Abstract
Introduction. The hydrothermal scheduling problem is one of the important problems in the economic operation of power systems, which aims to determine the optimal production schedule of hydro and thermal power plants with the lowest fuel cost and observing all operating constraints. The existence of nonlinear relationships, time dependence between variables, the structure of cascaded reservoir system, hydraulic constraints and large search space make solving this problem an important challenge in the field of power system optimization. Problem. In this problem, the operation schedule of hydro and thermal power plants must be determined in such a way that in addition to meeting power demand, the constraints related to power balance, production limits, outlet flow limits, reservoir volume, water delay and hydraulic continuity are also fully observed. These features cause the problem to be placed in the group of nonlinear constrained optimization problems. Goal. To provide an efficient method to solve the hydrothermal scheduling problem and achieve an optimal operating plan with minimum production cost. Methodology. In this research, an improved version of the teaching-learning-based optimization (ITLBO) algorithm is introduced to solve this problem. In the proposed method, a small and controlled random perturbation is added to both the teacher and learner phases to maintain population diversity, reduce the probability of early convergence, and increase the search power of the algorithm. The performance of the proposed method is evaluated on a standard system consisting of 4 hydro power plants and 1 equivalent thermal power plant. Results. The results show that ITLBO is able to produce a valid operating plan for the hydrothermal scheduling problem by fully respecting all model constraints and increasing the value of the objective function to 1383011. Also, the performance of this method has been compared with 7 algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), multi-verse optimizer (MVO), whale optimization algorithm (WOA), Grey Wolf Optimizer (GWO) and tunicate swarm algorithm (TSA). The comparison results show that ITLBO, by obtaining the best values of mean, median and standard, along with the first rank, provides the best solution quality and the highest stability among all the algorithms studied. Scientific novelty. The providing an ITLBO and its effective application to solve the hydrothermal scheduling problem along with a comprehensive evaluation and comparison with 7 competing algorithms. Practical value. The findings show that ITLBO is an efficient, stable and reliable method for solving the hydrothermal scheduling problem and can reduce the operating cost of power systems and increase the economic efficiency of power networks. References 34, tables 7, figures 2.