Reduced-neighborhood-based Tabu Search for distribution system service restoration

In this paper, an efficient method is proposed for distribution system service restoration. It aims at constructing a network in which out-of-service areas are minimized. The proposed is based on Tabu Search that is one of meta-heuristics for solving a combinational optimization problem efficiently. TS updates the solution by creating the neighborhood and selecting the solution candidates with adaptive memory called tabu list in terms of solution quality and computational time. However, it is often inclined to be time-consuming in dealing with a large-scale problem. In particular, the creation of solution candidates requires heavy computational time as the problem size becomes larger. To allocate the problem, this paper introduces probabilistic neighborhood into TS so that computational efficiency is improved. By probabilistic neighborhood, we mean that a part of solution candidates is selected in a probabilistic way. The proposed method is successfully applied to the modified IEEE 34-node distribution system with three distribution generation units. The proposed method allows system operators to speed up computational efficiency of distribution system service restoration.