Parallel computing for power system climate resiliency: Solving a large-scale stochastic capacity expansion problem with mpi-sppy

We propose a nodal stochastic generation and transmission expansion planning model that incorporates the output from high-resolution global climate models through load and generation availability scenarios. We implement our model in Pyomo and perform computational studies on a realistically-sized te...

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Bibliographic Details
Published in:Electric power systems research Vol. 235; no. N/A; p. 110720
Main Authors: Valencia Zuluaga, Tomas, Musselman, Amelia, Watson, Jean-Paul, Oren, Shmuel S.
Format: Journal Article
Language:English
Published: United States Elsevier B.V 01.10.2024
Elsevier
Subjects:
Capacity expansion
Climate resiliency
Parallel computing
POWER TRANSMISSION AND DISTRIBUTION
Stochastic programming
Capacity expansion
Climate resiliency
Parallel computing
Stochastic programming
ISSN:0378-7796, 1873-2046
Online Access:Get full text
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Summary:We propose a nodal stochastic generation and transmission expansion planning model that incorporates the output from high-resolution global climate models through load and generation availability scenarios. We implement our model in Pyomo and perform computational studies on a realistically-sized test case of the California electric grid in a high performance computing environment. We propose model reformulations and algorithm tuning to efficiently solve this large problem using a variant of the Progressive Hedging Algorithm. We utilize the parallelization capabilities and overall versatility of mpi-sppy, exploiting its hub-and-spoke architecture to concurrently obtain inner and outer bounds on an optimal expansion plan. Initial results show that instances with 360 representative days on a system with over 8,000 buses can be solved to within 5% of optimality in under 4 h of wall clock time, a first step towards solving a large-scale power system expansion planning problem across a wide range of climate-informed operational scenarios. •Including climate projections into power system expansion plans helps resiliency.•Joint transmission, storage and generation expansion increases size of problem.•Stochastic optimization addresses uncertainty, but is computationally challenging.•Decomposition via Progressive Hedging Algorithm makes this problem tractable.•Parallel computing implementation allows quick solution times.
Bibliography:AC52-07NA27344
LLNL--JRNL-868315
USDOE National Nuclear Security Administration (NNSA)
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2024.110720