Parallel Computing for Two-Stage Stochastic Infrastructure Planning
Abstract
Infrastructure planning has become increasingly difficult in recent years as natural hazards affect supply and demand patterns as well as the network of equipment connecting the two. In order to plan coordinated infrastructure systems that are resilient to a variety of potential threats, it is necessary to represent these systems at sufficiently high resolution to capture geographic and temporal variations as well as uncertainty. Both of these factors translate into much larger optimization problems than have traditionally been considered, and solving these problems is at the frontier of what is computationally feasible. Parallel computing is a tool to push that frontier. In this tutorial, we present methods for solving large-scale two-stage stochastic mixed-integer linear problems (MILPs) using high-performance computing (HPC) resources, with a focus on infrastructure planning problems. To this end, we cover the necessary basics of modeling stochastic infrastructure planning problems and leveraging parallel computing resources to solve stochastic MILPs. We discuss decomposition algorithms and their parallel implementation in the Python package mpi-sppy and present examples of how to use this tool to solve stochastic infrastructure planning problems. Finally, we present an example of how mpi-sppy has been used to solve a realistically sized power system expansion planning problem for California to demonstrate the difficulty of solving large-scale, stochastic infrastructure planning problems and how HPC resources can be leveraged to solve such problems.
Funding: This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory (LLNL) [Contract DE-AC52-07NA27344] and was supported by the LLNL-Lab Directed Research and Development Program [Project No. 25-SI-007].
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