An Optimization-Based State Estimation Framework for
Large-Scale Natural Gas Networks
Version 2 2018-03-23, 18:38
Version 1 2018-03-22, 13:49
Posted on 2018-03-23 - 18:38
We propose an optimization-based
state estimation framework to
track internal space–time 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.
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Jalving, Jordan; Zavala, Victor M. (2018). An Optimization-Based State Estimation Framework for
Large-Scale Natural Gas Networks. ACS Publications. Collection. https://doi.org/10.1021/acs.iecr.7b04124
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