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  1. Generating Short-Term Probabilistic Wind Power Scenarios via Non-Parametric Forecast Error Density Estimators.

    Abstract not provided.
  2. Accelerating and automatic tuning for Progressive Hedging.

    Abstract not provided.
  3. Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production.

    Abstract not provided.
  4. Pyomo - Optimization Modeling in Python 2nd Ed.

    Abstract not provided.
  5. Constructing probabilistic scenarios for wide-area solar power generation

    Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions.more » Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.« less
  6. Pyomo v5.0

    Pyomo supports the formulation and analysis of mathematical models for complex optimization applications. This capability is commonly associated with algebraic modeling languages (AMLs), which support the description and analysis of mathematical models with a high-level language. Although most AMLs are implemented in custom modeling languages, Pyomo's modeling objects are embedded within Python, a full- featured high-level programming language that contains a rich set of supporting libraries.
  7. New developments in Pyomo.

    Abstract not provided.
  8. Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

    Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less
  9. Monitoring and Accelerating Progressive Hedging with Cross-scenario Information.

    Abstract not provided.
  10. Co-occurring woody species have diverse hydraulic strategies and mortality rates during an extreme drought: Belowground hydraulic failure during drought

    From 2011 to 2013, Texas experienced its worst drought in recorded history. This event provided a unique natural experiment to assess species-specific responses to extreme drought and mortality of four co-occurring woody species: Quercus fusiformis, Diospyros texana, Prosopis glandulosa and Juniperus ashei. We examined hypothesized mechanisms that could promote these species’ diverse mortality patterns using post-drought measurements on surviving trees coupled to retrospective process modeling. The species exhibited a wide range of gas exchange responses, hydraulic strategies, and mortality rates. Multiple proposed indices of mortality mechanisms were not consistent with the observed mortality patterns across species, including measures of iso/anisohydry,more » photosynthesis, carbohydrate depletion, and hydraulic safety margins. Large losses of growing season whole-tree conductance (driven by belowground losses of conductance), and shallower rooting depths, were associated with species that exhibited greater mortality. Based on this retrospective analysis, we suggest that species more vulnerable to drought were more likely to have succumbed to hydraulic failure belowground.« less

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"Woodruff, David"

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