Abstract.Transmission expansion planning (TEP) is one of the issue that have to be faced caused by addition of large scale power generation into the existing power system. Optimization need to be conducted to get optimal solution technically and economically. Several mathematic methods have been applied to provide optimal allocation of new transmission line such us genetic algorithm, particle swarm optimization and tabu search. This paper proposed novel binary particle swarm optimization (NBPSO) to determine which transmission line should be added to the existing power system. There are two scenerios in this simulation. First, considering transmission power losses and the second is regardless transmission power losses. NBPSO method successfully obtain optimal solution in short computation time. Compare to the first scenario, the number of new line in second scenario which regardless power losses is less but produces high power losses that cause the cost becoming extremely expensive. IntroductionThe additional of high scale power generating capacity increases over the year to fulfill the electricity demand. Most of generating plants are located far from the load center. Transmission network is an important infrastructure that must be considered in generation expansion planning. To avoid line overloading, net transmission capacity (NTC) has to meet reliability criteria while delivering electric power to the load. The construction of transmission line which needs a high budged becoming an issue in transmission expansion [1]. Transmission expansion planning (TEP) aims to minimize the line construction which directly relates to investment cost. It determines which line must be added to existing power system by considering technical, economic and reliability aspect.There are a lot of mathematical methods that have been used to do TEP optimization. Genetic algorithm has been used in several researches in binary and decimal codification model [2]. Discrete PSO with decimal codification also has been applied in STEP optimization. The precision and convergence speed of discrete PSO are better and faster than genetic algorithm. In addition to discrete PSO, traditional PSO with real number for continuous model also have been used to find the optimum transmission line considering line loading [4]. The other methods such as discrete evolutionary particle swarm optimization, simulated annealing algorithm, tabu search, and hybrid algorithm successfully provide optimum solution [5]-[8]. Ordinal optimization and mixed integer linear programming also have been applied to solve TEP problem [9]- [11].Most of previous researches conducted TEP study to obtain optimal cost of transmission line investment regardless power losses in power system. In this research, the optimization of TEP considers investment cost, power losses and line loading transmission. Novel binary PSO algorithm is applied to solve TEP problem in this research.