Two-layered neighborhood tabu search for multi-objective distribution network expansion planning

In this paper, a new method is proposed for distribution network expansion planning. The proposed method is based on the multi-objective meta-heuristics with the epsiv-constraint method. As the power network becomes more deregulated and competitive, distribution network planning is faced with the uncertainty due to several factors. Thus, it is important to consider the uncertainty of the network planning. This paper proposes a new method that makes use of the Monte-Carlo simulation to consider the uncertainty of load patterns in the future. For many scenarios, the multi-objective two-layered tabu search is employed to determine the optimal allocation of feeders and substations. Two-layered tabu search separates integer variables by the difference of the neighborhoods. The proposed method is successfully applied to a sample system