Stochastic second-order cone programming: Applications models

Alzalg, Baha

doi:10.1016/j.apm.2011.12.053

Cited by 24 publications

(15 citation statements)

References 13 publications

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“…A diverse set of real-world applications can be modeled as stochastic second-order cone programming problems (see [3,4]). The use of convex quadratic objective together with second-order cone constraints provides an important class of optimization problems, and apparently promises to be applicable to a more diverse variety of real-world applications.…”

Section: Discussionmentioning

confidence: 99%

Decomposition-based interior point methods for stochastic quadratic second-order cone programming

Alzalg

2014

Applied Mathematics and Computation

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Section: Discussionmentioning

confidence: 99%

Decomposition-based interior point methods for stochastic quadratic second-order cone programming

Alzalg

2014

Applied Mathematics and Computation

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“…Stochastic second-order cone programming with recourse is a class of optimization problems defined to handle uncertainty in data defining deterministic second-order cone programming [2]. The standard form of stochastic second-order cone programming is the following:…”

Section: Stochastic Second-order Cone Programming For the Problemmentioning

confidence: 99%

“…Interior point methods are considered to be one of the most successful classes of algorithms for solving stochastic convex optimization problems [2]. Baha used a unified practical primal interior decomposition algorithm to solve all SSOCP models mentioned in [2]. Thus the SSOCP models in this paper are optimized using the second-order cone programming package MOSEK [16].…”

Section: Stochastic Second-order Cone Programming For the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

TANG¹,

Jiekang²,

Wu³

et al. 2018

Turk J Elec Eng & Comp Sci

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This paper introduces a stochastic second-order cone programming (SSCOP) approach to solve the distributed generation coordination problem considering the uncertainty of electric vehicle charging in distribution networks. To minimize the total power loss in the distribution system, the problem is formulated to coordinate the output power of distributed generations (DGs). Two stages are presented to solve the optimization problem: the first stage is to optimize the output power of distributed generators without electric vehicle charging in the distribution system, and the second stage is to optimize the output power of distributed generators according to the stochastic increased load due to the uncertainty of electric vehicle charging. The proposed approach is tested on 69-node and 118-node large-scale distribution systems. The simulated results demonstrate the feasibility and effectiveness of SSCOP.

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“…Zhu et al 15 introduced stochastic semi-definite programs and chance-constrained semi-definite programs as paradigms to deal with uncertainty in applications leading to semi-definite programs. Alzalg 16 described four application models leading to stochastic secondorder cone programming. Rough set theory can deal with the problems with inexact data or imprecise information for complicated systems effectively.…”

Section: Related Workmentioning

confidence: 99%

A Synthesizing Effect-Based Solution Method for Stochastic Rough Multi-objective Programming Problems

Zhou¹,

Zhang²,

Li³

2014

IJCIS

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Multi-objective programming with uncertain information has been widely applied in modeling of industrial produce and logistic distribution problems. Usually the expectation value model and chance-constrained model as solution models are used to deal with such uncertain programming. In this paper, we consider the uncertain programming problem which contains random information and rough information and is hard to be solved. A new solution model, called stochastic rough multi-objective synthesis effect (MOSE) model, is developed to deal with a class of multiobjective programming problems with random rough coefficients. The MOSE model contains expectation value model and chance-constrained model by choosing different synthesis effect functions and can be considered as an extension of crisp multi-objective programming model. Combined with genetic algorithm, the optimal solution of the MOSE model can be obtained. It shows that the solutions of the MOSE model are better than that of other solution models. Finally, an illustrative example is provided to show the effectiveness of the proposed method.

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Stochastic second-order cone programming: Applications models

Cited by 24 publications

References 13 publications

Decomposition-based interior point methods for stochastic quadratic second-order cone programming

Decomposition-based interior point methods for stochastic quadratic second-order cone programming

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

A Synthesizing Effect-Based Solution Method for Stochastic Rough Multi-objective Programming Problems

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