A Comparative Study of Metaheuristic Methods for Transmission Network Expansion Planning

Transmission network expansion planning is a very complex and computationally demanding problem due to the discrete nature of the optimization variables. This complexity has increased even more in a restructured deregulated environment. In this regard, there is a need for development of more rigorous optimization techniques. This paper presents a comparative analysis of three metaheuristic algorithms known as Bacteria foraging (BF), Genetic algorithm (GA), and Particle swarm optimization (PSO) for transmission network expansion planning with and without security constraints. The DC power flow based model is used for analysis and results for IEEE 24 bus system are obtained with the above three metaheuristic drawing a comparison of their performance characteristic. DOI: 10.4018/jaec.2010100104 72 International Journal of Applied Evolutionary Computation, 1(4), 71-91, October-December 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Static TNEP is done in a single planning horizon, whereas dynamic TNEP requires stage wise addition of lines over the planning horizon with varying stage wise loads. Basically, TNEP is dynamic in nature; however, most of the research work in the literature is for a simple static problem to keep the mathematical formulation simple. This paper also considers the static TNEP model only. However, the algorithms can be extended for a multistage transmission network expansion planning. DC load flow model is used for simplicity.