With load growth and new-generation sources installations in power systems, supplying demand in long-term needs electric power transmission networks reinforcement or expansion planning. Since a power system is ex- posed to many random events such as unscheduled outage of equipment, expansion planning should be done to satisfy the reliability requirements. One of the most important aspects of reliability that needs to be considered in expansion planning is “security”, i.e., the ability of a power system to withstand any contingency. To ensure the security of the power system, it is necessary to consider the active and reactive power supply requirements, simultaneously. In this thesis, static security constrained transmission expansion planning (SC-TEP) problem with the aim of minimizing the investment and operation costs in the target year is studied, while N-1 security constraints in transmission network are taken into account. In these studies, capacity and location of power plants, as well as the forecasted load (as a load duration curve) are assumed to be known in the target year. In the first step, the SC-TEP problem is modeled based on DC power flow equations as a mixed integer linear programming (MILP) problem. Then, based on AC power flow equations, SC-TEP problem is modeled in order to consider the active and reactive power supply requirements simultaneously in maintaining the security of the power system. The reactive power planning (RPP) model is also inserted into SC-TEP problem. In order to achieve the global optimum solution, AC power flow equations convexification is employed, by which, the proposed coordinated transmission system and reactive power sources expansion planning problem is expressed as a mixed integer second order cone programming (MISOCP) problem. Considering contingencies on all existing and candidate transmission equipment at all load levels increases Keywords Transmission expansion planning, Reactive power planning, N-1 security criterion, Mixed integer second order cone programming, Bi-level optimization
