Experiences and Techniques for Modernization at Sandia.
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The major goals for the project were to remain in contact with accelerator stewardship customers, develop and test new techniques based to further improve accelerator and peripheral systems, and train early career persons to participate on the project and teach them aspects of the field of accelerator science and technology.
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We learned why we don’t need plutonium to make new pits in an earlier edition of Pits 101, but why do we need new pits from a national security standpoint? A plutonium pit, or the core of a nuclear weapon, is like a weapon’s battery. The pits that Los Alamos will make in coming years will be like new batteries for nuclear weapons in the existing stockpile. Pit production is mandated in order to meet Department of Defense (DOD) requirements by our primary customer, the National Nuclear Security Administration (NNSA), which is a semi-autonomous agency within the Department of Energy. NNSA’s mission is to “deliver safe, secure, reliable warheads for an effective nuclear deterrent.” As Marv Adams, head of NNSA Defense Programs, describes, it’s a mission that is simple to state, but challenging to deliver.
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We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.
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