent extensions to multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainty.
This poster highlights uncertainty and technical risk reduction capabilities in CCSI2, with a focus on robust optimization. It presents recent advances of the two-stage robust optimization (RO) solver PyROS and applications to advanced energy systems optimization. To demonstrate the computational performance and reliability of PyROS, a benchmarking study on a library of over 8,500 small-scale RO problems is presented. Further, PyROS is used to obtain robust system designs of a MEA-based CO2 absorber under uncertainty in the thermodynamic property models for a variety of CO2 capture rate threshold requirements. Overall, the results demonstrate that the PyROS solver, including recent extensions to multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainty.
To accelerate progress toward the realization of advanced energy systems, this review explores the potential of pulsar technology to create a more stable, economical, and environmentally friendly energy infrastructure. Pulsars, with their precise and reliable timing characteristics, have emerged as a promising tool for enhancing energy systems. This review begins by examining the development history of pulsar technology, shedding light on its evolution and the milestones achieved. It then provides a comprehensive summary of the current state of research, highlighting recent advancements and breakthroughs in this field. It also explores transformative pulsar applications in energy systems, including improved grid stability, advanced energy synchronization, and efficient energy storage management. However, implementing pulsar-related technologies presents significant technical, economic, and operational challenges. This review examines these hurdles and proposes strategies to overcome them, emphasizing the need for innovation, interdisciplinary collaboration, and supportive policies to fully integrate pulsar technologies into sustainable energy systems.
As heterogeneous systems become increasingly popular for both mobile and high-performance computing, conventional efficiency techniques such as dynamic voltage and frequency scaling (DVFS) fail to account for the tightly coupled and varied nature of systems on a chip (SoCs). In this work, we explore the impact of system unaware DVFS techniques on a mobile SoC under three benchmark suites: Chai, Rodinia, and Antutu. We then analyze performance trends across the suites to identify a set of consistent operating points that optimally balance power and performance across the system. The consistent operating points are then constructed into a dependency graph which can be leveraged to produce a more effective, SoC-wide governor.
We report high-temperature fluids such as molten salts, liquid metals, and gasses are being proposed for many advanced energy systems including thermal energy storage devices, concentrating solar plants, and advanced nuclear reactor designs. However, the chemical behavior and thermophysical properties of many of these fluids have not been well characterized, which hinders the design, modeling, safety analysis, and deployment of these systems. Thermal conductivity is a property that is especially limited by existing measurement capabilities, which are subject to errors caused by convection, material interaction, radiative heat transfer, and instrument degradation. Therefore, there is a lack of standard, systematic measurement techniques for high-temperature, reactive, and corrosive fluids. In this work, the development of a variable gap thermal conductivity measurement system is detailed. The system is designed to measure the thermal conductivity of highly corrosive and reactive fluids, and survive operation between 100 °C and 800 °C. The effects of convection are minimized by limiting the thickness of the specimen to thin sizes (<0.3 mm). Corrections for radiative heat transfer were included in the working equations to consider specimens with varying optical properties. The design, construction, instrumentation, operating principles, and data analysis techniques are discussed in detail. The system was tested up to 500 °C using helium gas and molten KNO 3 -NaNO 3 to verify the measurement technique and determine the sources of error. At 300 and 400 °C KNO 3 -NaNO 3 , results showed maximum relative error of 6% when compared to results in the literature. The helium results were within 13% of those in the literature at 300 and 400 °C. Higher errors were observed at 500 °C for both fluids, and the sources of these errors are discussed.
While quantum computing (QC) is considered as a paradigm shift in our basic understanding of physical computation, effective implementation of QC in energy applications also depends on progress and development in the dimensions of both QC hardware and algorithms. To fully address the status and future challenges of QC applied within the energy sector, in this presentation, we firstly summarize recent advancements on the applications of QC to energy infrastructure and materials, complex energy system processes, advanced manufacturing, and energy system security. Then, we will demonstrate the results of QC performed both on a simulator and a quantum device targeting on energy-related applications.
Growing interest in quantum computing and simulations have created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. While quantum computing is considered as a paradigm shift in our basic understanding of physical computation, effective implementation of quantum computing in energy applications also depends on progress and development in the dimensions of both quantum computing hardware and quantum computing algorithms. To fully address the status and future challenges of quantum information science (QIS) applied within the energy sector, in this presentation, we firstly summarize recent advancements on the applications of quantum computing to energy infrastructure and materials, complex energy system processes, advanced manufacturing, and energy system security. Then, we will demonstrate the results of quantum computing both on a simulator and a quantum device accessing from OLCF. Our first example is to use the variational quantum eigensolver (VQE) with a unitary coupled cluster with singles and doubles (UCCSD) ansatz to simulate a series of LixHyq molecules (q=-1, 0, +1). The obtained results showed that the quantum computing VQE-UCCSD is comparable to classical CCSD for small systems like LiH with respect to full configuration interaction (FCI). Targeting on CO2 capture application, our second example is to use VQE to quantify molecular vibrational energies and reaction pathways between CO2 and a simplified amine-based solvent model—NH3 to form H2NCOOH. This research showcases quantum computing applications in the study of CO2 capture reactions.
This work describes the initial development of dynamic system models of the cryogenic moderator system (CMS) of the Spallation Neutron Source (SNS) at ORNL as a part of the ORNL LDRD funded project Building TRANSFORM to Accelerate Digital Twin Applications for Nuclear Systems, LOIS 10563. The goal of the work is to start the dynamic system modeling effort with the end goal of using them for real-time applications as digital twins. The CMS is a cryogenic liquid hydrogen flow loop that provides moderation of the neutrons that are generated by the SNS. For optimal neutron production, the CMS needs to maintain a steady and controlled density of cryogenic hydrogen in the moderator section thus requiring precise temperature and pressure control. Due to the varied time scales and system characteristics, control of the system is complex, and diagnostics are also difficult. Difficulty in accessing the flow loop during operations, limited instrumentation and unknown design details of the equipment combine to make the case for having sophisticated digital twin models of the system. Operationally the CMS also provides a strong use case for digital twins due to the constant need of optimization and for troubleshooting/diagnostics. The large amount of data collected which are freely available for using in building the model and verifying and validating the model also makes it a great candidate for a proof-of-concept for digital twins. The project extends ORNL's capacity of development and implementation of the open-source dynamic system modeling tool TRANSFORM for engineering design and digital twin/real-time applications. Specific system configuration data for the CMS have been gathered and an initial dynamic model was created in the TRANSFORM library using Dymola as the solution platform. Models of increasing complexity are created to demonstrate the need for a multi-layered approach in digital twin modeling depending on the scale and phenomena being focused on. The dynamic modeling is shown to bring the dynamic operational aspects to the design process for systems as well as serve as a digital twin to the hardware and allow for models to be tuned and compared against real time operational data. These aims should help to push forward strategic goals of application of digital twins and increase the impact of ORNL systems modeling capabilities with TRANSFORM/Modelica for various advanced energy systems.
Overview presentation to be used in multiple venues to discuss the current state of advanced nuclear energy and integrated energy systems.
Advanced, high-performance computing at the National Renewable Energy Laboratory (NREL) has enabled access to vast data resources with cutting-edge software techniques to understand, design, plan for, and maintain energy-efficient and resilient infrastructure. We have focused on cities and airports, but the technology we have developed will easily translate to seaports, inland ports, military installations, or other complex and large-scale energy-intensive systems. We can digitally simulate and explore current and future scenarios to make datadriven decisions for optimizing advanced energy systems, transportation and building operations, infrastructure planning and expansion, and battery storage to guide short- and long-term investments, electrification strategies, and integration of new technologies.
Decarbonization efforts across North America, Europe, and beyond rely on variable renewable energy sources such as wind and solar, as well as alternative fuels, such as hydrogen, to support the sustainable energy transition. These advancements have prompted a need for more flexibility in the electric grid to complement non-dispatchable energy sources and increased demand from electrification. Integrated energy systems are well suited to provide this flexibility, but conventional technoeconomic modeling paradigms neglect the time-varying dynamic nature of the grid and thus undervalue resource flexibility. In this work, we develop a computational optimization framework for dynamic market-based technoeconomic comparison of integrated energy systems that coproduce low-carbon electricity and hydrogen (e.g., solid oxide fuel cells, solid oxide electrolysis) against technologies that only produce electricity (e.g., natural gas combined cycle with carbon capture) or only produce hydrogen. Our framework starts with rigorous physics-based process models, built in the open-source Institute for the Design of Advanced Energy Systems (IDAES) modeling and optimization platform, for six energy process concepts. Using these rigorous models and a workflow to optimally design each technology, the framework is shown to be capable of evaluating new and emerging technologies in varying energy markets under a plethora of future scenarios (i.e., renewables penetration, carbon tax, etc.). Ultimately, our framework finds that solid oxide fuel cell-based coproduction systems achieve positive profits for 85% of the analyzed market scenarios. From these market optimization results, we use multivariate linear regression (R 2 values up to 0.99) to determine which electricity price statistics are most significant to predict the optimized annual profit of each system. The proposed framework provides a powerful tool for directly comparing flexible, multi-product energy process concepts to help discern optimal technology and integration options.
2021 Crosscutting Research and Advanced Energy Systems Project Review Meeting, Virtual, May 4-5, 2021
This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.
This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.
In 2022 Congress authorized the $25M National Energy Technology Laboratory Direct Air Capture Center. The facility will be specifically targeted at accelerating the commercialization of technologies beyond the conceptual stage which have not yet reached full pilot-scale (TRL 3 to 6). The ability to operate over a wide range of conditions will help the developers to understand how their technologies respond in different climates, from summer to winter and arid to tropical. NETL recognizes that DAC technology is still early in its evolution, and a wide variety of highly varied technologies are being explored. The Center will be designed with substantial flexibility to accommodate the rapidly evolving technological landscape. Testing systems at three scales will be included: lab-scale systems designed to examine the long-term stability of DAC materials, bench-scale module testing systems capable of probing flow dynamics, and small pilot-scale skid rooms able to test prototype DAC units under a wide variety of climate conditions. The NETL DAC Center will feature dedicated engineering, technician, scientific and logistical support for experimental system design, installation, operation of experiments, and interpretation of results for projects including but not limited to kinetics of absorption/desorption, thermal duty, and post-mortem elemental characterization to understand degradation. The NETL DAC Center projects will also have access to dedicated process modeling and analysis to evaluate the technoeconomic aspects of new technologies. NETL’s Systems Engineering and Analysis (SEA) team, the Carbon Capture Simulation Initiative (CCSI), and the Institute for the Design of Advanced Energy Systems (IDAES) are all potential collaborators with NETL’s testing partners from industry, academia, and other research institutions. NETL also has world class capabilities in device scale modeling (i.e., MFIX) that will be available to help design more efficient contactors and optimize unique internal configurations that can be realized by advanced manufacturing. The talk will cover the design and capabilities of the center, the planned availability, and feedback will be solicited from the audience on DAC testing needs.
In 2022 Congress authorized the $25M National Energy Technology Laboratory Direct Air Capture Center. The facility will be specifically targeted at accelerating the commercialization of technologies beyond the conceptual stage which have not yet reached full pilot-scale (TRL 3 to 6). The ability to operate over a wide range of conditions will help the developers to understand how their technologies respond in different climates, from summer to winter and arid to tropical. NETL recognizes that DAC technology is still early in its evolution, and a wide variety of highly varied technologies are being explored. The Center will be designed with substantial flexibility to accommodate the rapidly evolving technological landscape. Testing systems at three scales will be included: lab-scale systems designed to examine the long-term stability of DAC materials, bench-scale module testing systems capable of probing flow dynamics, and small pilot-scale skid rooms able to test prototype DAC units under a wide variety of climate conditions. The NETL DAC Center will feature dedicated engineering, technician, scientific and logistical support for experimental system design, installation, operation of experiments, and interpretation of results for projects including but not limited to kinetics of absorption/desorption, thermal duty, and post-mortem elemental characterization to understand degradation. The NETL DAC Center projects will also have access to dedicated process modeling and analysis to evaluate the technoeconomic aspects of new technologies. NETL’s Systems Engineering and Analysis (SEA) team, the Carbon Capture Simulation Initiative (CCSI), and the Institute for the Design of Advanced Energy Systems (IDAES) are all potential collaborators with NETL’s testing partners from industry, academia, and other research institutions. NETL also has world class capabilities in device scale modeling (i.e., MFIX) that will be available to help design more efficient contactors and optimize unique internal configurations that can be realized by advanced manufacturing. The talk will cover the design and capabilities of the center, the planned availability, and feedback will be solicited from the audience on DAC testing needs.
DOE and NETL have created an advanced, open-source computational platform through the Institute for the Design of Advanced Energy Systems (IDAES). The IDAES platform is a very extensive modeling environment that can be used for a broad range of power plant and process applications. MapEx Software is developing and commercializing a software application that makes it easier to set-up and run IDAES-based analyses. The MapEx-developed software application will replace the need for custom Python language coding with a user-friendly graphical user interface where the user can construct a flowsheet diagram of the IDAES model by inserting icons representing the plant equipment onto the screen. This application will make the implementation of modeling and optimization of existing fossil-fired power plants more straight-forward and less time-consuming. The effort focuses on performance monitoring and optimization of plant operations for the existing coal-fired power plant fleet but is built on a structure that allows expansion into the broad range of applications where IDAES methods may be applied.