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At least 91 records · Page 5

Optimization of Integrated Energy Systems

Integrated energy systems that couple nuclear power plants with additional products including hydrogen, storage, or synthetic fuels provide a more flexible energy source that can be more economical than generating electricity alone. Determining the size and shape of these systems and optimizing their operation is challenging. INL, through the IES program, has developed optimization software to help solve these challenges. Holistic Energy Resource Optimization Network (HERON) is a software tool to optimize the size and capacity of integrated energy systems using stochastic optimization. Optimization of Real-time Capacity Allocation (ORCA) is a software tool under development to perform real-time economic optimization of these systems using economic model predictive control. An overview of these tools and a discussion of their optimization methods will be presented in this talk.

97 MATHEMATICS AND COMPUTING↗

Energy Systems Integration Facility Stewardship Summary: Fiscal Year 2022

A summary of NREL's good stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, infrastructure and capability upgrades, and examples of R&D impact. In fiscal year 2022, ESIF researchers made breakthroughs in everything from long-duration energy storage and cybersecurity visualizations to a world record in heavy-duty hydrogen vehicle fueling, and built out research assets to advance microgrid operation and controls, renewable hydrogen production, electric vehicles, energy-efficient buildings, and more.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

WISDEM® v3.4.1(Wind Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nlr.gov/wind/systems-engineering

Dykes, Katherine↗

Industrial Scale Energy Systems Integration; NREL (National Renewable Energy Laboratory)

The industrial sector consumes 25% of the total energy in the U.S. and produces 18% of the greenhouse gas (GHG) emissions. Energy Systems Integration (ESI) opportunities can reduce those values and increase the profitability of that sector. This presentation outlines several options. Combined heat and power (CHP) is an option that is available today for many applications. In some cases, it can be extended to trigeneration by adding absorbtion cooling. Demand response is another option in use by the industrial sector - in 2012, industry provided 47% of demand response capacity. A longer term option that combines the benefits of CHP with those of demand response is hybrid energy systems (HESs). Two possible HESs are described and development implications discussed. extended to trigeneration by adding absorbtion cooling. Demand response is another option in use by the industrial sector - in 2012, industry provided 47% of demand response capacity. A longer term option that combines the benefits of CHP with those of demand response is hybrid energy systems (HESs). Two possible HESs are described and development implications discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Robotic Systems integration for cleanroom SRF assembly at Fermilab

Superconducting Radio Frequency (SRF) cryomodule assembly is a critical process that demands the highest levels of contamination control in a cleanroom to ensure product quality and reliability. Within the cleanroom environment, human-driven activities remain the major source of uncontrolled particulate. Moreover, the precise manual alignment of components often exceeds expected timeframes, increasing the risk of particulate migration into the critical inner space of the SRF resonant cavity, and causing extended discomfort for workers. Automation comes as a natural solution to address these challenges. The goal of this activity is to set up and integrate a robotic system to support cavity coupler installation in the cleanroom environment. Initially, remote control of a 6-axis collaborative robot arm will be implemented as an introductory step towards automation in the cleanroom. Subsequently, the system will be further enhanced by integrating manipulation capabilities through an appropriate end-effector, and computer vision technology to enable accurate pose estimation. This comprehensive approach aims to achieve a fully automated and adaptable assembly process in the partially structured cleanroom environment, thereby enhancing efficiency and mitigating existing challenges for better product quality and workers safety.

42 ENGINEERING↗

New Industry Partnerships: Solar Energy Technologies Office Support for Early Projects in the Energy Systems Integration Facility: An Agreement Closeout Summary Report

The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office (SETO)-funded New Industry Partnerships (NIPs) agreement established multiple new cooperative research projects with industry partners. Each project demonstrated the use of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF) as a national asset for research and development, testing, and validation of new technologies to support high penetrations of solar energy on the electric grid. The agreement was launched in 2013 and represented the SETO portion of the DOE EERE-wide Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) program. The focus of INTEGRATE was on multi-energy system testing and on testing the interactions of energy systems with information technology, communications, and telecommunications (ICT) systems. The NIPs agreement was a bit different from traditional research projects because it was specifically structured to enable partnerships for ESIF testing and as such required at least a 1:1 funds-in cost share from industry partners. In the end, the project engaged six different industry partnerships and resulted in testing a large number of innovative technologies. These included tiny inverters; power-to-gas technologies; and advanced simulation, analysis, and power-hardware-in-the-loop testing techniques. The agreement also provided significant impact for both research and industrial communities. This summary report provides a brief, high-level overview of these projects and their highlights along with lists of citations and other impacts. Readers are referred to the corresponding project reports for more in-depth information.

14 SOLAR ENERGY↗

Integrated System and Application Continuous Performance Monitoring and Analysis Capability

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

97 MATHEMATICS AND COMPUTING↗

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

ESIF User Guide: Energy Systems Integration Facility

The ESIF User Guide provides information NREL's campus, safety, and ESIF user responsibilities while performing research at NREL's Energy Systems Integration Facility (ESIF). The ESIF is a U.S. Department of Energy user facility, available to the research community and open to qualified individuals from institutions around the world.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Wind Systems Integration Workshop

The U.S. Department of Energy’s Wind Energy Technologies Office (WETO) Wind Systems Integration Workshop was held to facilitate an exchange of information and to solicit feedback to inform WETO’s near- to mid-term research priorities and to accelerate near-term, rapid deployment and integration of wind technologies at both the transmission and distribution levels. Workshop participants identified key research challenges and opportunities for grid services, power electronics, modeling and decision-support tools, transmission and distribution system coordination, and applying energy equity principles to wind grid integration research.

17 WIND ENERGY↗

WISDEM® (Wind-Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, https://openmdao.org/, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nlr.gov/wind/systems-engineering

Ning, S.Andrew↗

WISDEM® v3.15.2 2024 (Wind-Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nrel.gov/wind/systems-engineering.html

Dykes, Katherine↗

Energy Systems Integration Facility (ESIF) Facility Stewardship Plan: Revision 2.1

The U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), has established the Energy Systems Integration Facility (ESIF) on the campus of the National Renewable Energy Laboratory (NREL) and has designated it as a DOE user facility. This 182,500-ft2 research facility provides state-of-the-art laboratory and support infrastructure to optimize the design and performance of electrical, thermal, fuel, and information technologies and systems at scale. This Facility Stewardship Plan provides DOE and other decision makers with information about the existing and expected capabilities of the ESIF and the expected performance metrics to be applied to ESIF operations. This plan is a living document that will be updated and refined throughout the lifetime of the facility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimal feed flow sequence for multi-effect distillation system integrated with supercritical carbon dioxide Brayton cycle for seawater desalination

Multi-effect distillation is often integrated with a Rankine power cycle for cogeneration (simultaneous production of power and desalinated water). Such integration increases the condenser operating pressure for the Rankine cycle (and increases the heat-rejection temperature) to produce desalinated water, resulting in decreased power-plant efficiency. The supercritical carbon dioxide Brayton cycle has a higher efficiency compared to the Rankine cycle. The waste heat rejected from supercritical carbon dioxide Brayton cycle is at sufficiently hot temperature to have feasible energy integration with multi-effect distillation system. The paper introduces the novel concept of cogeneration without being a parasitic load to the power cycle. For the illustrative example considered, integrating 4-effect distillation system with a 115 MWe power plant can produce 3041 m3 of distillate per day at 1.06 $/m3, at a constant power plant efficiency of 49.2%. The pattern in which feed enters the desalination system often dictates its energy consumption. The two most commonly used feed configurations for multi-effect distillation system are parallel/cross feed and forward feed. With steam as a heat source (i.e., a latent heat source), parallel/cross feed is the most energy-efficient feed configuration. The other objective of this paper is to identify the optimal feed flow configuration for multi-effect distillation system integrated with a supercritical carbon dioxide power cycle (i.e., a sensible heat source). For the simplified network, forward feed is the best feed configuration, which yields a 7.5% increase in distillate production at 2.6% reduced distillate cost. Additionally, different methods for reducing the brine discharge are studied, which can help to achieve zero liquid discharge. Result show increasing the maximum brine concentration gives superior results compared to brine recycling. The system modelling is done using the principle of process integration, and an analytical methodology for cogeneration is derived.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Smart Homes and Buildings Research at the Energy Systems Integration Facility

Watch how NREL researchers are using the unique capabilities of the Energy Systems Integration Facility (ESIF) to develop technologies that will help the “smart” homes and buildings of the future perform efficiently and communicate effectively with the electricity grid while enhancing occupants' comfort and convenience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗