Real-Time Stochastic Optimization of Complex Energy Systems on High-Performance Computers

A scalable approach computes in operationally-compatible time the energy dispatch under uncertainty for electrical power grid systems of realistic size with thousands of scenarios. The authors propose several algorithmic and implementation advances in their parallel solver PIPS-IPM for stochastic op...

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Bibliographic Details
Published in:Computing in science & engineering Vol. 16; no. 5; pp. 32 - 42
Main Authors: Petra, Cosmin G., Schenk, Olaf, Anitescu, Mihai
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer Science
continuation (homotopy) methods
distributed programming
Factorization
Generators
High performance computing
Intervals
Linear systems
Mathematical models
Multicore processing
Optimization
Power grids
Power supply
Real time
Scalability
scientific computing
Solvers
stochastic programming
Stochasticity
Symmetric matrices
ISSN:1521-9615, 1558-366X
Online Access:Get full text
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Summary:A scalable approach computes in operationally-compatible time the energy dispatch under uncertainty for electrical power grid systems of realistic size with thousands of scenarios. The authors propose several algorithmic and implementation advances in their parallel solver PIPS-IPM for stochastic optimization problems. New developments include a novel, incomplete, augmented, multicore, sparse factorization implemented within the PARDISO linear solver and new multicore- and GPU-based dense matrix implementations. They show improvement on the interprocess communication on Cray XK7 and XC30 systems. PIPS-IPM is used to solve 24-hour horizon power grid problems with up to 1.95 billion decision variables and 1.94 billion constraints on Cray XK7 and Cray XC30, with observed parallel efficiencies and solution times within an operationally defined time interval. To the authors' knowledge, "real-time"-compatible performance on a broad range of architectures for this class of problems hasn't been possible prior to this work.
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USDOE
None
DOE Office of Science
AC02-06CH11357; AC05-00OR22725
ISSN:1521-9615
1558-366X
DOI:10.1109/MCSE.2014.53