Substation Expansion Planning Based on BFOA

Abstract

In recent years, significant research efforts have been devoted to the optimal planning of power systems. Substation Expansion Planning (SEP) as a sub-system of power system planning consists of finding the most economical solution with the optimal location and size of future substations and/or feeders to meet the future demand. The large number of design variables, and combination of discrete and continuous variables makes the substation expansion planning a very challenging problem. So far, various methods have been presented to solve such a complicated problem. Since the Bacterial Foraging Optimization Algorithm (BFOA) has been proper results in studies of power systems, and has not been applied to SEP problem yet, this paper develops a new BFO-based method to solve the SEP problem. The technique discussed in this paper uses BFOA to simultaneously optimize the sizes and locations of both the existing and new installed substation and feeders by considering reliability constraints. To clarify the capabilities of the presented method a typical network is considered and the results of applying GA and BFOA on the network are compared. The simulation results demonstrate that the BFOA has the potential to find more optimal results than the other algorithm under the same conditions. Also, the fast convergence, consideration of real-world networks limitations as problem constraints and simplicity in applying to large scale networks are the main features of the proposed method.