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

Journal Article · · Electric Power Systems Research
 [1];  [2];  [2];  [1]
  1. Univ. of California, Berkeley, CA (United States)
  2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
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Here 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.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
2438189
Report Number(s):
LLNL--JRNL-868315; 1104252
Journal Information:
Electric Power Systems Research, Journal Name: Electric Power Systems Research Journal Issue: N/A Vol. 235; ISSN 0378-7796
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (10)

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A scalable solution framework for stochastic transmission and generation planning problems journal March 2015
Pyomo: modeling and solving mathematical programs in Python journal August 2011
A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty journal August 2023
Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards journal December 2016
Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources journal April 2016
Mission net-zero America: The nation-building path to a prosperous, net-zero emissions economy journal November 2021
An Engineering-Economic Approach to Transmission Planning Under Market and Regulatory Uncertainties: WECC Case Study journal January 2014
Projective Hedging Algorithms for Multistage Stochastic Programming, Supporting Distributed and Asynchronous Implementation journal July 2023

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Capacity expansion
Climate resiliency
Parallel computing
Stochastic programming