A new hybrid GA-fuzzy optimization algorithm for security-constrained based generation and transmission expansion planning in the deregulated environment

Abstract

This study presents a novel method to solve the multi-period generation and transmission expansion planning (GTEP) problem in a deregulated environment. This framework optimizes simultaneously multiple goals including economic and market indices. The investment cost as an economic criterion and the congestion cost and global welfare as the market-based criteria are taken into account in the proposed planning problem. The market reliability is also assessed considering the N-1 security criterion. An efficient combination of genetic algorithm and fuzzy technique is used to cope with non-linear nature of the proposed multi-objective optimization problem. This solving technique enables planner to adopt a perfect solution according to different levels of importance for planning objectives. The proposed GTEP methodology is implemented on IEEE 6-bus test system and IEEE 24-bus reliability test system considering a 6-year planning horizon. Different expansion planning problems are tested in order to show the impact of the proposed model on the future conditions of the case studies. To evaluate the effectiveness of the proposed optimization method, comparative studies are also provided. The obtained results justify the superiority of the proposed method in finding better expansion plan comparing to some previously reported methods.