A differential evolution algorithm for multistage transmission expansion planning

In previous research by the authors of this paper [6] a novel Differential Evolution Algorithm (DEA) was applied directly to the DC power flow based model in order to solve the static Transmission Expansion Planning (TEP) problem. The DEA performed well with regard to both low and medium complexity transmission systems as demonstrated on the Garver six-bus and IEEE 25-bus test systems, respectively. As a consequence of the successful results obtained with regard to the static TEP problem, the DEA is selected again to solve the multistage TEP problem with DC model, which is classed as a mixed integer nonlinear optimisation problem. The problem is more complex and difficult to solve than the static TEP problem because it considers riot only the optimal number of lines and location that should be added to an existing network but also the most appropriate times to carry out the investment. In this paper, the effectiveness of the proposed enhancement is initially demonstrated via the analysis of the medium complexity transmission test systems as described in figures 2 and 3. The analysis is performed within the mathematical programming environment of MATLAB using both a DEA and a Conventional Genetic Algorithm (CGA) and a detailed comparison of accuracy and performance is presented.