A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch

This paper presents a new optimization algorithm, named modified teaching-learning algorithm, to solve a more practical formulation of the reserve constrained dynamic economic dispatch of thermal units considering the network losses and operating limitations of the generating units (i.e., the valve loading effect and ramp rate limits). Unlike the previous approaches, three types of the system spinning reserve requirements are explicitly modeled in the problem and a new constraint-handling is proposed to satisfy them. The proposed teaching-learning optimization algorithm is a new population-based optimization method features between the teacher and learners (students). Therefore, this algorithm searches for the global optimal solution through two main phases: 1) the “teacher phase” and 2) the “learner phase”. Nevertheless, these two phases are not adequate for learning interaction between the teacher and the learners in the entire search space. Thus, in this paper a new phase named “modified phase” based on a self-adaptive learning mechanism is added to the algorithm to improve the process of knowledge learning among the learners and accordingly generate promising candidate solutions. The proposed framework is applied to 5-, 10-, 30-, 40-, and 140-unit test systems in order to evaluate its efficiency and feasibility.