A short‐term operational planning model for natural gas production systems

Selot, Ajay; Kuok, Loi Kwong; Robinson, Mark; Mason, Thomas L.; Barton, Paul I.

doi:10.1002/aic.11385

Cited by 70 publications

(63 citation statements)

References 45 publications

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“…-3 Natural Gas Production Problems [82,83,123] GloMIQO and SCIP 2.1.0 are the two best-performing solvers in Tables S8 and S9. GloMIQO dominates the process networks performance profile in Figure 6 because it effectively addresses all three problem sets whereas the BARON, LindoGLOBAL, and SCIP each do well on a specific problem sets.…”

Section: Results: Process Network Problems (61 Test Cases)mentioning

confidence: 99%

“…org without modification. -3 Natural Gas Production Problems [82,83,123] Li et al [82,83] reformulated the natural gas production model of Selot et al [123] from a general MINLP to MIQCQP. The 3 test cases in Table 10 are effectively the same problem with a different number of uncertain scenarios.…”

Section: Testing the Global Mixed-integer Quadratic Optimizer (Glomiqo)mentioning

confidence: 99%

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GloMIQO: Global mixed-integer quadratic optimizer

2012

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This paper introduces the Global Mixed-Integer Quadratic Optimizer, GloMIQO, a numerical solver addressing mixed-integer quadratically-constrained quadratic programs (MIQCQP) to ε-global optimality. The algorithmic components are presented for: reformulating user input, detecting special structure including convexity and edge-concavity, generating tight convex relaxations, partitioning the search space, bounding the variables, and finding good feasible solutions. To demonstrate the capacity of GloMIQO, we extensively tested its performance on a test suite of 399 problems of diverse size and structure. The test cases are taken from process networks applications, computational geometry problems, GLOBALLib, MINLPLib, and the Bonmin test set. We compare the performance of GloMIQO with respect to four state-of-the-art global optimization solvers: BARON 10.1.2,

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Section: Results: Process Network Problems (61 Test Cases)mentioning

confidence: 99%

Section: Testing the Global Mixed-integer Quadratic Optimizer (Glomiqo)mentioning

confidence: 99%

GloMIQO: Global mixed-integer quadratic optimizer

2012

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“…Theorem 2 Assume the vectors (x; π + , π − ) satisfy the necessary and sufficient optimality conditions (11) for problem (10) with d defined by (12).…”

Section: A New Formulation Of the Investment And Operations Problemsmentioning

confidence: 99%

“…The operations problem deals with the optimal choice of supply and delivery given the market prices and the network technical resources. A detailed infrastructure model with a complex contractual system which belongs to MIP programming is presented in [12]. More recent studies focus on new formulation to reflect the liberalization process in the natural gas industry in Europe.…”

Section: Introductionmentioning

confidence: 99%

Design and Operations of Gas Transmission Networks

Babonneau

Nesterov

Vial

2012

Operations Research

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Problems dealing with the design and the operations of gas transmission networks are challenging. The difficulty mainly arises from the simultaneous modeling of gas transmission laws and of the investment costs. The combination of the two yields a nonlinear non-convex optimization problem. To obviate this shortcoming, we propose a new formulation as a multi-objective problem, with two objectives. The first one is the investment cost function or a suitable approximation of it; the second is the cost of energy that is required to transmit the gas. This energy cost is approximated by the total energy dissipated into the network. This bi-criterion problem turns out to be convex and easily solvable by convex optimization solvers. Our continuous optimization formulation can be used as an efficient continuous relaxation for problems with non-divisible restrictions such as a limited number of available commercial pipe dimensions.

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“…1 Variants of the pooling problem have applications including 2 : crude oil scheduling, [3][4][5][6] water networks, 7 natural gas production, 8,9 fixed-charge transportation with product blending, 10 hybrid energy systems, 11 and multiperiod blend scheduling. 12 The difficulty of the pooling problem arises from the nonconvex bilinear terms representing linear blending in the pools.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Using functional programming to recognize named structure in an optimization problem: Application to pooling

2016

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in Wiley Online Library (wileyonlinelibrary.com) Branch-and-cut optimization solvers typically apply generic algorithms, e.g., cutting planes or primal heuristics, to expedite performance for many mathematical optimization problems. But solver software receives an input optimization problem as vectors of equations and constraints containing no structural information. This article proposes automatically detecting named special structure using the pattern matching features of functional programming. Specifically, we deduce the industrially-relevant nonconvex nonlinear Pooling Problem within a mixed-integer nonlinear optimization problem and show that we can uncover pooling structure in optimization problems which are not pooling problems. Previous work has shown that preprocessing heuristics can find network structures; we show that we can additionally detect nonlinear pooling patterns. Finding named structures allows us to apply, to generic optimization problems, cutting planes or primal heuristics developed for the named structure. To demonstrate the recognition algorithm, we use the recognized structure to apply primal heuristics to a test set of standard pooling problems.

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A short‐term operational planning model for natural gas production systems

Cited by 70 publications

References 45 publications

GloMIQO: Global mixed-integer quadratic optimizer

GloMIQO: Global mixed-integer quadratic optimizer

Design and Operations of Gas Transmission Networks

Using functional programming to recognize named structure in an optimization problem: Application to pooling

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