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

Journal Article · Mon Sep 01 04:00:00 EDT 2014 · Computing in Science and Engineering

DOI:https://doi.org/10.1109/mcse.2014.53· OSTI ID:1565223

Petra, Cosmin G.; Schenk, Olaf; Anitescu, Mihai

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

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Argonne National Lab. (ANL), Argonne, IL (United States); UT-Battelle LLC/ORNL, Oak Ridge, TN (Unted States)
• Sponsoring Organization: DOE Office of Science; USDOE
• DOE Contract Number: AC02-06CH11357; AC05-00OR22725
• OSTI ID: 1565223
• Journal Information: Computing in Science and Engineering, Journal Name: Computing in Science and Engineering Journal Issue: 5 Vol. 16; ISSN 1521-9615
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

Computer Science