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Preprint ANL/MCS-P1858-0311 Scalable Stochastic Optimization of Complex Energy

Summary: Preprint ANL/MCS-P1858-0311
Scalable Stochastic Optimization of Complex Energy
Miles Lubin, Cosmin G. Petra, Mihai Anitescu, and Victor Zavala
Argonne National Laboratory
9700 S. Cass Avenue
Argonne, IL 60439, USA
{mlubin, petra, anitescu, vzavala}@mcs.anl.gov
We present a scalable approach and implementation for solv-
ing stochastic programming problems, with application to
the optimization of complex energy systems under uncer-
tainty. Stochastic programming is used to make decisions
in the present while incorporating a model of uncertainty
about future events (scenarios). These problems present se-
rious computational difficulties as the number of scenarios
becomes large and the complexity of the system and plan-
ning horizons increase, necessitating the use of parallel com-
puting. Our novel hybrid parallel implementation PIPS is
based on interior-point methods and uses a Schur comple-


Source: Anitescu, Mihai - Mathematics and Computer Science Division, Argonne National Laboratory


Collections: Mathematics