A graph-based computational framework for simulation and optimisation of coupled infrastructure networks

Jalving, Jordan; Abhyankar, Shrirang; Kim, Kibaek; Hereld, Mark; Zavala, Victor M.

doi:10.1049/iet-gtd.2016.1582

Title: A graph-based computational framework for simulation and optimisation of coupled infrastructure networks

Journal Article · Mon Apr 24 00:00:00 EDT 2017 · IET Generation, Transmission, & Distribution

DOI:https://doi.org/10.1049/iet-gtd.2016.1582· OSTI ID:1393955

Jalving, Jordan ^[1]; Abhyankar, Shrirang ^[2]; Kim, Kibaek ^[2]; Hereld, Mark ^[2]; Zavala, Victor M. ^[1]

Univ. of Wisconsin-Madison, Madison, WI (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

Here, we present a computational framework that facilitates the construction, instantiation, and analysis of large-scale optimization and simulation applications of coupled energy networks. The framework integrates the optimization modeling package PLASMO and the simulation package DMNetwork (built around PETSc). These tools use a common graphbased abstraction that enables us to achieve compatibility between data structures and to build applications that use network models of different physical fidelity. We also describe how to embed these tools within complex computational workflows using SWIFT, which is a tool that facilitates parallel execution of multiple simulation runs and management of input and output data. We discuss how to use these capabilities to target coupled natural gas and electricity systems.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1393955

Alternate ID(s):: OSTI ID: 1786635

Journal Information:: IET Generation, Transmission, & Distribution, Vol. 11, Issue 12; ISSN 1751-8687

Publisher:: Institution of Engineering and TechnologyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 16 works

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References (16)

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Cited By (1)

A scalable global optimization algorithm for stochastic nonlinear programs Cao, Yankai; Zavala, Victor M. Journal of Global Optimization, Vol. 75, Issue 2 https://doi.org/10.1007/s10898-019-00769-y	journal	April 2019

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