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Title: A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Journal Article · · Mathematical Programming Computation
 [1];  [2];  [3];  [4];  [5];  [6]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Rice Univ., Houston, TX (United States)
  3. Georgia Institute of Technology, Atlanta, GA (United States)
  4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  5. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  6. Univ. of California, Davis, CA (United States)
+ Show Author Affiliations

Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. Finally, the mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC36-08GO28308; AC52-07NA27344; NA0003525
OSTI ID:
1998620
Report Number(s):
NREL/JA-2C00-84450; MainId:85223; UUID:9e8eac8a-4f40-4eda-bda6-39efe9a4091f; MainAdminID:68994
Journal Information:
Mathematical Programming Computation, Vol. 15, Issue 4; ISSN 1867-2949
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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decomposition strategies
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