Pemodelan Vektor Metode Interior Point Untuk Studi Pembebanan Maksimum Sistem Tenaga Listrik

Beberapa tahun terakhir, pada sistem tenaga listrik beban yang ada semakin besar seiring peningkatan beban listrik. Permasalahan utama kondisi pembebanan akan menyebabkan ketidakstabilan tegangan di sistem tenaga listrik. Untuk mencegah ketidakstabilan sistem, penting bagi operator sistem tenaga untuk mengidentifikasi seberapa jauh sistem memuat dari kondisi kritisnya. Penelitian ini membahas masalah maksimum pembebanan dengan menggunakan metode non-linear primal-dual interior point method. Caranya dengan memaksimalkan beban sistem yang diwakili oleh pengganda skalar ke beban sistem. Kontribusi utama dari pekerjaan ini adalah dalam pengembangan model vectorized dari masalah dan pengembangan program aplikasi dalam bahasa pemrograman Pyhton. Model yang dikembangkan kemudian diuji untuk memecahkan masalah loadability maksimum untuk sistem pengujian IEEE 14-bus dan 30-bus. Simulasi dari model ini dan program komputer yang dikembangkan memberikan hasil yang memuaskan.Kata Kunci : Python, Interior Point Method. Loadability Maksimum, dan Model Vectorized

An interior-point trust-region algorithm to solve a nonlinear bilevel programming problem

<abstract><p>In this paper, a nonlinear bilevel programming (NBLP) problem is transformed into an equivalent smooth single objective nonlinear programming (SONP) problem utilized slack variable with a Karush-Kuhn-Tucker (KKT) condition. To solve the equivalent smooth SONP problem effectively, an interior-point Newton's method with Das scaling matrix is used. This method is locally method and to guarantee convergence from any starting point, a trust-region strategy is used. The proposed algorithm is proved to be stable and capable of generating approximal optimal solution to the nonlinear bilevel programming problem.</p> <p>A global convergence theory of the proposed algorithm is introduced and applications to mathematical programs with equilibrium constraints are given to clarify the effectiveness of the proposed approach.</p></abstract>

Path-following interior-point algorithm for monotone linear complementarity problems

This paper presents a path-following full-Newton step interior-point algorithm for solving monotone linear complementarity problems. Under new choices of the defaults of the updating barrier parameter [Formula: see text] and the threshold [Formula: see text] which defines the size of the neighborhood of the central-path, we show that the short-step algorithm has the best-known polynomial complexity, namely, [Formula: see text]. Finally, some numerical results are reported to show the efficiency of our algorithm.

A weighted logarithmic barrier interior-point method for linearly constrained optimization

In this paper, a weighted logarithmic barrier interior-point method for solving the linearly convex constrained optimization problems is presented. Unlike the classical central-path, the barrier parameter associated with the per- turbed barrier problems, is not a scalar but is a weighted positive vector. This modi cation gives a theoretical exibility on its convergence and its numerical performance. In addition, this method is of a Newton descent direction and the computation of the step-size along this direction is based on a new e cient tech- nique called the tangent method. The practical e ciency of our approach is shown by giving some numerical results.