A New Approach for Modeling and Global Optimum Solution of Transmission Expansion Planning Including Contingency Conditions

ts cause different neural responses containing a regular firing, or a long latency before firing with or without a leading spike. In this paper, the firing behavior of DCN pyramidal cells is simulated first Transmission Expansion Planning (TEP) is an important issue of power system planning studies. In literature, different methods are investigated to achieve good solutions for TEP. This paper uses Mixed Integer Linear Programming (MILP) and Mixed Integer Nonlinear Programming (MINLP) methods to study TEP. It also presents a new NLP model in which the integer variables are omitted. Moreover, the models are properly modified so that contingency conditions are also observed. Different combinations of cost functions such as the expansion cost, the operation cost and the cost of the losses are considered and compared. To reach a global optimum solution, BARON solver is applied. The proposed algorithm is applied on Garver 6-bus and IEEE-118 bus test systems. It is shown that modeling the problem by MINLP and NLP methods, in combination with a proper solver, can result in a quick optimum solution.