Optimal Sizing of Behind-the-Meter Energy Storage with Stochastic Load and PV Generation for Islanded Operation
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Scientific computing workloads at HPC facilities have been shifting from traditional numerical simulations to AI/ML applications for training and inference while processing and producing ever-increasing amounts of scientific data. To address the growing need for increased storage capacity, lower access latency, and higher bandwidth, emerging technologies such as non-volatile memory are integrated into supercomputer I/O subsystems. With these emerging trends, we need a better understanding of the multilayer supercomputer I/O systems and ways to use these subsystems efficiently. In this work, we study the I/O access patterns and performance characteristics of two representative supercomputer I/O subsystems. Through an extensive analysis of year-long I/O logs on each system, we report new observations in I/O reads and writes, unbalanced use of storage system layers, and new trends in user behaviors at the HPC I/O middleware stack.
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Presentation for NAPS paper
In this project, we developed a set of high-fidelity modeling and optimization algorithms to enable conversion of individual homes into grid-integrated buildings, and a community of homes into a virtual power plant (VPP).
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A lighting apparatus (100) includes one or more first LEDs (202) for generating a first spectrum of radiation (503), and one or more second LEDs (204) for generating a second different spectrum radiation (505). The first and second LEDs are electrically connected in series between a first node (516A) and a second node (516B), between which a series current (550) flows with the application of an operating voltage (516) across the nodes. A controllable current path (518) is connected in parallel with one or both of the first and second LEDs so as to at least partially divert the series current, such that a first current (552) through the first LED(s) and a second current (554) through the second LED(s) are different. Such current diversion techniques may be employed to compensate for shifts in color or color temperature of generated light during thermal transients, due to different temperature-dependent current-to-flux relationships for different types of LEDs.
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In response to the U.S. Department of Energy (DoE)’s goal of achieving market ready, net-zero energy residential and commercial buildings by 2020 and 2025, Eaton partnered with the Department of Energy’s National Renewable Energy Laboratory (NREL) and Georgia Institute of Technology to develop an intelligent load identification and management technology enabled by a novel “smart power strip” to provide critical intelligence and information to improve the capability and functionality of building load analysis and building power management systems. Buildings account for 41% of the energy consumption in the United States, significantly more than either transportation or industrial. Within the building sector, plug loads account for a significant portion of energy consumption. Plug load consumes 15-20% of building energy on average. As building managers implement aggressive energy conservation measures, the proportion of plug load energy can increase to as much as 50% of building energy leaving plug loads as the largest remaining single source of energy consumption. This project focused on addressing plug-in load control and management to further improve building energy efficiency accomplished through effective load identification. The execution of the project falls into the following three major aspects; An intelligent load modeling, identification and prediction technology was developed to automatically determine the type, energy consumption, power quality, operation status and performance status of plug-in loads, using electric waveforms at a power outlet level. This project demonstrated the effectiveness of the developed technology through a large set of plug-in loads measurements and testing; A novel “Smart Power Strip (SPS) / Receptacle” prototype was developed to act as a vehicle to demonstrate the feasibility of load identification technology as a low-cost, embedded solution; and Market environment for plug-in load control and management solutions, in particular, advanced power strips (APSs) was studied. The project evaluated the market potential for Smart Power Strips (SPSs) with load identification and the likely impact of a load identification feature on APS adoption and effectiveness. The project also identified other success factors required for widespread APS adoption and market acceptance. Even though the developed technology is applicable for both residential and commercial buildings, this project is focused on effective plug-in load control and management for commercial buildings, accomplished through effective load identification. The project has completed Smart Receptacle (SR) prototype development with integration of Load ID, Control/Management, WiFi communication, and Web Service. Twenty SR units were built, tested, and demonstrated in the Eaton lab; eight SR units were tested in the National Renewable Energy Lab (NREL) for one-month of field testing. Load ID algorithm testing for extended load sets was conducted within the Eaton facility and at local university campuses. This report is to summarize the major achievements, activities, and outcomes under the execution of the project.