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Title: An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

Abstract

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.

Authors:
ORCiD logo [1]; ORCiD logo [2]
  1. Department of Chemical and Biological Engineering, University of Wisconsin−Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States; Global Security Sciences Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, United States
  2. Department of Chemical and Biological Engineering, University of Wisconsin−Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1439882
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Industrial and Engineering Chemistry Research; Journal Volume: 57; Journal Issue: 17
Country of Publication:
United States
Language:
English

Citation Formats

Jalving, Jordan, and Zavala, Victor M. An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks. United States: N. p., 2017. Web. doi:10.1021/acs.iecr.7b04124.
Jalving, Jordan, & Zavala, Victor M. An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks. United States. doi:10.1021/acs.iecr.7b04124.
Jalving, Jordan, and Zavala, Victor M. Tue . "An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks". United States. doi:10.1021/acs.iecr.7b04124.
@article{osti_1439882,
title = {An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks},
author = {Jalving, Jordan and Zavala, Victor M.},
abstractNote = {We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.},
doi = {10.1021/acs.iecr.7b04124},
journal = {Industrial and Engineering Chemistry Research},
number = 17,
volume = 57,
place = {United States},
year = {Tue Dec 26 00:00:00 EST 2017},
month = {Tue Dec 26 00:00:00 EST 2017}
}