Engineering topics
Publications and source records attributed to Martin, Shawn.
Conference paper explaining the new user interface and software design for the Pyomo.DoE and ParmEst packages. An example utilizing these packages will be presented, showcasing modeling capabilities, and capabilities for handling complex models that have been developed within the PrOMMiS and CCSI2 ecosystems.
Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.
A computing system obtains test data for a particle source. The test data was generated by the particle source when the particle source was caused to emit particles. The test data comprises a first set of measurements of a first type and a second set of measurements of a second type. The computing system applies a data agnostic predictive model to the test data. The data agnostic predictive model is generated without a parametric analysis of variables of the first type and variables of the second type. The data agnostic predictive model outputs, based upon the test data, a value that is indicative of whether or not the test data is abnormal. Based upon the value, the computing system outputs an indication that the particle source was operating sub-optimally when emitting the particles.
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