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  1. Solving the MINLP Problem of Unit Commitment on AC Power Systems.

    Abstract not provided.
  2. A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities

    Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem thatmore » requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This study was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.« less
  3. A Stochastic Optimization Approach for Improving Power Systems Resilience through Operations and Planning.

    Abstract not provided.
  4. Pyomo.dae: A Framework Enabling Rapid Development of Optimization Problems Constrained by Differential Equations.

    Abstract not provided.
  5. Recent developments in Pyomo.

    Abstract not provided.
  6. Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring Measurement and Verification.

    Abstract not provided.
  7. Pyomo Tutorial.

    Abstract not provided.
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"Laird, Carl Damon"

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