Formulation and solution of distribution system voltage and VAR control with distributed generation as a mixed integer non-linear programming problem

2014 ◽  
Vol 108 ◽  
pp. 164-169 ◽  
Author(s):  
Esa A. Paaso ◽  
Yuan Liao ◽  
Aaron M. Cramer
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Distributed Generation ◽  
Distribution System ◽  
Linear Programming Problem ◽  
Mixed Integer ◽  
Non Linear Programming ◽  
Non Linear

scholarly journals Generalized Transformation Techniques for Multi-Choice Linear Programming Problems

2012 ◽  
Vol 3 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Srikumar ACHARYA ◽  
Mitali Madhumita ACHARYA
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Decision Maker ◽  
Linear Programming Problem ◽  
Programming Model ◽  
Mixed Integer ◽  
Transformation Techniques ◽  
Programming Technique ◽  
Binary Variables ◽  
Non Linear

The multi-choice programming allows the decision maker to consider multiple number of resources for each constraint or goal. Multi-choice linear programming problem can not be solved directly using the traditional linear programming technique. However, to deal with the multi-choice parameters, multiplicative terms of binary variables may be used in the transformed mathematical model. Recently, Biswal and Acharya (2009) have proposed a methodology to transform the multi-choice linear programming problem to an equivalent mathematical programming model, which can accommodate a maximum of eight goals in righthand side of any constraint. In this paper we present two models as generalized transformation of the multi-choice linear programming problem. Using any one of the transformation techniques a decision maker can handle a parameter with nite number of choices. Binary variables are introduced to formulate a non-linear mixed integer programming model. Using a non-linear programming software optimal solution of the proposed model can be obtained. Finally, a numerical example is presented to illustrate the transformation technique and the solution procedure.

scholarly journals A comparative study of the methods of solving non-linear programming problem

1970 ◽  
Vol 4 (1) ◽  
pp. 28-34
Author(s):  
Bimal Chandra Das
Keyword(s):  
Linear Programming ◽  
Quadratic Programming ◽  
Comparative Study ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Feasible Region ◽  
Non Linear Programming ◽  
Condition Method ◽  
Non Linear ◽  
Matlab Programming

The work present in this paper is based on a comparative study of the methods of solving Non-linear programming (NLP) problem. We know that Kuhn-Tucker condition method is an efficient method of solving Non-linear programming problem. By using Kuhn-Tucker conditions the quadratic programming (QP) problem reduced to form of Linear programming(LP) problem, so practically simplex type algorithm can be used to solve the quadratic programming problem (Wolfe's Algorithm).We have arranged the materials of this paper in following way. Fist we discuss about non-linear programming problems. In second step we discuss Kuhn- Tucker condition method of solving NLP problems. Finally we compare the solution obtained by Kuhn- Tucker condition method with other methods. For problem so consider we use MATLAB programming to graph the constraints for obtaining feasible region. Also we plot the objective functions for determining optimum points and compare the solution thus obtained with exact solutions. Keywords: Non-linear programming, objective function ,convex-region, pivotal element, optimal solution. DOI: 10.3329/diujst.v4i1.4352 Daffodil International University Journal of Science and Technology Vol.4(1) 2009 pp.28-34

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