Applying the modified TLBO algorithm to solve the unit commitment problem

In the day-ahead power systems scheduling, system operators formulate and solve the unit commitment (UC) problem to determine ON/OFF status and power dispatch of the generating units. Although various methods have been presented to solve the UC problem, it is a mixed-integer optimization problem which is hard to find its global optimum solution. In this paper, the new teaching-learning-based optimization (TLBO) technique, which is an evolutionary algorithm, is employed to solve the unit commitment problem. The application of the TLBO on the UC include two phases, teaching and learning phases. A set of population is defined for the UC problem solution. Then, an iterative teaching and learning procedure is implemented to determine the best UC population which has the minimum operating cost while all system and units' constraints, including spinning reserve constraint, are satisfied. Simulation studies show the convergence speed and effeteness of the proposed TLBO algorithm to solve the UC problem. The proposed algorithm is compared with several existing methods.