Application of Interior-Point Theory for Reactive Power Optimization in Large Power Systems
|School||Harbin Institute of Technology|
|Keywords||reactive power optimization interior-point theory prime-dual path following method restriction bound|
Reactive power optimization is a nonlinear programming problems, many algorithms are used to calculate this problem. With the scale of power system becoming more and more large, the restrictions are more than before, the problem of reactive power optimization becomes complex and it’s difficult to calculate this problem. In order to improve the convergence and calculating speed, reactive power optimization was deeply researched in this paper. During the process of looking for the optimized point with interior-point theory, it finds that parts of the variables are no longer changed, the alteration of variables tends to become zero. On the other hand, the track of looking for the optimized point goes along with a few restrictions, but far away from the bounds of some restrictions. This paper improves the interior-point theory to calculate reactive power optimization,in which the main method is to reduce the variables that tends to become zero and the restrictions that are far away from the bounds.This paper calculates the sensitivity and sets up the model of reactive power optimization by using sensitivity analysis method. Then it uses the prime-dual path following method to calculate this model. Combining with the flow program, this paper finishes the whole process of reactive power optimization.After the convergence of interior-point circulation, all the variables and constrictions are judged. Before the next big circulation of reactive power optimization, this paper reduces the variables and constrictions which accord with the standard, and the scale of system is decreased. Comparing with the prime-dual path following method, the calculation capacity of the improving algorithm is reduced. The computation results of IEEE118 system show that the convergence and speed of algorithm is increased, and the precision of calculation is assured. It is obvious that improving algorithm don’t reduce the efficiency of optimization.