Probabilistic interactive fuzzy satisfying generation and transmission expansion planning using fuzzy adaptive chaotic binary PSO algorithm

Abstract

In this paper, an interactive fuzzy satisfying method based on fuzzy adaptive chaotic binary particle swarm optimization (FACBPSO) algorithm is proposed to investigate the multi-objective Generation and Transmission Expansion Planning (G-TEP). The objective functions of the G-TEP problem, which are modeled by fuzzy sets, present the total invest- ment/operation cost and the total pollutant emission. In modern power systems, the necessity of considering the wind/solar energy resources in Generation Expansion Planning (GEP) studies is important. In addition, HVDC links, transmitting renewable resource power from remote sites, should be considered in TEP problem. The use of the wind/solar energy due to the uncertainty of their generation and the integration of HVDC links would consequently impose more complexity to solution of the G-TEP problem. In this paper, the proposed algorithm is tested on an IEEE test system based on economic and environmental considerations to generate an optimal expansion plan.