In this paper a novel flexible planning strategy based on the teaching-learning-based optimization (TLBO) algorithm and pattern search algorithm (PS) is proposed to improve the security optimal power flow (SOPF) by minimizing the total fuel cost, total power loss and total voltage deviation considering critical load growth. The main particularity of the proposed hybrid method is that TLBO algorithm is adapted and coordinated dynamically with a local search algorithm (PS). In order validate the efficiency of the proposed strategy, it has been demonstrated on the Algerian 59-bus power system and the IEEE 118-bus for different objectives considering the integration of multi SVC devices. Considering the interactivity of the proposed combined method and the quality of the obtained results compared to the standard TLBO and to recent methods reported in the literature, the proposed method proves its ability for solving practical planning problems related to large power systems.

This paper presents an improved artificial bee colony (IABC) optimization method to solving practical economic dispatch taking into account the nonlinear generator characteristics such as valve-point loading effects. In order to exploit the performance of this new variant based ABC method to solving practical economic dispatch, a new local search mechanism (LSM) associated to the original ABC algorithm; it allows exploiting effectively the promising region to locate the best solution. The proposed approach has been examined and applied to many practical electrical power systems, the 13 generating units, and to the large electrical system with 40 generating units considering valve point loading effects. From the different case studies, it is observed that the results compared with the other recent techniques demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical and large ED.