SCHEDULE: NOV 12-18, 2011

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Scalable Stochastic Optimization of Complex Energy Systems

SESSION: Applications

EVENT TYPE: Paper

TIME: 2:00PM - 2:30PM

AUTHOR(S):Miles Lubin, Cosmin G. Petra, Mihai Anitescu, Victor Zavala

ROOM:TCC 305

ABSTRACT:
We present a scalable approach and implementation for solving stochastic programming problems, with application to the optimization of complex energy systems under uncertainty. Stochastic programming is used to make decisions in the present while incorporating a model of uncertainty about future events (scenarios). These problems present serious computational difficulties as the number of scenarios becomes large, necessitating the use of parallel computing. Our novel hybrid parallel implementation PIPS is based on interior-point methods and uses a Schur-complement technique to obtain a scenario-based decomposition of the linear algebra. PIPS is applied to a stochastic economic dispatch problem that uses hourly wind forecasts and a detailed physical power flow model. Solving this problem is necessary for efficient integration of wind power with the Illinois power grid and real-time energy market. Strong scaling efficiency of 96% is obtained on 32 racks (131,072 cores) of the "Intrepid" Blue Gene/P system at Argonne National Laboratory.

Chair/Author Details:

Miles Lubin - Argonne National Laboratory

Cosmin G. Petra - Argonne National Laboratory

Mihai Anitescu - Argonne National Laboratory

Victor Zavala - Argonne National Laboratory

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The full paper can be found in the ACM Digital Library