A Multiobjective Teaching-Learning Algorithm for Power Losses Reduction in Power Systems

Abstract The reactive power dispatch is one of the most complex problems of power systems and may include the simultaneous optimization of several objective functions, possibly in conflict with each other. Hence, the optimal reactive power dispatch problem becomes a multiobjective optimization problem. Such an optimization problem has a set of possible solutions which represent the best commitment among the objective functions. Novel methods based on meta-heuristics have become a popular choice for solving complex real-world, multiobjective, optimization problems due to their flexibility, generality, and ease of use. The advantages of evolutionary algorithms in terms of the modeling capability and excellent global search characteristics have encouraged their application to the reactive power dispatch problem in power systems. The teaching learning-based optimization (TLBO) is a population-based optimization algorithm suitable for solving complex problems. TLBO imitates the interaction between a teacher and their students. The global solution search process of this approach consists of two phases: the teacher-phase and the learner-phase. This chapter proposes a multiobjective teaching learning algorithm based on decomposition (MOTLA/D) for solving a reactive power handling problem. In order to assess the effectiveness of the proposed approaches to solve the multiobjective reactive power dispatch problem, the algorithms are tested in three power systems of different complexity: IEEE 14 bus, 30 bus, and 118 bus system. Several studies have been carried out among the algorithms involving fuel cost minimization, power losses reduction, and voltage stability enhancement as objective functions. Furthermore, the proposed method is applied to a simplified Mexican power grid.