Research On Optimal Power Flow Algorithm For Large Scale Power System

Optimal Power Flow (OPF) has wide application in various aspect of Power System, such as economic distribution, network plan, secure operation, reliability analysis. With the increasing scale and interconnection of modern power networks, the existing level of processor cannot satisfy the requirement for calculation taken by OPF. So, power system desires for the algorithms that could solve OPF problems in regulated precision and time consuming. Therefore, research on large scale OPF problems has profound meaning and broad research prospective.The mathematical model of OPF problem is a complex nonlinear programming problem, which intends to regulate the variables by means of control ways to implement the optimization of the stability of the power system. Presently, there are many methods available to solve the OPF problems, such as gradient method, linear programming, mixing programming, interior point method, and artificial intelligence.This paper introduced interior point method (IPM) that applied widely in current OPF calculation, and proposed interior-exterior point method (IEPM) that could be applied to large scale OPF. IEPM overcomes the shortcoming of the IPM that the convergence speed will be slowed down by the increasing ill-conditioning of Hessian matrix and shrinking steplength. Hense the IEPM possesses the better local convergence ability. At last, this paper proposed another algorithm for large scale OPF based on entropy theory. This method transforms the equality constraints and inequality constraints into one entropy function, which could dramatically decrease the dimensions of the Hessian matrix and Jacobian matrix, thus could improve the arithmetic speed. The results of the simulation indicated that the methods proposed by this paper are all applicable for large scale OPF problems.