This book addresses the scientific domains of operations research, information science and statistics with a focus on engineering applications. The purpose of this book is to report on the implications of the loop equations formulation of the state estimation procedure of the network systems, for the purpose of the implementation of Decision Support (DS) systems for the operational control of the network systems. In general an operational DS comprises a series of standalone applications from which the mathematical modeling and simulation of the distribution systems and the managing of the uncertainty in the decision-making process are essential in order to obtain efficient control and monitoring of the distribution systems. The mathematical modeling and simulation forms the basis for detailed optimization of the network operations and the second one uses uncertainty based reasoning in order to reduce the complexity of the network system and to increase the credibility of its model. This book reports on the integration of the two aspects of operational DS into a single computational framework of loop network equations. The proposed DS system will be validated using case studies taken from the water industry. The optimal control of water distribution systems is an important problem because the models are non-linear and large-scale and measurements are prone to errors and very often they are incomplete.
Deep Dive into Operational Decision Support in the Presence of Uncertainties.
This book addresses the scientific domains of operations research, information science and statistics with a focus on engineering applications. The purpose of this book is to report on the implications of the loop equations formulation of the state estimation procedure of the network systems, for the purpose of the implementation of Decision Support (DS) systems for the operational control of the network systems. In general an operational DS comprises a series of standalone applications from which the mathematical modeling and simulation of the distribution systems and the managing of the uncertainty in the decision-making process are essential in order to obtain efficient control and monitoring of the distribution systems. The mathematical modeling and simulation forms the basis for detailed optimization of the network operations and the second one uses uncertainty based reasoning in order to reduce the complexity of the network system and to increase the credibility of its model. Thi
OPERATIONAL DECISION
SUPPORT IN THE PRESENCE
OF UNCERTAINTIES
Water Distribution Systems
Corneliu T. C. Arsene
arXiv: 1701.04681v3 [cs.SY] 13 Apr 2018
Operations Research, Information Science
and Statistics
Corneliu T. C. Arsene
Operational Decision Support in
the Presence of Uncertainties
Water Distribution Systems
Corneliu T. C. Arsene, PhD
CorneliuArsene@gmail.com
Copyright © Corneliu T.C. Arsene 2011
All rights reserved. This work may not be translated or copied in whole or in
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v
Preface
This book addresses the scientific domains of operations research, information science
and statistics with a focus on engineering applications. The purpose of this book is to
report on the implications of the loop equations formulation of the state estimation
procedure of the network systems, for the purpose of the implementation of Decision
Support (DS) systems for the operational control of the network systems. In general an
operational DS comprises a series of standalone applications from which the
mathematical modeling and simulation of the distribution systems and the managing of
the uncertainty in the decision-making process are essential in order to obtain efficient
control and monitoring of the distribution systems. The mathematical modeling and
simulation forms the basis for detailed optimization of the network operations and the
second one uses uncertainty based reasoning in order to reduce the complexity of the
network system and to increase the credibility of its model. This book reports on the
integration of the two aspects of operational DS into a single computational framework
of loop network equations.
The proposed DS system will be validated using case studies taken from the
water industry. The optimal control of water distribution systems is an important
problem because the models are non-linear and large-scale and measurements are prone
to errors and very often they are incomplete.
The problem of steady state analysis of water distribution systems is studied in
the context of a co-tree flows simulator algorithm that is derived from the basic loop
corrective flows algorithm. It is shown that the co-tree formulation has several inherent
advantages over the original formulation due to the use of the spanning trees. This
allows a rapid determination of the necessary input data for the simulator (the loop and
the topological incidence matrices and the initial flows) as well as the fast calculus of
the nodal heads at the end of the simulation.
A novel Least Square (LS) state estimator that is suitable for on-line monitoring
of the water distribution systems is presented. The state variables are both the loop
corrective flows and the variation of nodal demands. It is shown that the input data
necessary to build the network equations can be derived from the spanning tree obtained
for the co-tree flows simulator and so there is a natural connection between the novel
state estimator and the simulator algorithm. In spite of the increased size of the state
vector, a satisfactory convergence is obtained through an enhancement in the Jacobian
matrix. Furthermore a fine-tuning of the inverse of the tree incidence matrix is carried
out in order to avoid the lack of numerical stability characteristic to the nodal heads
vi
state estimators. A very efficient and effective loop flows LS state estimator is
developed that is tested successfully on realistic water networks.
Based on the novel state estimation technique, new algorithms for quantifying
the measurement uncertainty impact on the state estimates are developed. The
Confidence Limit Analysis (CLA) algorithms include a formulation of an Experimental
Sensitivity Matrix (ESM) method, a sensitivity matrix method within the loop equations
framework and an Error Maximization technique (EM). The performances of these
algorithms are assessed in terms of their computational complexity and the accuracy of
the results that they produce. It is shown that the computational efficiency and the
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