TRANSMISSION EXPANSION PLANNING OPTIMIZIN USING DC AND DISJUNCTIVE MODELS

Abstract

Transmission systems play an unquestionably important role in avoiding load-shedding, i.e. loss of load, by providing energy to various kinds of users during crucial situations. Transmission expansion planning (TEP) involves constructing or adding a new transmission line to the network, preferably at the least cost. However, a TEP allows a network to convey sufficient generated power to load centers at particular times to meet growing demand for electrical power. The expansion of the transmission network is one of the strategic initiatives in which critical decisions are organized and developed at the national level, demanding the allocation of significant financial resources. Consequently, the TEP problem inherently poses a mixed-integer nonlinear programming (MINLP) optimization challenge, characterized by significant computational complexity, especially in the context of large-scale power systems. Despite the application of various simplifications and computational techniques, achieving an optimal solution for TEP within an acceptable simulation time frame remains a formidable challenge. In this thesis, based on a detailed literature review, a mixed integer linear programing (MINLP) approach in DC model is firstly formulated for a static TEP problem respectively for a Graver 6-bus network and for an IEEE 24-bus reliability test system (RTS), and applying a disjunctive model to the proposed test systems. These models are simulated on the Graver 6-bus network and IEEE 24-bus reliability test system to compare optimal plans between the models. Further assumptions and adjustment are searched and tested to achieve more accurate optimal plans.