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“Stochastic Unit Commitment: Scenario Generation, Scalable Computation, & Experimental Results”

Jean-Paul Watson
Sandia National Laboratories
February 10, 2017   2:30-3:30pm
JDT 410

 

 

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Abstract
The objective in stochastic unit commitment is to optimize day-ahead and intra-day electricity generation schedules taking into account the uncertainty associated with both load and renewables production. The resulting large scale stochastic mixed-integer programming problems present serious computational challenges. We address these challenges using scenario-based decomposition techniques, in particular variants of progressive hedging, and modest parallel computing resources, achieving tractable run-times on moderate-scale instances. Our solver is embedded in a stochastic simulation environment, which is used to validate the model and to quantify cost savings relative to a standard deterministic unit commitment model. We describe experimental results on an ISO-NE test case, in addition to a smaller WECC-240 case. We also describe challenges and novel solutions to probabilistic scenario generation, required to represent the uncertainty associated with load and renewables production. to address more routine and commonly-occurring conditions.
Bio
Dr. Jean-Paul Watson is a Distinguished Member of Technical Staff in the Discrete Math and Optimization Department at Sandia National Laboratories, in Albuquerque, New Mexico. He has over 12 years of experience applying and analyzing algorithms for solving difficult combinatorial optimization and informatics problems, in fields ranging from logistics and infrastructure security to power systems and computational chemistry. His research currently focuses on methods for approximating the solution of large -scale deterministic and stochastic mixed-integer and non-linear programs, with applications in the domain of electricity grid operations, planning, and resiliency. Previously, he developed solutions for real-world stochastic optimization problems in logistics (Lockheed Martin and the US Army) and sensor placement (US Environmental Protection Agency).

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